An intelligent monitoring method and system for textile printing and dyeing production equipment

Abstract

The application discloses an intelligent monitoring method and system for a textile printing and dyeing production device, and relates to the field of intelligent control. The method comprises the following steps: determining first color depth estimation information according to initial dye liquor concentration information, working time information of a target dye vat, working temperature information and a process curve template of a target color depth; obtaining current dye liquor concentration information through an online spectrophotometer; determining second color depth estimation information according to the initial dye liquor concentration information, the current dye liquor concentration information, target fabric type information and shape parameter information of a target fabric; determining current color depth information according to the first color depth estimation information and the second color depth estimation information; and controlling the working state of the textile printing and dyeing production device according to the current color depth information and target color depth information. The method solves long-term problems such as uneven dyeing and batch color difference through double-path fusion detection and dynamic process optimization.

Description

A method and system for intelligent monitoring of textile printing and dyeing production equipment Technical Field

[0001] This specification relates to the field of intelligent control, and more specifically, to an intelligent monitoring method and system for textile printing and dyeing production equipment. Background Technology

[0002] Color depth control during the dyeing process is usually set through a fixed process flow and is not adjusted in real time throughout the dyeing cycle. Because the dyeing results are greatly affected by factors such as fabric type, fiber structure, initial concentration, and temperature fluctuations, traditional processes often struggle to take into account the adsorption characteristics of different fabrics and the kinetic changes of different dye systems.

[0003] In actual production, color depth often falls short of expectations, frequently requiring rework or manual intervention for adjustments and touch-ups. This leads to waste of dye and water resources, increased energy consumption, and extended processing cycles. Furthermore, the lack of a real-time monitoring mechanism forces dyeing quality to rely on experience-based judgment and post-processing inspections, limiting the automation, digitalization, and intelligentization of the dyeing and printing process.

[0004] With the development of online detection technologies (such as spectrophotometers) and data modeling methods, some systems have been able to monitor dye concentration, but an integrated intelligent control system based on multi-source data fusion, dyeing process evolution model-driven, and color depth estimation control linkage has not yet been formed.

[0005] Therefore, how to integrate process parameters, dye liquor characteristics, fabric properties, and real-time observation data to construct an accurate and highly responsive intelligent monitoring method remains an urgent problem to be solved in the textile printing and dyeing field. It is necessary to propose an intelligent monitoring method and system for textile printing and dyeing production equipment to at least solve some of the above problems. Summary of the Invention

[0006] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. The summary section of this invention is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] In a first aspect, the present invention proposes an intelligent monitoring method for textile printing and dyeing production equipment, the method comprising:

[0008] Obtain information on the target fabric type, the shape parameters of the target fabric, and the initial dye concentration of the target dye vat;

[0009] Obtain the working time information, working temperature information, and process curve template of the target dyeing vat for the target color depth;

[0010] The first color depth estimation information is determined based on the above initial dye liquor concentration information, the above target dyeing vat working time information, the above working temperature information, and the above target color depth process curve template.

[0011] Obtain the current dye solution concentration information using an online spectrophotometer;

[0012] The second color depth estimation information is determined based on the above initial dye concentration information, the above current dye concentration information, the above target fabric type information, and the above target fabric shape parameter information.

[0013] The current color depth information is determined based on the first color depth estimation information and the second color depth estimation information mentioned above;

[0014] The operating status of the textile printing and dyeing production equipment is controlled based on the current color depth information, target color depth information, and printing and dyeing stage information.

[0015] In one feasible implementation, the specific steps for constructing the above-mentioned process curve template for the target color depth include:

[0016] Obtain the type information, shape parameter information, and target color number information of the experimental fabric. The shape parameter information includes the weight information, thickness information, and width information of the fabric.

[0017] Based on the above experimental fabric and the above target color information, a standard dyeing experiment was conducted, and experimental temperature data, experimental dye concentration data and experimental color depth information were collected in time-related manner during the dyeing process.

[0018] Based on the above experimental temperature data and the above experimental dye concentration data, construct temperature-time change curves and dye concentration change curves;

[0019] Based on the above information about the type of experimental fabric, the target dye type was determined;

[0020] Based on the above experimental dye concentration data and experimental fabric type information, a dyeing process evolution model for the target color depth is determined, wherein the above dyeing process evolution model is a mathematical function describing the relationship between color depth and time.

[0021] The above temperature-time change curve, the above dye concentration change curve, and the above dyeing process evolution model are used as templates for the above target color depth process curve.

[0022] In one feasible implementation, the dyeing process evolution model for determining the target color depth based on the experimental dye concentration data and the type information of the experimental fabric includes:

[0023] The target dye information was determined based on the type information of the experimental fabric described above.

[0024] Based on the target dye information and the experimental dye concentration data, a dyeing process evolution model for the target color depth was determined.

[0025] In one feasible implementation, when the target dye information is a disperse dye, the process of determining the dyeing process evolution model of the target color depth includes:

[0026] Obtain information on the maximum achievable color depth, color depth growth rate constant, and time.

[0027] The dyeing process evolution model for the target color depth is determined based on the maximum achievable color depth, the color depth growth rate constant, and the time information.

[0028] In one feasible implementation, when the target dye information is a reactive dye, the process of determining the dyeing process evolution model of the target color depth includes:

[0029] Obtain the saturated adsorption capacity, Langmuir adsorption constant, dye concentration, and dyeing efficiency coefficient;

[0030] The dye adsorption amount was determined based on the above-mentioned saturated adsorption capacity, the above-mentioned Langmuir adsorption constant, and the above-mentioned dye liquor concentration.

[0031] The dyeing process evolution model for determining the target color depth is based on the above dyeing efficiency coefficient and the above dye adsorption amount.

[0032] In one feasible implementation, determining the second color depth estimation information based on the initial dye concentration information, the current dye concentration information, the target fabric type information, and the shape parameter information of the target fabric includes:

[0033] Calculate the current dye exhaustion rate based on the initial dye concentration information and the current dye concentration information mentioned above.

[0034] Find the corresponding dyeing efficiency coefficient and saturated adsorption capacity based on the target fabric type mentioned above;

[0035] Based on the above dye exhaustion rate, dyeing efficiency coefficient and saturated adsorption amount, the amount of dye adsorbed per unit area of ​​fabric is estimated.

[0036] The amount of dye adsorbed per unit area of ​​the fabric is mapped to the corresponding color depth value, which is used as the second color depth estimation information.

[0037] In one feasible implementation, determining the current color depth information based on the first color depth estimation information and the second color depth estimation information includes:

[0038] The absolute value of the color depth deviation is calculated based on the first color depth estimation information and the second color depth estimation information mentioned above;

[0039] Dynamic weighting information is determined based on the absolute value of color depth deviation, empirical deviation tolerance, and sensitivity factor mentioned above.

[0040] The current color depth information is determined based on the aforementioned dynamic weight information, the aforementioned first color depth estimation information, and the aforementioned second color depth estimation information.

[0041] In one feasible implementation, controlling the operating state of the textile printing and dyeing production equipment based on the current color depth information, target color depth information, and printing and dyeing stage information includes:

[0042] Determine the color depth deviation range based on the current color depth information and the target color depth information mentioned above;

[0043] The working status of the textile printing and dyeing production equipment is controlled based on the above-mentioned color depth deviation range and the above-mentioned printing and dyeing stage information.

[0044] In one feasible implementation, controlling the operating state of the textile printing and dyeing production equipment based on the color depth deviation range and the printing and dyeing stage information includes:

[0045] When the above-mentioned dyeing stage information indicates the heating stage, if the color depth deviation exceeds a first preset threshold, the control device increases the heating rate or extends the heating time; if the color depth deviation is lower than a second preset threshold, the control device decreases the heating rate or delays the heating start, wherein the first preset threshold is greater than the second preset threshold; and / or,

[0046] When the above-mentioned dyeing stage information indicates the heat preservation stage, if the above-mentioned color depth deviation exceeds the above-mentioned first preset threshold, the control equipment extends the heat preservation time or accelerates the dye liquor circulation; if the above-mentioned color depth deviation is lower than the above-mentioned second preset threshold, the control equipment shortens the heat preservation time or reduces the liquor ratio; and / or,

[0047] When the above dyeing stage information indicates a slow cooling stage, if the above color depth deviation exceeds the above first preset threshold, the control device activates the re-dyeing cycle process or increases the cycle temperature. If the above color depth deviation is lower than the above second preset threshold, the control device accelerates the cooling rate and starts the dye reduction device.

[0048] Secondly, this invention proposes an intelligent monitoring system for textile printing and dyeing production equipment, comprising:

[0049] The first acquisition unit is used to acquire information about the target fabric type, the shape parameters of the target fabric, and the initial dye concentration of the target dye vat.

[0050] The second acquisition unit is used to acquire the working time information, working temperature information, and process curve template of the target dyeing vat for the target color depth.

[0051] The first determining unit is used to determine the first color depth estimation information based on the above-mentioned initial dye liquor concentration information, the above-mentioned target dyeing vat working time information, the above-mentioned working temperature information and the above-mentioned target color depth process curve template.

[0052] The third acquisition unit is used to acquire the current dye concentration information through an online spectrophotometer;

[0053] The second determining unit is used to determine the second color depth estimation information based on the above-mentioned initial dye concentration information, the above-mentioned current dye concentration information, the above-mentioned target fabric type information and the above-mentioned target fabric shape parameter information.

[0054] The third determining information is used to determine the current color depth information based on the first color depth estimation information and the second color depth estimation information mentioned above.

[0055] The control unit is used to control the working status of the textile printing and dyeing production equipment based on the current color depth information, target color depth information, and printing and dyeing stage information.

[0056] In summary, this invention constructs a temperature-time curve, a dye concentration change curve, and a dyeing process evolution model to form a standardized process curve template for the target color depth. This enables the system to predict the process, thereby reducing reliance on manual experience to judge the dyeing progress. This method simultaneously calculates theoretically estimated values ​​(first color depth information) and actual observed values ​​(second color depth information) and intelligently fuses them, improving the accuracy and stability of the current color depth estimation and overcoming the risks caused by single model errors or sensor fluctuations. By identifying the dyeing stages (heating, heat preservation, slow cooling) and combining them with the color depth deviation amplitude to execute different response operations (such as temperature adjustment, liquor addition, circulation, etc.), fine-grained control logic is achieved, effectively avoiding over-dyeing or under-dyeing problems and significantly improving dyeing consistency. This invention supports the construction of dyeing process models for reactive and disperse dyes and considers the fabric's thickness, width, weight, and other shape parameters, demonstrating strong adaptability and applicability to multi-variety, small-batch dyeing production scenarios. Through real-time sensing and feedback control closed loop, the system can automatically adjust the dyeing process progress without human intervention, effectively reducing rework, dye waste and energy consumption, and helping the textile printing and dyeing industry achieve green transformation and digital upgrading.

[0057] The intelligent monitoring method for textile printing and dyeing production equipment proposed in this invention will be partly apparent from the following description, and partly understood by those skilled in the art through study and practice of this invention. Attached Figure Description

[0058] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0059] Figure 1 is a flowchart illustrating an intelligent monitoring method for textile printing and dyeing production equipment according to an embodiment of the present invention.

[0060] Figure 2 is a flowchart illustrating a method for constructing a process curve template for a target color depth according to an embodiment of the present invention.

[0061] Figure 3 is a schematic flowchart of a dyeing process evolution model method for determining target color depth provided by an embodiment of the present invention;

[0062] Figure 4 is a flowchart illustrating a dyeing process evolution model method for determining the target color depth when the target dye information is a disperse dye, according to an embodiment of the present invention.

[0063] Figure 5 is a schematic flowchart of a dyeing process evolution model method for determining the target color depth when the target dye information is a reactive dye, according to an embodiment of the present invention.

[0064] Figure 6 is a flowchart illustrating a method for determining second color depth estimation information according to an embodiment of the present invention.

[0065] Figure 7 is a flowchart illustrating a method for determining current color depth information according to an embodiment of the present invention.

[0066] Figure 8 is a schematic flowchart of a method for controlling the working status of the above-mentioned textile printing and dyeing production equipment according to an embodiment of the present invention.

[0067] Figure 9 is a schematic diagram of the structure of an intelligent monitoring system for textile printing and dyeing production equipment provided in an embodiment of the present invention. Detailed Implementation

[0068] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this invention will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them.

[0069] Please refer to Figure 1, which is a flowchart illustrating an intelligent monitoring method for textile printing and dyeing production equipment according to an embodiment of the present invention. Specifically, it may include:

[0070] S110. Obtain the target fabric type information, the shape parameter information of the target fabric, and the initial dye liquor concentration information of the target dyeing vat.

[0071] S120. Obtain the working time information, working temperature information, and process curve template of the target dyeing vat for the target color depth.

[0072] S130. Determine the first color depth estimation information based on the above initial dye liquor concentration information, the above target dyeing vat working time information, the above working temperature information, and the above target color depth process curve template.

[0073] S140. Obtain the current dye solution concentration information through an online spectrophotometer;

[0074] S150. Determine the second color depth estimation information based on the above initial dye concentration information, the above current dye concentration information, the above target fabric type information, and the above target fabric shape parameter information.

[0075] S160. Determine the current color depth information based on the first color depth estimation information and the second color depth estimation information.

[0076] S170. Control the working status of the textile printing and dyeing production equipment based on the current color depth information, target color depth information and printing and dyeing stage information.

[0077] For example, in step S110, key input parameters are first obtained from the target dyeing vat, including: the type of target fabric (such as cotton, polyester, nylon, etc.), shape parameter information (including weight, thickness, and width, used to estimate the dye adsorption capacity per unit area), and initial dye liquor concentration information, i.e., the effective dye concentration in the dye liquor before dyeing begins. The target dyeing vat can be the dyeing vat of an airflow dyeing machine.

[0078] Next, in step S120, the system obtains the working time information (such as heating duration, heat preservation time, etc.) and working temperature information (such as maximum temperature and temperature change curve) set in this dyeing process, as well as the target color depth process curve template corresponding to the target fabric and color number. This template is constructed from historical experimental dyeing data and includes temperature-time curve, dye liquor concentration change curve and dyeing process evolution model.

[0079] In step S130, the system calculates the target color depth that should be achieved in this stage based on the initial dye concentration, the current time position and temperature data, and the dyeing process evolution model corresponding to the selected process curve template. This generates the first color depth estimation information, which can be regarded as a theoretical estimate.

[0080] Subsequently, in step S140, the current absorbance of the dye liquor is measured in real time by a spectrophotometer installed online in the return pipe or liquid surface of the dye vat, and the concentration of residual dye in the current dye liquor is deduced.

[0081] In step S150, the system calculates the proportion of dye that has been adsorbed by the fabric (i.e., exhaustion rate) by combining the initial dye concentration and the difference between the current concentration and the current concentration. Then, based on the target fabric type, weight, thickness, and other shape characteristics, the system calls the corresponding dyeing efficiency coefficient and saturation adsorption parameter to further estimate the actual amount of dye currently adsorbed per unit area of ​​fabric, and maps it to the corresponding color depth value, thereby obtaining the second color depth estimation information, which reflects the actual measured color depth trend.

[0082] In step S160, the first color depth estimation information and the second color depth estimation information are fused and calculated to generate more accurate and stable current color depth information, which represents the overall dyeing progress of the fabric.

[0083] Finally, in step S170, based on the deviation between the current color depth information and the target color depth, and in conjunction with the current dyeing stage (such as heating, holding, or slow cooling), an appropriate control strategy is selected. For example: if it is in the heating stage and the color is too light, the heating rate can be increased; if it is in the holding stage and the color is too dark, the holding time can be shortened or the liquor ratio can be reduced; if it is in the slow cooling stage and the color is too light, re-dyeing or delayed cooling can be initiated. The control command will be automatically sent to the dyeing vat control system, driving the temperature control module, pump circulation system, liquid replenishment device, etc., to achieve closed-loop intelligent control of the dyeing process.

[0084] In summary, this invention constructs a temperature-time curve, a dye concentration change curve, and a dyeing process evolution model to form a standardized process curve template for the target color depth. This enables the system to predict the process, thereby reducing reliance on manual experience to judge the dyeing progress. This method simultaneously calculates theoretically estimated values ​​(first color depth information) and actual observed values ​​(second color depth information) and intelligently fuses them, improving the accuracy and stability of the current color depth estimation and overcoming the risks caused by single model errors or sensor fluctuations. By identifying the dyeing stages (heating, heat preservation, slow cooling) and combining them with the color depth deviation amplitude to execute different response operations (such as temperature adjustment, liquor addition, circulation, etc.), fine-grained control logic is achieved, effectively avoiding over-dyeing or under-dyeing problems and significantly improving dyeing consistency. This invention supports the construction of dyeing process models for reactive and disperse dyes and considers the fabric's thickness, width, weight, and other shape parameters, demonstrating strong adaptability and applicability to multi-variety, small-batch dyeing production scenarios. Through real-time sensing and feedback control closed loop, the system can automatically adjust the dyeing process progress without human intervention, effectively reducing rework, dye waste and energy consumption, and helping the textile printing and dyeing industry achieve green transformation and digital upgrading.

[0085] In one feasible implementation, as shown in Figure 2, which is a flowchart illustrating a method for constructing a process curve template for a target color depth according to an embodiment of the present invention, the specific steps for constructing the aforementioned process curve template for the target color depth include:

[0086] S210. Obtain the type information, shape parameter information and target color number information of the experimental fabric, wherein the shape parameter information includes the weight information, thickness information and width information of the fabric.

[0087] S220. Based on the above experimental fabric and the above target color information, a standard dyeing experiment is conducted, and experimental temperature data, experimental dye concentration data and experimental color depth information related to time are collected during the dyeing process.

[0088] S230. Based on the above experimental temperature data and the above experimental dye concentration data, construct temperature-time change curves and dye concentration change curves.

[0089] S240. Based on the above information about the type of experimental fabric, determine the type of target dye;

[0090] S250. Based on the above experimental dye concentration data and experimental fabric type information, determine the dyeing process evolution model of the target color depth, wherein the above dyeing process evolution model is a mathematical function describing the relationship between color depth and time.

[0091] S260. Use the above temperature-time change curve, the above dye concentration change curve, and the above dyeing process evolution model as the template for the above target color depth process curve.

[0092] For example, this embodiment provides a specific method for constructing a target color depth process curve template. Its core idea is to establish a standardized template that accurately describes the evolution of color depth over time during the dyeing process through experimental data acquisition and model derivation. This template provides a crucial reference for subsequent dyeing process estimation and control. The method specifically includes the following steps:

[0093] First, in step S210, the system acquires basic information about representative experimental fabrics, including their type (e.g., whether they are cotton, polyester, nylon, etc.) and their shape parameters, such as weight per unit area, fabric thickness, and width. These parameters affect the dye adsorption behavior of the fabric and are the basis for the accurate construction of subsequent models. Simultaneously, the target color information is also acquired to clarify the dyeing target.

[0094] Next, in step S220, the system selects the aforementioned fabric sample and performs a complete standard dyeing experiment according to the process requirements. During this process, the system collects key experimental data in real time, including: time-series temperature data during the dyeing process (temperature control curves reflecting the heating, holding, and cooling stages), dye liquor concentration data (measured by sampling or an online spectrophotometer), and fabric color depth information (obtained through mid-process sampling or end-point back-calculation). This data will serve as the basis for subsequent model training and curve construction.

[0095] In step S230, the system performs data processing and analysis on the collected experimental temperature and dye concentration data, constructing standard temperature-time curves and dye concentration curves, respectively. These two curves accurately describe the temperature control path and dye dissipation dynamics during the dyeing process, providing a fundamental representation of environmental changes during the dyeing process.

[0096] Subsequently, in step S240, the system further identifies the matching dye type based on the fabric type information, such as whether the experiment uses disperse dyes, reactive dyes, or other types of dyes. The dyeing kinetics mechanisms of different dyes vary considerably, therefore they need to be modeled separately.

[0097] In step S250, the system uses the dye liquor concentration data obtained in the experiment as a basis, combined with the fabric type (i.e., its adsorption and dyeing characteristics), to select a suitable dyeing process evolution model for fitting. For example, for disperse dyes, an exponential model of color depth growth can be used; for reactive dyes, a model based on Langmuir adsorption theory can be used to reflect the combined effect of saturated adsorption and dyeing efficiency. Through model fitting, a mathematical function describing the relationship between color depth and time evolution can be obtained, i.e., the dyeing process evolution model of the target color depth.

[0098] Finally, in step S260, the system encapsulates the completed temperature-time change curve, dye concentration change curve, and the aforementioned dyeing process evolution model into a standardized template, which serves as the "target color depth process curve template" for this fabric type and color number under a specific dye system. This template will serve as the basis for generating the first color depth estimation information in the control logic, supporting intelligent prediction and control.

[0099] In summary, this implementation, through a systematic data collection and modeling process, transforms traditional experience-based processes into reproducible, calculable, and predictable digital templates, providing strong data support and a model foundation for the intelligent control of textile printing and dyeing processes.

[0100] In one feasible implementation, as shown in Figure 3, which is a flowchart illustrating a method for determining a target color depth using a dyeing process evolution model according to an embodiment of the present invention, step S250, based on the experimental dye liquor concentration data and the type information of the experimental fabric, determines the dyeing process evolution model for the target color depth, including:

[0101] S2501. Determine the target dye information based on the type information of the experimental fabric mentioned above;

[0102] S2502. Based on the above target dye information and the above experimental dye concentration data, determine the dyeing process evolution model for the target color depth.

[0103] In one feasible implementation, as shown in Figure 4, which is a flowchart illustrating a dyeing process evolution model method for determining the target color depth when the target dye information is a disperse dye, the process of determining the dyeing process evolution model for the target color depth includes:

[0104] S310. Obtain the maximum achievable color depth, color depth growth rate constant, and time information;

[0105] S320. Determine the dyeing process evolution model for the target color depth based on the maximum achievable color depth, the color depth growth rate constant, and the time information.

[0106] In one feasible implementation, as shown in Figure 5, which is a flowchart illustrating a method for determining a target color depth using a dyeing process evolution model when the target dye information is a reactive dye, the process of determining the target color depth using the dyeing process evolution model includes:

[0107] S410, obtain saturated adsorption capacity, Langmuir adsorption constant, dye concentration and dyeing efficiency coefficient;

[0108] S420. Determine the dye adsorption amount based on the above saturated adsorption amount, the above Langmuir adsorption constant, and the above dye liquor concentration.

[0109] S430. Determine the dyeing process evolution model for the target color depth based on the above dyeing efficiency coefficient and the above dye adsorption amount.

[0110] For example, firstly, in step S2501, the system identifies the target dye information based on the type information of the experimental fabric. For instance, if the experimental fabric is polyester (such as polyester fiber), the system determines the target dye information to be a disperse dye; if it is a cellulose material such as cotton or linen, the corresponding target dye information is a reactive dye. This identification process can be completed automatically based on a built-in fiber-dye reference table, or it can be identified through experimental formula records to determine the dye type and its concentration range.

[0111] Next, in step S2502, the system selects an appropriate mathematical modeling mechanism based on the identified dye type and, in conjunction with the dye concentration data recorded during the experiment, constructs a dyeing process evolution model for the target color depth. The modeling paths for disperse dyes and reactive dyes are detailed below:

[0112] In disperse dye applications, color depth evolution typically exhibits a dynamic characteristic of increasing towards saturation over time. The model construction process for this involves:

[0113] S310: Obtain the maximum achievable color depth obtained from the fitting in the experiment. Color depth growth rate constant And the corresponding time information t. Wherein, This represents the theoretical saturation color depth under the current fabric and dye system. It reflects the dyeing rate.

[0114] S320: Based on the above parameters, a colorimetric evolution function is constructed, typically using a first-order kinetic exponential model.

[0115]

[0116] In this model, the color depth increases rapidly in the early stage of dyeing and then gradually stabilizes, which can better fit the diffusion and fixation behavior of disperse dyes in hydrophobic fabrics.

[0117] In the application of reactive dyes, dye molecules undergo an adsorption-reaction process with the fiber surface, thus their color depth evolution behavior better conforms to the adsorption equilibrium theory. Model construction includes:

[0118] S410: Obtain the saturated adsorption capacity measured in the experiment. Langmuir adsorption constant The current dye concentration C in the dye bath, and the dyeing efficiency coefficient of the fabric dyeing system. .in, This indicates the maximum amount of dye that a unit area of ​​fabric can absorb. Used to measure adsorption affinity. It reflects the conversion efficiency from adsorption amount to actual color depth.

[0119] S420: Calculate the adsorption capacity A per unit area of ​​fabric based on the Langmuir adsorption model.

[0120]

[0121] S430: Combining the dyeing efficiency coefficient, the adsorption amount is mapped to a color depth value:

[0122]

[0123] in, This represents the color depth value corresponding to time t. This model considers the concentration saturation effect and the adsorption conversion process, and can more accurately simulate the dyeing process behavior of reactive dyes.

[0124] Through the above modeling process, specific mathematical models were constructed for disperse dyes and reactive dyes to describe the evolution of color depth over time. This dyeing process evolution model will ultimately serve as an important component of the target color depth process curve template. This embodiment achieves dye type-driven modeling, improving model adaptability; it can automatically extract dyeing process patterns from experimental data, supporting the construction of digital process templates; and it can serve as the core logic unit of the color depth prediction module in an intelligent control system.

[0125] In one feasible implementation, as shown in FIG6, FIG6 is a flowchart illustrating a method for determining second color depth estimation information provided by an embodiment of the present invention. Step S150, which determines the second color depth estimation information based on the initial dye concentration information, the current dye concentration information, the target fabric type information, and the shape parameter information of the target fabric, includes:

[0126] S1501. Calculate the current dye exhaustion rate based on the above initial dye liquor concentration information and the above current dye liquor concentration information;

[0127] S1502. Find the corresponding dyeing efficiency coefficient and saturated adsorption amount according to the target fabric type mentioned above.

[0128] S1503. Based on the above dye exhaustion rate, the above dyeing efficiency coefficient and the above saturated adsorption amount, estimate the amount of dye adsorbed per unit area of ​​fabric.

[0129] S1504. Map the amount of dye adsorbed per unit area of ​​the fabric to the corresponding color depth value, and use it as the second color depth estimation information.

[0130] For example, firstly, the system utilizes the initial dye concentration Compared with the current dye concentration The change in the dye exhaustion rate is used to calculate the current dye exhaustion rate. This parameter represents the proportion of dye that migrates from the dye bath to the fabric, and is a core indicator for assessing the degree of dye uptake.

[0131] The calculation formula is as follows:

[0132]

[0133] in, This represents the initial dye concentration, in units of... ; The real-time concentration of the dye solution at time t, in units of ; This represents the dye exhaustion rate, ranging from 0 to 1.

[0134] Based on the type of target fabric (e.g., cotton, polyester, nylon), two key parameters are retrieved from a pre-defined material database: dyeing efficiency coefficient. This represents the contribution of a unit adsorption amount to color depth, expressed in units of... or ; Saturated adsorption capacity This represents the theoretical maximum amount of dye that a unit area of ​​fabric can absorb, expressed in units of... .

[0135] Based on the absorption rate in the previous step With saturated adsorption capacity Estimate the amount of dye currently adsorbed per unit area of ​​fabric. The calculation formula is:

[0136]

[0137] in, The mass of dye adsorbed per unit area of ​​fabric at time t, in units of ; This refers to the absorption rate; This represents the saturation adsorption capacity.

[0138] Finally, the system will determine the amount of dye adsorbed. Mapped to actual color depth values As information presumed for the second color depth:

[0139]

[0140] in, To estimate the color depth value, one can use... value or express; This is the dyeing efficiency coefficient; This represents the amount of dye adsorbed per unit area.

[0141] This formula integrates the changes in dye concentration with the adsorption characteristics of the fabric, and constructs an indirect color depth sensing mechanism. It avoids the need for frequent physical sampling of the fabric. By combining physical adsorption laws and process experience parameters, it achieves indirect and high-precision estimation of fabric color depth. It is calculated by matching dye data with the database, without the need for actual fabric sampling.

[0142] In one feasible implementation, as shown in FIG7, FIG7 is a schematic flowchart of a method for determining current color depth information provided by an embodiment of the present invention. The above-mentioned step S160 determines the current color depth information based on the above-mentioned first color depth estimation information and the above-mentioned second color depth estimation information, including:

[0143] S1601. Calculate the absolute value of the color depth deviation based on the first color depth estimation information and the second color depth estimation information.

[0144] S1602. Determine dynamic weighting information based on the absolute value of color depth deviation, empirical deviation tolerance, and sensitivity factor mentioned above.

[0145] S1603. Determine the current color depth information based on the above dynamic weight information, the above first color depth estimation information, and the above second color depth estimation information.

[0146] For example, to more accurately estimate the current color depth state during the dyeing process, this invention provides a dynamic weighted fusion mechanism that integrates two independent inference paths (i.e., "first color depth estimation information" and "second color depth estimation information"). This mechanism can adaptively evaluate the degree of deviation between the two estimation results and dynamically assign confidence weights, thereby generating a more reliable current color depth estimate. The process includes the following three steps:

[0147] S1601: First, the system estimates the information based on the first color depth. Information related to the second color depth estimation Calculate the absolute value of the color depth difference between the two. This is used to measure the consistency between the results of two hypothetical paths:

[0148]

[0149] in: Information is inferred for the first color depth. Information for estimating the second color depth is derived by inversely calculating the dye exhaustion rate and fabric parameters. This is the absolute value of the color depth deviation, in units of .

[0150] S1602: To avoid blind averaging, an empirical tolerance threshold is introduced. and sensitivity factor A dynamic weighting function is constructed to assign higher fusion weights to more reliable paths.

[0151] Specifically, let the weight of the first presumed value be... Define the following sigmoid type function:

[0152]

[0153] in: The dynamic weight of the first presumed path; This is a sensitivity factor that determines the strength of the response to the deviation caused by changes in weights; This represents the empirical deviation tolerance, indicating the permissible estimated difference limit; Absolute value of color depth deviation.

[0154] when At that time, the weight is in the middle (e.g.) );when When the two paths are at the same height, Balance and integration; when When the results are inconsistent, the system will favor the more stable path (e.g., the default path can be set to favor the second path).

[0155] S1603: Based on the obtained dynamic weights The system integrates two presumed pieces of information. This yields the final estimated current color depth value. :

[0156]

[0157] in: This is the final current color depth value, used by subsequent control modules; The weights are adaptively adjusted based on the deviation. These are presuppositions from two different sources.

[0158] This invention integrates two types of estimation paths to effectively address sensor errors and external disturbances. A sigmoid weighting function smooths the transition, avoiding "threshold jumps"; and parameters λ and δ are used to adjust system sensitivity to adapt to different staining stability requirements.

[0159] In one feasible implementation, as shown in FIG8, FIG8 is a flowchart illustrating a method for controlling the working state of the textile printing and dyeing production equipment according to an embodiment of the present invention. Step S170 controls the working state of the textile printing and dyeing production equipment based on the current color depth information, target color depth information, and printing and dyeing stage information, including:

[0160] S1701. Determine the color depth deviation range based on the current color depth information and the target color depth information mentioned above;

[0161] S1702. Control the working status of the textile printing and dyeing production equipment according to the above-mentioned color depth deviation range and the above-mentioned printing and dyeing stage information.

[0162] In one feasible implementation, step S1702, which controls the operating state of the textile printing and dyeing production equipment based on the color depth deviation range and the printing and dyeing stage information, includes:

[0163] S1702-A: When the above-mentioned dyeing stage information indicates the heating stage, if the above-mentioned color depth deviation exceeds a first preset threshold, the control device increases the heating rate or extends the heating time; if the above-mentioned color depth deviation is lower than a second preset threshold, the control device decreases the heating rate or delays the heating start, wherein the above-mentioned first preset threshold is greater than the above-mentioned second preset threshold; and / or,

[0164] S1702-B: When the above-mentioned dyeing stage information indicates the heat preservation stage, if the above-mentioned color depth deviation exceeds the above-mentioned first preset threshold, the control equipment extends the heat preservation time or accelerates the dye liquor circulation; if the above-mentioned color depth deviation is lower than the above-mentioned second preset threshold, the control equipment shortens the heat preservation time or reduces the liquor ratio; and / or,

[0165] S1702-C. When the above-mentioned dyeing stage information indicates a slow cooling stage, if the above-mentioned color depth deviation exceeds the above-mentioned first preset threshold, the control device activates the re-dyeing cycle process or increases the cycle temperature. If the above-mentioned color depth deviation is lower than the above-mentioned second preset threshold, the control device accelerates the cooling rate and starts the dye reduction device.

[0166] For example, in order to achieve precise closed-loop control of the dyeing process, the key process parameters of the dyeing equipment are dynamically adjusted based on the deviation between the current color depth information and the target color depth information, and in combination with the stage attributes of the dyeing process (such as heating, heat preservation, and slow cooling), so as to achieve refined control and improve quality stability at different stages of the dyeing process.

[0167] The specific process is as follows:

[0168] S1701: First, obtain the color depth estimate at the current moment. Color depth as expected Calculate the magnitude of the deviation between them. This serves as a core indicator for subsequent control decisions. Its calculation formula is as follows:

[0169]

[0170] in: To integrate the estimated current color depth information; The target color depth is set; This indicates the degree to which the current staining state deviates from the target, expressed in units of... .

[0171] S1702: Based on the current dyeing and printing stage information (heating, heat preservation, slow cooling) and deviation range The system executes the following phased control strategy:

[0172] S1702-A: The specific control strategy for the heating stage includes: when the dyeing stage information is marked as the "heating stage" (such as the initial stage)... Within minutes, if First preset threshold (Significant deviation, delayed dyeing process) Control the heating system to increase the heating rate, accelerate dye diffusion, or extend the heating time to postpone entering the next process stage, giving the system more response time. If Second preset threshold (Minimum deviation, dyeing process ahead of schedule), the control system reduces the heating rate or delays the heating start time to prevent premature accumulation of color depth. Among these, the first threshold... Greater than the second threshold .

[0173] S1702-B: The specific control strategy for the heat preservation stage includes: when the dyeing process enters the "heat preservation" stage (such as...) At the minute mark, the control strategy focuses on stabilizing the staining reaction: if Extend the holding time to increase the reaction time; or accelerate the dye liquor circulation to improve the dye mass transfer rate. Shorten the holding time to prevent over-dyeing; or reduce the liquor ratio to lower the dye concentration gradient. This stage mainly involves dynamically adjusting the reaction environment to avoid overshooting when the color depth is close to the target.

[0174] S1702-C: The slow cooling stage control strategy specifically includes: when the system is in the "slow cooling" stage (such as the final stage of the dyeing process, after 45 minutes), pay attention to the final correction and stability of the color depth. To reactivate the dyeing cycle (e.g., reintroduce dye liquor and re-immerse the fabric) or increase the cycle temperature, delaying cooling to continue dyeing. It accelerates the cooling rate and quickly terminates the staining reaction; and can activate the destaining device (such as using a slow-release reducing agent) to repair slight overstaining.

[0175] The method proposed in this invention avoids a single control logic and reflects the dynamic sensitivity of color depth at different process stages. It adaptively adjusts dyeing parameters based on the magnitude of color depth error, thereby improving consistency.

[0176] Secondly, as shown in Figure 9, this invention proposes an intelligent monitoring system for textile printing and dyeing production equipment, comprising:

[0177] The first acquisition unit 21 is used to acquire information about the type of target fabric, the shape parameters of the target fabric, and the initial dye concentration of the target dye vat.

[0178] The second acquisition unit 22 is used to acquire the working time information, working temperature information and process curve template of the target dyeing vat for the target color depth;

[0179] The first determining unit 23 is used to determine the first color depth estimation information based on the above-mentioned initial dye liquor concentration information, the above-mentioned target dyeing vat working time information, the above-mentioned working temperature information and the above-mentioned target color depth process curve template.

[0180] The third acquisition unit 24 is used to acquire the current dye concentration information through an online spectrophotometer;

[0181] The second determining unit 25 is used to determine the second color depth estimation information based on the above-mentioned initial dye concentration information, the above-mentioned current dye concentration information, the above-mentioned target fabric type information and the above-mentioned target fabric shape parameter information.

[0182] The third determination information 26 is used to determine the current color depth information based on the first color depth estimation information and the second color depth estimation information mentioned above;

[0183] The control unit 27 is used to control the working status of the textile printing and dyeing production equipment based on the current color depth information, target color depth information and printing and dyeing stage information.

[0184] Understandably, an intelligent monitoring system for textile printing and dyeing production equipment can also perform the steps of any of the methods described in the first aspect.

[0185] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent monitoring of textile printing and dyeing production equipment, characterized in that, include: Obtain information on the target fabric type, the shape parameters of the target fabric, and the initial dye concentration of the target dye vat; Obtain the working time information, working temperature information, and process curve template of the target dyeing vat for the target color depth; Based on the initial dye liquor concentration information, the working time information of the target dyeing vat, the working temperature information, and the process curve template of the target color depth, a first color depth estimation information is determined; the current dye liquor concentration information is obtained through an online spectrophotometer; a second color depth estimation information is determined based on the initial dye liquor concentration information, the current dye liquor concentration information, the target fabric type information, and the shape parameter information of the target fabric; the current color depth information is determined based on the first color depth estimation information and the second color depth estimation information; the working status of the textile printing and dyeing production equipment is controlled based on the current color depth information, the target color depth information, and the printing and dyeing stage information; The specific steps for constructing the process curve template for the target color depth include: acquiring the type information, shape parameter information, and target color number information of the experimental fabric, wherein the shape parameter information includes the weight information, thickness information, and width information of the fabric; conducting a standard dyeing experiment based on the experimental fabric and the target color number information, and collecting time-related experimental temperature data, experimental dye liquor concentration data, and experimental color depth information during the dyeing process; constructing a temperature-time change curve and a dye liquor concentration change curve based on the experimental temperature data and the experimental dye liquor concentration data; determining the target dye type based on the type information of the experimental fabric; determining the dyeing process evolution model for the target color depth based on the experimental dye liquor concentration data and the type information of the experimental fabric, wherein the dyeing process evolution model is a mathematical function describing the relationship between color depth and time; and using the temperature-time change curve, the dye liquor concentration change curve, and the dyeing process evolution model as the process curve template for the target color depth.

2. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 1, characterized in that, The step of determining the dyeing process evolution model for the target color depth based on the experimental dye concentration data and the type information of the experimental fabric includes: determining the target dye information based on the type information of the experimental fabric; and determining the dyeing process evolution model for the target color depth based on the target dye information and the experimental dye concentration data.

3. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 2, characterized in that, When the target dye information is a disperse dye, the process of determining the dyeing process evolution model of the target color depth includes: obtaining the maximum achievable color depth, the color depth growth rate constant, and time information; and determining the dyeing process evolution model of the target color depth based on the maximum achievable color depth, the color depth growth rate constant, and the time information.

4. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 2, characterized in that, When the target dye information is a reactive dye, the process of determining the dyeing process evolution model of the target color depth includes: obtaining the saturated adsorption amount, Langmuir adsorption constant, dye liquor concentration, and dyeing efficiency coefficient; determining the dye adsorption amount based on the saturated adsorption amount, the Langmuir adsorption constant, and the dye liquor concentration; and determining the dyeing process evolution model of the target color depth based on the dyeing efficiency coefficient and the dye adsorption amount.

5. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 1, characterized in that, The step of determining the second color depth estimation information based on the initial dye concentration information, the current dye concentration information, the target fabric type information, and the shape parameter information of the target fabric includes: calculating the current dye exhaustion rate based on the initial dye concentration information and the current dye concentration information; finding the corresponding dyeing efficiency coefficient and saturation adsorption amount based on the target fabric type; estimating the dye adsorption amount per unit area of ​​fabric based on the dye exhaustion rate, the dyeing efficiency coefficient, and the saturation adsorption amount; and mapping the dye adsorption amount per unit area of ​​fabric to the corresponding color depth value as the second color depth estimation information.

6. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 5, characterized in that, The step of determining the current color depth information based on the first color depth estimation information and the second color depth estimation information includes: calculating the absolute value of the color depth deviation based on the first color depth estimation information and the second color depth estimation information; determining dynamic weight information based on the absolute value of the color depth deviation, empirical deviation tolerance, and sensitivity factor; and determining the current color depth information based on the dynamic weight information, the first color depth estimation information, and the second color depth estimation information.

7. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 1, characterized in that, The step of controlling the working state of the textile printing and dyeing production equipment based on the current color depth information, target color depth information, and printing and dyeing stage information includes: determining the color depth deviation range based on the current color depth information and target color depth information; and controlling the working state of the textile printing and dyeing production equipment based on the color depth deviation range and printing and dyeing stage information.

8. The intelligent monitoring method for textile printing and dyeing production equipment according to claim 7, characterized in that, The method of controlling the working state of the textile printing and dyeing production equipment based on the color depth deviation amplitude and the printing and dyeing stage information includes: when the printing and dyeing stage information indicates a heating stage, if the color depth deviation amplitude exceeds a first preset threshold, controlling the equipment to increase the heating rate or extend the heating time; if the color depth deviation amplitude is lower than a second preset threshold, controlling the equipment to decrease the heating rate or delay the heating start, wherein the first preset threshold is greater than the second preset threshold; and / or, when the printing and dyeing stage information indicates a heat preservation stage, if the color depth deviation amplitude exceeds the first preset threshold, controlling the equipment to extend the heat preservation time or accelerate the dye liquor circulation; if the color depth deviation amplitude is lower than the second preset threshold, controlling the equipment to shorten the heat preservation time or reduce the liquor ratio; and / or, when the printing and dyeing stage information indicates a slow cooling stage, if the color depth deviation amplitude exceeds the first preset threshold, controlling the equipment to activate the re-dyeing cycle process or increase the circulation temperature; if the color depth deviation amplitude is lower than the second preset threshold, controlling the equipment to accelerate the cooling rate and start the dye reduction device.

9. An intelligent monitoring system for textile printing and dyeing production equipment, used to execute the intelligent monitoring method for textile printing and dyeing production equipment according to any one of claims 1 to 8, characterized in that, include: The first acquisition unit is used to acquire information about the target fabric type, the shape parameters of the target fabric, and the initial dye concentration of the target dye vat. The second acquisition unit is used to acquire the working time information, working temperature information, and process curve template of the target dyeing vat for the target color depth. The first determining unit is used to determine the first color depth estimation information based on the initial dye liquor concentration information, the working time information of the target dyeing vat, the working temperature information, and the process curve template of the target color depth; The third acquisition unit is used to acquire the current dye concentration information through an online spectrophotometer; The second determining unit is used to determine the second color depth estimation information based on the initial dye concentration information, the current dye concentration information, the target fabric type information, and the shape parameter information of the target fabric. The third determining information is used to determine the current color depth information based on the first color depth estimation information and the second color depth estimation information; the control unit is used to control the working status of the textile printing and dyeing production equipment based on the current color depth information, the target color depth information and the printing and dyeing stage information.

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