A dyeing formula intelligent conversion control method and system for hosiery production

Abstract

This invention relates to the field of program control technology, specifically a method and system for intelligent dyeing recipe conversion control for hosiery production. This system collects product data and preset data to build a conversion control model, automatically dividing processing areas and calculating treatment agent dosage. Initial dosages are calculated based on area, material water absorption, and target concentration, and dynamically compensated using a material type correction factor and the density-permeability relationship. Regional processing is performed using the treatment hue and compensated dosage. An automatic control model is built to compare the processed data with preset values ​​in real time. If deviations exceed limits, the processing time, temperature, and pH value are automatically adjusted until they meet the target, achieving precise closed-loop control of the dyeing process.

Description

Technical Field

[0001] The present invention relates to the field of program control technology, and in particular to a dyeing formula intelligent conversion control method and system for hosiery production. Background Art

[0002] During the production process, adaptive control systems can automatically adjust their own parameters according to predetermined criteria to achieve optimal performance, and have been applied in many industrial fields. However, in the field of dyeing formula control, existing systems still have obvious shortcomings when dealing with complex changes in working conditions. The current system lacks sufficient adaptive adjustment capabilities when dealing with real-time changes in process parameters and is unable to automatically optimize the type and amount of dyes based on real-time collected data, resulting in less than ideal dyeing results. At the same time, due to fluctuations in environmental conditions such as temperature and humidity, the system's response speed and intelligence level are insufficient, affecting the stability and consistency of the dyeing process. In addition, existing adaptive control algorithms often exhibit slow convergence and poor stability when dealing with nonlinear and multivariable coupling problems in the dyeing process, which limits their application in actual production.

[0003] To this end, an intelligent conversion control method and system for dyeing formula for hosiery production is proposed. Summary of the Invention

[0004] The object of the present invention is to provide an intelligent conversion control method and system for dyeing formulas for hosiery production, which collects first product data and preset data of a product to be processed; constructs a conversion control model to analyze the preset data, obtains a product processing area, and combines the first product data to obtain the product area processing color tone and product area processing dosage; the dosage control is specifically as follows: based on the product processing area area, material and target color concentration, the initial treatment agent dosage is calculated; the initial treatment agent dosage is controlled in combination with the correction coefficient of the material type for the material absorption rate; based on the material density and the dye penetration depth, the treatment agent dosage is dynamically compensated to obtain the first processing data; a processing condition automatic control model is constructed to obtain the processing deviation; the processing time, processing temperature and processing pH value are controlled to reduce the processing deviation.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A dyeing formula intelligent conversion control method for hosiery production, comprising:

[0007] Collecting first product data of the product to be processed; obtaining preset data of the product to be processed;

[0008] A conversion control model is constructed to analyze preset data to obtain the product treatment area. The product treatment area and first product data are analyzed to determine the product area treatment color tone and product area treatment dosage. The product area treatment dosage is controlled by calculating the initial treatment agent dosage based on the product treatment area area, material water absorption rate, and target color concentration. The initial treatment agent dosage is adjusted based on the correction factor for material absorption rate based on material type. Dynamic compensation control of treatment agent dosage is performed based on the relationship between material density and dye penetration depth.

[0009] Divide the product to be processed into regions according to the product processing regions to obtain product regions to be processed; and process the product regions to be processed according to the product region processing hue and product region processing amount to obtain first processing data;

[0010] Constructing a processing condition automatic control model to obtain a processing deviation by comparing the first processing data with the preset data; when the processing deviation is greater than the preset processing deviation, controlling the processing time, processing temperature and processing pH value until the processing deviation is less than the preset processing deviation.

[0011] Preferably, the first product data includes hosiery material, material ratio, fabric structure, fabric tension, knitting density and color; the preset data includes target color, dye type and dye ratio; the first processing data includes actual dyed color, dyeing amount, actual dyeing time, actual temperature and actual pH value.

[0012] Preferably, the conversion control model includes a sock dyeing data processing and analysis layer, a sock dyeing area division layer, a regional dyeing hue control layer and a regional dyeing dosage control layer; the sock dyeing data processing and analysis layer obtains sock feature data by preprocessing and extracting features from the first product data; the sock dyeing area division layer divides the sock feature data into regions through a clustering algorithm to obtain product processing areas and boundaries and identifications of different dyeing areas; the regional dyeing hue control layer analyzes the product processing areas and boundaries, identifications and first product data of different dyeing areas through a color matching algorithm to obtain product area processing hues; the regional dyeing dosage control layer analyzes the product processing areas and boundaries, identifications and first product data of different dyeing areas based on material properties and dye dosage of the target color to obtain product area processing dosage.

[0013] Preferably, the regional dyeing hue control layer also includes control over the overlay of multiple colors in the same region. The specific process is as follows: analyzing the color types required for each dyeing layer based on the design pattern and color of the hosiery; determining the initial values ​​of each color layer by combining the color types, hosiery material, and dyeing properties; determining the overlay order of each color layer by analyzing the interaction between the dyeing process and the multiple colors; correcting the initial values ​​of each color layer using a color matching algorithm, and generating corrected color parameters based on the material absorptivity and reflectivity of the hosiery material and the overlay influence coefficient of adjacent color layers; obtaining the actual color chromaticity of each color layer after overlay based on the corrected color parameters and the overlay order; comparing the actual color with the target color, and dynamically adjusting the color parameters or overlay order until the color accuracy requirements are met if the color difference exceeds a preset threshold. The specific process is as follows: locating the layer where the color deviation occurs based on the color difference value distribution; if the deviation is due to the color parameters of a single layer, adjusting the concentration and ratio of the color in that layer based on the dye concentration-color rendering relationship; and if the deviation is due to interference from multiple overlays, recalculating the overlay order using a color overlay simulation model and updating the color compensation value after overlay.

[0014] Preferably, the processing condition automatic control model includes a data acquisition layer, a deviation analysis layer and a parameter control layer;

[0015] The data acquisition layer obtains the dyeing condition deviation by analyzing the first processed data and the preset data; the dyeing condition deviation includes the temperature difference , dyeing pH difference and dyeing time difference The deviation analysis layer analyzes the color difference between the dyeing condition deviation and the first processed data and the preset data through a multi-objective weighted fusion algorithm to obtain a comprehensive dyeing deviation value. The parameter control layer controls the comprehensive dyeing deviation value through the PID control algorithm Controls were performed to obtain adjusted dyeing time, temperature and pH value.

[0016] Preferably, the comprehensive staining deviation value The specific calculation formula is:

[0017] ;

[0018] in, is the color difference dynamic weight, is the color difference, is the dynamic weight of the coloring condition deviation, For preset time, To preset pH value, is the preset temperature.

[0019] An intelligent dyeing formula conversion control system for hosiery production, comprising:

[0020] A multi-parameter acquisition module, used to acquire first product data of the product to be processed and preset data of the product to be processed;

[0021] The conversion control model construction module is used to construct a conversion control model to analyze preset data and obtain the product treatment area. The module also analyzes the product treatment area and the first product data to obtain the product area treatment color tone and product area treatment dosage. The product area treatment dosage is controlled by calculating the initial treatment agent dosage based on the area of ​​the product treatment area, the material water absorption rate, and the target color concentration; adjusting and controlling the initial treatment agent dosage based on the correction factor for the material absorption rate based on the material type; and dynamically compensating and controlling the treatment agent dosage based on the relationship between material density and dye penetration depth.

[0022] The dyeing control module for the product to be processed is used to divide the product to be processed into regions according to the product processing regions to obtain the product regions to be processed; and to process the product regions to be processed according to the product region processing color tone and the product region processing amount to obtain first processing data;

[0023] The dyeing adaptive parameter adjustment control module constructs an automatic control model for processing conditions and obtains the processing deviation by comparing the first processing data with the preset data; when the processing deviation is greater than the preset processing deviation, the processing time, processing temperature and processing pH value are controlled until the processing deviation is less than the preset processing deviation.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention achieves intelligent control of hosiery dyeing by constructing a conversion control model and an automatic processing condition control model. By automatically collecting and analyzing hosiery data and preset data, it accurately divides dyeing areas and intelligently adjusts dyeing hues and amounts. By real-time monitoring of processing deviations and automatically adjusting dyeing conditions, the consistency and stability of dyeing quality are significantly improved.

[0026] 2. The conversion control model provided by this invention utilizes a clustering algorithm to segment hosiery feature data into regions and, in combination with a color matching algorithm, precisely controls the dye hue and amount for each region. When overlaying multiple layers of color, it analyzes the design pattern, corrects color parameters, and optimizes the overlay sequence to ensure the final color is consistent with the target. Furthermore, dye dosage calculation comprehensively considers factors such as the area of ​​the product treatment area, the material's water absorption rate, and the target color concentration. It also incorporates a correction factor for material absorption rate based on material type, as well as a dynamic compensation mechanism for material density and dye penetration depth, ensuring optimal dosage.

[0027] 3. The present invention uses an automatic control model for processing conditions to monitor deviations during the dyeing process in real time. The data acquisition layer analyzes the first processed data against preset data to determine temperature, pH, and time differences. The deviation analysis layer uses a multi-objective weighted fusion algorithm to analyze these deviations and color differences to determine a comprehensive deviation value. The parameter control layer uses a PID control algorithm to analyze this comprehensive deviation value and adjust the dyeing time, temperature, and pH value. By using the PID control algorithm to dynamically adjust the dyeing time, temperature, and pH value, the present invention rapidly responds to process changes, adjusts parameters to accommodate various production scenarios, and controls process deviations within a preset range, ensuring stable product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention provides a flow chart of an intelligent conversion control method for dyeing formulas used in hosiery production;

[0029] Figure 2 The present invention provides a structural schematic diagram of a dyeing formula intelligent conversion control system for hosiery production;

[0030] Figure 3 This is a schematic diagram of the conversion control model structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] Example 1

[0033] See also Figures 1 to 2 The present invention provides a dyeing formula intelligent conversion control method for hosiery production, which is applied to a dyeing formula intelligent conversion control system for hosiery production. The technical solution is as follows:

[0034] Collecting first product data of the product to be processed; obtaining preset data of the product to be processed;

[0035] A conversion control model is constructed to analyze preset data to obtain the product treatment area. The product treatment area and first product data are analyzed to determine the product area treatment color tone and product area treatment dosage. The product area treatment dosage is controlled by calculating the initial treatment agent dosage based on the product treatment area area, material water absorption rate, and target color concentration. The initial treatment agent dosage is adjusted based on the correction factor for material absorption rate based on material type. Dynamic compensation control of treatment agent dosage is performed based on the relationship between material density and dye penetration depth.

[0036] Divide the product to be processed into regions according to the product processing regions to obtain product regions to be processed; and process the product regions to be processed according to the product region processing hue and product region processing amount to obtain first processing data;

[0037] Constructing a processing condition automatic control model to obtain a processing deviation by comparing the first processing data with the preset data; when the processing deviation is greater than the preset processing deviation, controlling the processing time, processing temperature and processing pH value until the processing deviation is less than the preset processing deviation.

[0038] Furthermore, the first product data includes hosiery material, material ratio, fabric structure, fabric tension, knitting density and color; the preset data includes target color, dye type and dye ratio; the first processing data includes actual dyed color, dyeing amount, actual dyeing time, actual temperature and actual pH value.

[0039] In this example, by specifying the specific content of the first product data (including materials, fabric structure, etc.), the preset data (target color, dye ratio), and the first processing data (actual color, dosage, etc.), this method enables more precise dyeing control. Detailed data collection and analysis enables comprehensive monitoring of the dyeing process, ensuring accurate and consistent dyeing results. This data-driven control approach enhances the scientific nature and predictability of the process, providing a solid foundation for high-quality dyeing.

[0040] Furthermore, the conversion control model includes a sock dyeing data processing and analysis layer, a sock dyeing area division layer, a regional dyeing color tone control layer and a regional dyeing amount control layer, see Figure 3 ; The sock dyeing data processing and analysis layer obtains sock feature data by preprocessing and extracting features from the first product data; the sock dyeing area division layer divides the sock feature data into areas by a clustering algorithm to obtain product processing areas and boundaries and identifications of different dyeing areas; the regional dyeing tone control layer analyzes the product processing areas and boundaries, identifications and the first product data of different dyeing areas by a color matching algorithm to obtain product regional processing tones; the regional dyeing dosage control layer analyzes the product processing areas and boundaries, identifications and the first product data of different dyeing areas based on material properties and dye dosage of the target color to obtain product regional processing dosage.

[0041] In this embodiment, the conversion control model enhances the flexibility and adaptability of dyeing control through a multi-layered design involving data processing, zone division, hue control, and dosage control. Clustering and color matching algorithms are used to implement personalized dyeing management tailored to the characteristics of different hosiery products, improving the effectiveness and stability of the dyeing process. This structured design provides systematic support for complex dyeing tasks, ensuring efficient processing of diverse hosiery products. Compared to the adaptive control algorithm, the conversion control model provided in this embodiment significantly improves dyeing accuracy, zone division time, treatment agent dosage error, and the pass rate for complex patterns. See Table 1 for details.

[0042] Table 1 Transformation control model validity table

[0043]

[0044] Furthermore, the regional dyeing hue control layer also includes control over the overlay of multiple colors in the same region. The specific process is as follows: based on the design pattern and color of the hosiery, the color type required for each dyeing layer is analyzed; the initial value of each color is determined by combining the color type, hosiery material, and dyeing properties of the dye; the overlay order of each color is determined by analyzing the interaction between the dyeing process and the multiple colors; the initial value of each color layer is corrected using a color matching algorithm, and the corrected color parameters are generated based on the material absorptivity and reflectivity of the hosiery material and the overlay influence coefficient of adjacent color layers; the actual color chromaticity of each color layer after overlay is obtained based on the corrected color parameters and the overlay order; the actual color is compared with the target color, and if the color difference value exceeds a preset threshold, the color parameters or overlay order is dynamically adjusted until the color accuracy requirements are met. The specific process is as follows: based on the color difference value distribution, the layer where the color deviation occurs is located; if the deviation is caused by the color parameters of a single layer, the concentration and ratio of the color in that layer are adjusted based on the dye concentration-color rendering relationship; if the deviation is caused by interference from multiple overlays, the overlay order is recalculated using a color overlay simulation model, and the color compensation value after overlay is updated.

[0045] In this embodiment, the multi-layer color overlay control within the regional dyeing tone control layer significantly improves the precision of complex pattern dyeing by analyzing the design pattern, correcting color parameters, and optimizing the overlay sequence. Dynamically adjusting color parameters based on the hosiery material characteristics ensures consistent multi-layer overlay effects and meets high-precision requirements. See Table 2 for details. This mechanism supports design innovation while ensuring product appearance quality.

[0046] Table 2 Verification table of multi-layer color overlay correction effect

[0047]

[0048] Furthermore, the processing condition automatic control model includes a data acquisition layer, a deviation analysis layer and a parameter regulation layer;

[0049] The data acquisition layer obtains the dyeing condition deviation by analyzing the first processed data and the preset data; the dyeing condition deviation includes the temperature difference , dyeing pH difference and dyeing time difference The deviation analysis layer analyzes the color difference between the dyeing condition deviation and the first processed data and the preset data through a multi-objective weighted fusion algorithm to obtain a comprehensive dyeing deviation value. The parameter control layer controls the comprehensive dyeing deviation value through the PID control algorithm Perform analysis to obtain the adjusted dyeing time, temperature and pH value.

[0050] In this example, the automatic processing condition control model achieves real-time monitoring and dynamic adjustment of the dyeing process through a multi-layered design involving data acquisition, deviation analysis, and parameter control. Utilizing a multi-objective weighted fusion and PID control algorithm, it ensures precise control of dyeing conditions, improves the stability and reliability of dyeing quality, reduces human error, and increases production efficiency.

[0051] Furthermore, the comprehensive staining deviation value The specific calculation formula is:

[0052] ;

[0053] in, is the color difference dynamic weight, is color difference, is the dynamic weight of the coloring condition deviation, For preset time, To preset pH value, is the preset temperature.

[0054] In this example, a method for calculating dyeing deviations, combined with dynamic weighting and a multi-objective weighted fusion algorithm, achieves comprehensive assessment and precise control of process deviations. This quantitative approach enhances the controllability of the dyeing process, ensuring that dyeing results closely match preset targets, providing a scientific basis and process consistency for high-quality dyeing.

[0055] Furthermore, it also includes the dynamic replacement control process of the dyeing area, specifically:

[0056] After the dyeing process of the current dyeing area is completed, the dyeing area swap control is realized through the following steps:

[0057] Regional dyeing completion judgment:

[0058] Real-time monitoring of the actual color parameters, treatment agent dosage and treatment time of the current dyeing area; calculation of the color difference between the actual color parameters and the target color in the preset data; if the color difference is ≤1.5 and the treatment agent dosage error is ≤5%, the dyeing of the area is determined to be completed; simultaneous verification of whether the dyeing time meets the preset process requirements; if it times out, an alarm mechanism is triggered.

[0059] Next dye region matching logic:

[0060] Region selection strategy: Based on the region identification sequence generated by the sock dyeing region division layer, the next region is matched according to the following priority:

[0061] Areas with the same color requirements are given the highest priority to reduce the number of dye switching times; areas with adjacent physical locations are given second priority to shorten the movement path of the robotic arm; areas with complex patterns are processed in advance to avoid dye penetration interference.

[0062] Color matching controls:

[0063] The color matching algorithm in the regional dyeing tone control layer is called to extract the target color parameters of the next area. If the color difference between adjacent areas exceeds the preset color difference, the dye pipeline cleaning process is automatically inserted. Combined with the dynamic correction parameters of the material water absorption rate, the initial dyeing parameters of the next area are generated.

[0064] Dyeing equipment linkage control:

[0065] The socks are moved to the coordinate position of the next dyeing area through the robotic arm positioning system; the dye injection system automatically adjusts the nozzle pressure according to the new area and fabric tension; based on the material density-penetration depth relationship model, the dyeing temperature curve corresponding to the area is reloaded.

[0066] The dynamic switching control of dyeing areas added in this embodiment realizes efficient switching of dyeing areas through color difference threshold determination and multi-dimensional matching strategy. Please refer to Table 3 for details.

[0067] Table 3 Verification table of regional exchange control effect

[0068]

[0069] This invention achieves precise management of the hosiery dyeing process through intelligent dyeing recipe conversion control. Utilizing a conversion control model and an automatic processing condition control model, it automatically analyzes the initial product data and preset data for the hosiery, precisely divides the dyeing area, and dynamically adjusts the dyeing hue and dosage to ensure high consistency between the dyeing results and the preset targets. This intelligent control significantly reduces reliance on manual experience, improving production efficiency and product quality stability. Particularly in complex designs and multi-color dyeing scenarios, the system dynamically adjusts dye dosage based on area, material water absorption, target color concentration, and fabric properties, optimizing resource utilization and reducing production costs. Furthermore, the automatic processing condition control model dynamically adjusts dyeing time, temperature, and pH by comparing the initial processing data with preset data in real time, reducing rework due to uneven dyeing or color variations and ensuring the stability and reliability of the dyeing process. This method provides companies with an efficient and cost-effective production solution, enhancing process flexibility and market competitiveness, and is suitable for diverse hosiery dyeing needs.

[0070] Example 2

[0071] During the production process, the dyeing process is an important step in ensuring product quality and appearance. The process is dynamically affected by multiple factors such as dye adsorption characteristics, process parameters, and ambient temperature and humidity, which places extremely high demands on the precise control of the dyeing formula. Adaptive control systems can automatically adjust their own parameters according to predetermined criteria to achieve optimal performance and have been applied in many industrial fields. The present invention provides a dyeing formula intelligent conversion control method for hosiery production, which is applied to a dyeing formula intelligent conversion control system for hosiery production; wherein the dyeing formula intelligent conversion control system for hosiery production includes a multi-parameter acquisition module, a conversion control model construction module, a product dyeing control module to be processed, and a dyeing adaptive parameter adjustment control module;

[0072] Collecting first product data of the product to be processed; obtaining preset data of the product to be processed;

[0073] Furthermore, the first product data includes hosiery material, material ratio, fabric structure, fabric tension, knitting density, and color; the preset data includes target color, dye type, and dye ratio; and the first processed data includes actual dyed color, dyeing amount, actual dyeing time, actual temperature, and actual pH value. The first product data is acquired through a high-precision spectral sensor, a tension sensor, a material density detector, and a high-definition camera.

[0074] First product data: Multimodal sensors collect the physical properties and initial state of socks in real time, including:

[0075] The materials and proportions of socks use material density detectors and spectral sensors to identify fiber components, such as the proportions of cotton, nylon, and spandex, and are combined with a material database to match properties such as water absorption and dyeing affinity.

[0076] Fabric structure uses high-definition cameras, such as 3D structured light scanning, to capture fabric texture, porosity, and weave patterns, such as plain weave and rib, to generate a three-dimensional model to predict dye penetration paths.

[0077] The fabric tension is monitored in real time by embedded tension sensors in different areas of the sock body, and the dye spraying pressure is dynamically adjusted to prevent uneven coloring in deformed areas.

[0078] The knitting density is measured by a laser macro scanner to measure the number of yarn interlacing points per unit area. Combined with historical data, a density-penetration depth relationship model is established to compensate for the amount of dye used in high-density areas.

[0079] The initial color is measured by a high-precision spectral sensor with a wavelength range of 380-780nm and a resolution of ±0.1nm, which quantifies the Lab* value of the base color of the socks, providing a benchmark for subsequent color overlay.

[0080] Preset data: Target parameters are defined by process files or user input:

[0081] Target color: Based on the Pantone color card or the RGB / HEX values ​​provided by the customer, it is converted into the color rendering parameters of the dye formula.

[0082] Dye type and ratio: Select dyes according to material type, such as acid dyes for wool and disperse dyes for polyester, and match the optimal concentration ratio through the database.

[0083] A conversion control model is constructed to analyze the preset data to obtain the product processing area; and by analyzing the product processing area and the first product data, the product area processing color tone and the product area processing amount are obtained.

[0084] To achieve high-precision dynamic control of dyeing recipes, the multi-parameter acquisition module of the present invention utilizes multimodal sensing technology to acquire key data on hosiery materials, fabric structure, and environmental parameters in real time. Table 4 compares the technical specifications of traditional detection methods with those of the present sensor, demonstrating the improved detection accuracy of the multi-parameter acquisition module in dimensions such as material ratio, fabric tension, knit density, and initial color quantification. This provides highly reliable input data for the subsequent conversion control model.

[0085] Table 4 Comparison of detection accuracy of multi-parameter acquisition modules

[0086]

[0087] Furthermore, the conversion control model includes a hosiery dyeing data processing and analysis layer, a hosiery dyeing area division layer, a regional dyeing hue control layer, and a regional dyeing amount control layer; the hosiery dyeing data processing and analysis layer obtains hosiery feature data by preprocessing and extracting features from the first product data; wherein the hosiery feature data Including material eigenvectors , fabric structure feature vector and dynamic process parameters ;

[0088] The hosiery dyeing region division layer uses a clustering algorithm to divide the hosiery feature data into regions, obtaining the boundaries and identifiers of product processing regions and different dyeing regions; and uses an improved DBSCAN clustering algorithm to divide regions based on density and material similarity. The calculation formula is:

[0089] ;

[0090] in, For the Socks feature data to The distance between the feature data of socks, For the Individual hosiery product characteristic data: water absorption rate of materials, For the Individual hosiery product characteristic data: water absorption rate of materials, is the water absorption weight of the material, For the The knitting density in the characteristic data of socks, For the The knitting density in the characteristic data of socks, is the knitting density weight;

[0091] Region boundary identification uses edge detection algorithm to extract cluster boundaries.

[0092] The regional dyeing tone control layer analyzes the product treatment area, the boundaries of different dyeing areas, the identification, and the first product data using a color matching algorithm to obtain the product regional treatment tone. The control of the product regional treatment dosage is specifically as follows: calculating the initial treatment agent dosage based on the area of ​​the product treatment area, the water absorption rate of the material, and the target color concentration; adjusting and controlling the initial treatment agent dosage based on the correction coefficient of the material type for the material absorption rate; dynamically compensating and controlling the treatment agent dosage based on the relationship between material density and dye penetration depth; and controlling the treatment agent dosage through density-penetration compensation and tension compensation.

[0093] The calculation formula for the initial treatment agent dosage is:

[0094] ;

[0095] in, is the dosage of regional treatment agent, is the area of ​​the region, is the target color concentration, is the correction factor for the water absorption of the material, is the compensation factor of temperature on dye diffusion efficiency, is the difference between the actual temperature and the preset temperature;

[0096] The regional dyeing dosage control layer analyzes the product processing area, boundaries of different dyeing areas, identifications and first product data based on material properties and dye dosage of the target color to obtain the product regional processing dosage.

[0097] Furthermore, the regional dyeing tone control layer also includes control over the overlay of multiple colors in the same region. The specific process is as follows: based on the design pattern and color of the hosiery, the color type required for each dyeing layer is analyzed; the initial value of each color is determined by combining the color type, the hosiery material, and the dyeing characteristics of the dye; the overlay order of each color is determined by analyzing the dyeing process and the interaction between the multiple colors; the initial value of each color layer is corrected using a color matching algorithm, and the corrected color parameters are generated by combining the material absorptivity and reflectivity of the hosiery material and the overlay influence coefficient of adjacent color layers. The color matching algorithm is as follows:

[0098] Single-layer color correction is obtained using the CIELAB standard color difference calculation formula;

[0099] Multi-layer overlay correction is determined by the overlay influence coefficient; The calculation formula is ;in is the correction factor, and Respectively Layer color to The overlay influence coefficient of the layer color, and Respectively Layer and Dye concentration of the layer;

[0100] Based on the corrected color parameters and superposition order, the actual color chromaticity of each layer after color superposition is obtained; the actual color is compared with the target color, and if the color difference value exceeds a preset threshold, the color parameters or superposition order are dynamically adjusted until the color accuracy requirements are met; the specific process is as follows: according to the distribution of color difference values, the layer where the color deviation occurs is located; if the deviation comes from the color parameters of a single layer, the concentration and ratio of the color of the layer are adjusted based on the dye concentration-color rendering relationship; if the deviation comes from multi-layer superposition interference, the superposition order is recalculated through the color superposition simulation model, and the color compensation value after superposition is updated.

[0101] Divide the product to be processed into regions according to the product processing regions to obtain product regions to be processed; and dye the product regions to be processed according to the product region processing hue and product region processing amount to obtain first processing data;

[0102] Constructing a processing condition automatic control model to obtain a processing deviation by comparing the first processing data with the preset data; when the processing deviation is greater than the preset processing deviation, controlling the processing time, processing temperature and processing pH value until the processing deviation is less than the preset processing deviation.

[0103] Furthermore, the processing condition automatic control model includes a data acquisition layer, a deviation analysis layer and a parameter regulation layer;

[0104] The data acquisition layer obtains the dyeing condition deviation by analyzing the first processed data and the preset data; the dyeing condition deviation includes the temperature difference , dyeing pH difference and dyeing time difference The deviation analysis layer analyzes the color difference between the dyeing condition deviation and the first processed data and the preset data through a multi-objective weighted fusion algorithm to obtain a comprehensive dyeing deviation value. The parameter control layer controls the comprehensive dyeing deviation value through the PID control algorithm Perform analysis to obtain the adjusted dyeing time, temperature and pH value.

[0105] Furthermore, the comprehensive staining deviation value The specific calculation formula is:

[0106] ;

[0107] in, is the color difference dynamic weight, is color difference, is the dynamic weight of the coloring condition deviation, For preset time, To preset pH value, is the preset temperature.

[0108] During the dynamic control of dyeing deviations, the comprehensive dyeing deviation formula and the PID control algorithm work together to correct process parameter deviations in real time. Table 5 demonstrates the optimization effect of the proposed dynamic control mechanism on initial color difference, response time, and dye compensation using multiple sets of experimental data, demonstrating the model's rapid response and resource utilization advantages in complex dyeing scenarios.

[0109] Table 5 Verification of comprehensive staining deviation control effect

[0110]

[0111] When multiple layers of color are superimposed, the control of the product area processing amount also includes:

[0112] Allocate the amount of dye for each layer according to the coverage ratio of each layer color in the stacking order;

[0113] Simulate dye diffusion in the overlapping areas of adjacent color layers and adjust the amount of dye used in the overlapping areas based on the diffusion range;

[0114] When the color difference between the actual dyeing effect and the target color exceeds the threshold, compensation is preferentially performed by increasing or decreasing the amount of the bottom dye.

[0115] When adjusting the dyeing temperature and pH value, the parameter control layer simultaneously updates the dynamic weight of the product area processing amount, specifically:

[0116] If the temperature deviation causes the dye activity to decrease, the amount of dye should be increased proportionally; if the pH deviation causes the dye fixation rate to decrease, the amount of dye should be supplemented based on the ion concentration model.

[0117] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A dyeing formula intelligent conversion control method for hosiery production, characterized in that: include: Collecting first product data of the product to be processed; obtaining preset data of the product to be processed; Construct a conversion control model to analyze the preset data and obtain the product processing area; The product area treatment color tone and product area treatment dosage are obtained by analyzing the product treatment area and the first product data. The product area treatment dosage is controlled specifically by: calculating the initial treatment agent dosage based on the product treatment area area, the material water absorption rate, and the target color concentration; adjusting and controlling the initial treatment agent dosage based on the correction factor of the material type for the material absorption rate; and controlling the treatment agent dosage based on the relationship between the material density and the dye penetration depth. The conversion control model includes a hosiery dyeing data processing and analysis layer, a hosiery dyeing area division layer, a regional dyeing hue control layer, and a regional dyeing amount control layer. The hosiery dyeing data processing and analysis layer obtains hosiery feature data by preprocessing and extracting features from the first product data. The hosiery dyeing area division layer divides the hosiery feature data into regions using a clustering algorithm to obtain product processing areas and boundaries and identifiers of different dyeing areas. The regional dyeing hue control layer analyzes the boundaries and identifiers of the product processing areas and different dyeing areas and the first product data using a color matching algorithm to obtain product regional processing hues. The regional dyeing amount control layer analyzes the boundaries and identifiers of the product processing areas and different dyeing areas and the first product data based on material properties and target color dye amount to obtain product regional processing amounts. The products to be processed are divided into regions according to the product processing areas to obtain the product areas to be processed; the product areas to be processed are processed according to the product area processing color tone and the product area processing amount to obtain the first processing data; an automatic control model for processing conditions is constructed to obtain the processing deviation by comparing the first processing data with the preset data; when the processing deviation is greater than the preset processing deviation, the processing time, processing temperature and processing pH value are controlled until the processing deviation is less than the preset processing deviation.

2. The method for intelligent dyeing formula conversion control for hosiery production according to claim 1, characterized in that: The first product data includes the hosiery material, material ratio, fabric structure, fabric tension, knitting density and color; the preset data includes the target color, dye type and dye ratio; the first processing data includes the actual dyed color, dyeing amount, actual dyeing time, actual temperature and actual pH value.

3. The intelligent dyeing formula conversion control method for hosiery production according to claim 1, characterized in that: The regional dyeing hue control layer also includes control over the overlay of multiple colors in the same region. The specific process is as follows: based on the design pattern and color of the hosiery, the color type required for each dyeing layer is analyzed; the initial value of each color is determined based on the color type, hosiery material, and dyeing properties of the dye; the overlay order of each color is determined by analyzing the interaction between the dyeing process and the multiple colors; the initial value of each color layer is corrected using a color matching algorithm, and the corrected color parameters are generated based on the material absorptivity and reflectivity of the hosiery material and the overlay influence coefficient of adjacent color layers; the actual color chromaticity of each color layer after overlay is obtained based on the corrected color parameters and the overlay order; the actual color is compared with the target color, and if the color difference exceeds a preset threshold, the color parameters and overlay order are dynamically adjusted until the color accuracy requirements are met. The specific process of dynamically adjusting the color parameters and overlay order is as follows: based on the color difference value distribution, the layer where the color deviation occurs is located; if the deviation is caused by the color parameters of a single layer, the concentration and ratio of the color in that layer are adjusted based on the dye concentration-color rendering relationship; if the deviation is caused by interference from multiple overlays, the overlay order is recalculated using a color overlay simulation model, and the color compensation value after overlay is updated.

4. The method for intelligent dyeing recipe conversion control for hosiery production according to claim 1, characterized in that: The processing condition automatic control model includes a data acquisition layer, a deviation analysis layer and a parameter regulation layer; The data acquisition layer obtains the dyeing condition deviation by analyzing the first processed data and the preset data; the dyeing condition deviation includes the temperature difference , dyeing pH difference and dyeing time difference The deviation analysis layer analyzes the color difference between the dyeing condition deviation and the first processed data and the preset data through a multi-objective weighted fusion algorithm to obtain a comprehensive dyeing deviation value. The parameter control layer controls the comprehensive dyeing deviation value through the PID control algorithm Controls were performed to obtain adjusted dyeing time, temperature and pH value.

5. The intelligent dyeing formula conversion control method for hosiery production according to claim 4 is characterized in that: The comprehensive staining deviation value The specific calculation formula is: ； in, is the color difference dynamic weight, is the color difference, is the dynamic weight of the coloring condition deviation, For preset time, To preset pH value, is the preset temperature.

6. An intelligent dyeing formula conversion control system for hosiery production, characterized in that: The method for intelligent conversion control of dyeing formula for hosiery production according to claim 1 comprises: A multi-parameter acquisition module, used to acquire first product data of the product to be processed and preset data of the product to be processed; The conversion control model construction module is used to construct a conversion control model to analyze preset data and obtain the product treatment area. The module also analyzes the product treatment area and the first product data to obtain the product area treatment color tone and product area treatment dosage. The product area treatment dosage is controlled by calculating the initial treatment agent dosage based on the area of ​​the product treatment area, the material water absorption rate, and the target color concentration; adjusting and controlling the initial treatment agent dosage based on the correction factor for the material absorption rate based on the material type; and dynamically compensating and controlling the treatment agent dosage based on the relationship between material density and dye penetration depth. The dyeing control module for the product to be processed is used to divide the product to be processed into regions according to the product processing regions to obtain the product regions to be processed; and to process the product regions to be processed according to the product region processing color tone and the product region processing amount to obtain first processing data; The dyeing adaptive parameter adjustment control module constructs an automatic control model for processing conditions and obtains the processing deviation by comparing the first processing data with the preset data; when the processing deviation is greater than the preset processing deviation, the processing time, processing temperature and processing pH value are controlled until the processing deviation is less than the preset processing deviation.

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