Method and system for automatically adjusting conveying parameters of auxiliaries for yarn dyeing

Abstract

The invention provides an auxiliary conveying parameter automatic adjusting method and system for yarn dyeing, and relates to the technical field of yarn dyeing, and the auxiliary conveying parameter automatic adjusting method comprises the steps that a dyeing process parameter template is configured; initializing dyeing equipment; activating a monitoring sensor in the dyeing equipment, and collecting yarn data; in-tank flow analysis is carried out on the dyeing tanks, region segmentation results are established, and linkage sensors are configured respectively; establishing a response parameter standard by using real-time yarn data and a dyeing process parameter template; and generating a time sequence micro-assistant release strategy, and automatically adjusting assistant conveying parameters. The technical problem that in the prior art, due to the fact that flowing in the dyeing tank is uneven, the auxiliaries are excessive or insufficient, and the control precision of the conveying parameters of the auxiliaries for yarn dyeing is further affected can be solved, and the control precision of the conveying parameters of the auxiliaries is improved by automatically adjusting the conveying parameters of the auxiliaries through flowing analysis and area segmentation.

Description

Technical Field

[0001] The present application relates to the technical field of yarn dyeing, and in particular to a method and system for automatically adjusting auxiliary agent delivery parameters for yarn dyeing. Background Art

[0002] The delivery of auxiliaries in yarn dyeing is an important link in the entire dyeing process, which directly affects the dyeing effect and yarn quality. In the process of yarn dyeing, auxiliaries usually include dyeing auxiliaries, dispersants, regulators, etc., which are injected into the dyeing tank through specific delivery equipment, and act on the yarn after being fully mixed with the dye solution in the tank. The accuracy of auxiliaries delivery directly affects the dyeing effect. It is necessary not only to control the delivery amount, but also to ensure the uniform distribution of auxiliaries in the dyeing tank. However, during the dyeing process, affected by factors such as the flow state of the fluid in the tank, the flow of the dye solution is uneven, resulting in uneven distribution of dyes and auxiliaries, which in turn affects the dyeing effect. Excessive auxiliaries in local areas may cause uneven dyeing or waste; while areas with insufficient auxiliaries may result in insufficient dyeing depth or color deviation.

[0003] In summary, the prior art has a technical problem that uneven flow in the dyeing tank leads to excess or insufficient auxiliary agents, which further affects the control accuracy of auxiliary agent delivery parameters for yarn dyeing. Summary of the invention

[0004] The purpose of this application is to provide a method and system for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing, so as to solve the technical problem in the prior art that uneven flow in the dyeing tank leads to excessive or insufficient auxiliary agents, further affecting the control accuracy of the auxiliary agent delivery parameters for yarn dyeing.

[0005] In view of the above problems, the present application provides a method and system for automatically adjusting auxiliary agent delivery parameters for yarn dyeing.

[0006] In the first aspect, the present application provides an automatic adjustment method for auxiliary agent delivery parameters for yarn dyeing, which is implemented by an automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing, wherein the automatic adjustment method for auxiliary agent delivery parameters for yarn dyeing includes: after reading the input yarn material, target color number and process requirements, calling the database to configure the dyeing process parameter template; after initializing the dyeing equipment using the dyeing process parameter template, performing yarn dyeing based on the dyeing equipment; activating the monitoring sensor in the dyeing equipment, using the monitoring sensor to collect yarn data, and establishing real-time yarn data; performing in-tank flow analysis on the dyeing tank, establishing regional segmentation results, configuring linkage sensors for the regional segmentation results, performing temperature, pH and concentration monitoring, and establishing a linkage data set; establishing a response parameter standard using the yarn real-time data and the dyeing process parameter template; generating a time-series micro-auxiliary agent release strategy according to the response parameter standard and the linkage data set, and automatically adjusting the auxiliary agent delivery parameters according to the time-series micro-auxiliary agent release strategy.

[0007] In the second aspect, the present application also provides an automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing, which is used to execute the automatic adjustment method for auxiliary agent delivery parameters for yarn dyeing as described in the first aspect, wherein the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing includes: a process parameter configuration module, the process parameter configuration module is used to read the input yarn material, target color number and process requirements, and then call the database to configure the dyeing process parameter template; an equipment initialization module, the equipment initialization module is used to initialize the dyeing equipment using the dyeing process parameter template, and then perform yarn dyeing based on the dyeing equipment; a real-time data acquisition module, the real-time data acquisition module is used to activate the dyeing equipment in the dyeing equipment A monitoring sensor is used to collect yarn data and establish real-time yarn data; a region segmentation module is used to analyze the flow in the dyeing tank, establish region segmentation results, configure linkage sensors for the region segmentation results, perform temperature, pH and concentration monitoring, and establish a linkage data set; a parameter standard establishment module is used to establish a response parameter standard using the yarn real-time data and the dyeing process parameter template; a strategy generation module is used to generate a time-series micro-additive release strategy according to the response parameter standard and the linkage data set, and automatically adjust the additive delivery parameters according to the time-series micro-additive release strategy.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] After reading the input yarn material, target color number and process requirements, the database is called to configure the dyeing process parameter template; after the dyeing equipment is initialized using the dyeing process parameter template, the yarn dyeing is performed based on the dyeing equipment; the monitoring sensor in the dyeing equipment is activated, and the monitoring sensor is used to collect yarn data to establish the real-time data of the yarn; the flow analysis in the dyeing tank is performed to establish the regional segmentation result, and the linkage sensor is configured for the regional segmentation result, and the temperature, PH and concentration monitoring are performed to establish the linkage data set; the response parameter standard is established using the real-time data of the yarn and the dyeing process parameter template; the time-series micro-auxiliary agent release strategy is generated according to the response parameter standard and the linkage data set, and the auxiliary agent delivery parameters are automatically adjusted according to the time-series micro-auxiliary agent release strategy. In other words, by calling the corresponding dyeing process parameter template, the dyeing equipment is initialized, the real-time data of the yarn is collected, the flow analysis in the dyeing tank is performed to establish the regional segmentation result, and the delivery of the auxiliary agent is accurately adjusted according to the actual needs of different areas in the dyeing tank, thereby improving the control accuracy of the auxiliary agent delivery parameters, thereby ensuring the color fastness and uniformity of the dyeing quality.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 This is a schematic flow chart of the method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing in the present application;

[0013] Figure 2 This is a schematic diagram of the structure of the automatic adjustment system for auxiliary agent delivery parameters used for yarn dyeing in this application.

[0014] Explanation of the reference numerals: process parameter configuration module 11 , equipment initialization module 12 , real-time data acquisition module 13 , area segmentation module 14 , parameter standard establishment module 15 , strategy generation module 16 . DETAILED DESCRIPTION

[0015] The present application solves the technical problem in the prior art that the uneven flow in the dyeing tank leads to excessive or insufficient auxiliary agents, which further affects the control accuracy of the auxiliary agent delivery parameters for yarn dyeing by providing an automatic adjustment method and system for auxiliary agent delivery parameters for yarn dyeing. By calling the corresponding dyeing process parameter template, the dyeing equipment is initialized, the real-time data of the yarn is collected, the flow in the dyeing tank is analyzed, and the regional segmentation results are established. According to the actual needs of different areas in the dyeing tank, the delivery of auxiliary agents is accurately adjusted, and the control accuracy of the auxiliary agent delivery parameters is improved, thereby ensuring the color fastness and uniformity of the dyeing quality.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides an automatic adjustment method for auxiliary agent delivery parameters for yarn dyeing, wherein the automatic adjustment method for auxiliary agent delivery parameters for yarn dyeing is performed by an automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing, and the automatic adjustment method for auxiliary agent delivery parameters for yarn dyeing specifically comprises the following steps:

[0018] S100: After reading the input yarn material, target color number and process requirements, call the database to configure the dyeing process parameter template.

[0019] S200: After the dyeing equipment is initialized using the dyeing process parameter template, yarn dyeing is performed based on the dyeing equipment.

[0020] Specifically, the input yarn material, target color number and process requirements are obtained, the database is automatically called, the dyeing process parameter template that matches the input conditions is retrieved, and the template is used as the parameter input for initializing the dyeing equipment for subsequent process execution. Yarn material refers to the raw material of the yarn, such as cotton, silk, polyester, etc. Different materials have different absorbency and reactivity to dyes, and have different adaptation requirements for dyeing auxiliaries, dye solution formulas, temperatures and process conditions. The target color number is the color standard that the yarn needs to achieve after dyeing, usually represented by an international color card (such as a Pantone color card) or a customer-defined color code. Each color corresponds to a specific dye formula and dyeing conditions. Process requirements refer to the specific technical specifications that need to be followed during the dyeing process, including dye solution temperature, auxiliary agent concentration, dyeing time, etc., which vary according to the material and color number.

[0021] The database stores structured data of dyeing process parameters, including the mapping relationship between different yarn materials, target color numbers, process requirements and dyeing process parameters. According to the index of key fields (such as material, color number, process requirements), the relevant dyeing process parameter templates are quickly matched in the database, including dye type, dosage, dye solution ratio, stirring speed, temperature, time, etc., to guide the execution of the dyeing process.

[0022] Input the various set values ​​of the dyeing process parameter template into the control system of the dyeing equipment for initialization. The dyeing equipment sets the automatic dye batching system, adjusts the dye liquor ratio, adjusts the dye bath temperature, pH value, stirring speed, etc. according to the input parameters. Use the initialized dyeing equipment to dye the yarn, including dye liquor injection, heating, auxiliary agent addition, stirring and dyeing time control. By calling the dyeing process parameter template in the database, the dyeing process parameters suitable for specific yarns and colors can be configured quickly and accurately, reducing the time and potential errors of manual parameter configuration, and improving the efficiency and consistency of the dyeing process.

[0023] S300: activating the monitoring sensor in the dyeing equipment, using the monitoring sensor to collect yarn data, and establishing real-time yarn data.

[0024] Specifically, sensors for monitoring the yarn status are arranged at key locations of the dyeing equipment (such as the inlet, dyeing trough, and outlet), and various sensors are activated to monitor the yarn status in real time, including yarn color, yarn moisture, diameter, conveying speed, tension, and fiber status. Monitoring sensors are special equipment for collecting yarn status, including optical sensors, humidity sensors, mechanical sensors, laser speed meters, etc. Dynamic monitoring of the yarn status through monitoring sensors helps to evaluate the effect and progress of yarn dyeing and avoid quality problems caused by delayed response.

[0025] S400: Perform flow analysis on the dyeing tank, establish regional segmentation results, configure linkage sensors for the regional segmentation results, perform temperature, pH and concentration monitoring, and establish a linkage data set.

[0026] Further, the present application S400 includes:

[0027] Flow rate sensors are used to perform multi-point monitoring of the dye solution to establish the flow rate distribution in the dyeing tank. The movement path and speed of the yarn in the dyeing tank are recorded by optical tracking to establish the yarn trajectory distribution. Position association clustering is performed in the dyeing tank based on the flow rate distribution and the yarn trajectory distribution, and the region segmentation result is established based on the association clustering result.

[0028] Specifically, flow sensors are arranged at different positions of the dyeing tank to measure the flow rate of the dye solution, such as the bottom, middle and surface of the tank. The flow rate sensors are used to monitor the flow rate of the dye solution at multiple points in the dyeing tank, and the flow rate data at each position is recorded to establish the flow rate distribution in the dyeing tank, that is, the flow rate difference distribution of the dye solution in different areas of the dyeing tank, which is used to analyze the flow uniformity of the dye solution. Optical equipment (such as high-speed cameras or laser sensors) is used to record the position and movement of objects (such as yarns), and by capturing real-time images of the yarns, its path and movement state in the dyeing tank are analyzed. Optical tracking is a non-contact measurement technology used to record the movement path and speed of the yarn in the dyeing tank. The yarn trajectory distribution is the movement path and speed distribution of the yarn in the dyeing tank, which is used to evaluate the contact of the yarn with the dye solution in different areas.

[0029] By monitoring the flow velocity distribution, the areas with uneven flow velocity in the dyeing tank are found. By optically tracking and recording the yarn trajectory, the contact of the yarn with the dye solution in different areas is clarified, and the yarn path design is optimized. Combining the flow velocity distribution and the yarn trajectory distribution, a clustering algorithm (such as K-means or DBSCAN) is used to perform position association analysis, and the flow velocity distribution and the yarn trajectory distribution are associated to classify the areas of the dyeing tank. The flow velocity and yarn trajectory data of the spatial points of the dyeing tank are used as feature inputs, and the clustering algorithm is executed to generate spatial area groupings. According to the clustering results, the dyeing tank is divided into several areas, and attribute labels are assigned to each area. Each area has similar motion and flow characteristics, and the regional segmentation results are established.

[0030] For each area in the regional segmentation result, a linkage sensor is configured. According to the characteristics of the dye solution, appropriate sensors are selected, including temperature sensors, pH sensors, optical or electrochemical concentration sensors, etc. Sensors are deployed in multiple areas to collect temperature, pH and concentration data in each area. The monitoring data of sensors in each area are integrated to generate a linkage data set containing time series, which includes real-time monitoring data of each area in the dyeing tank. For example, in the active area at the entrance, more sensors are configured to monitor the rapid changes in temperature and dye concentration; while in the stable area at the exit, fewer sensors are configured because the parameters in the stable area change slowly. Through the linkage sensor configuration of the regional segmentation result, dynamic monitoring of temperature, pH and concentration in different areas of the tank is realized, and accurate monitoring and control of different areas in the dyeing tank are achieved.

[0031] S500: Establishing a response parameter standard using the yarn real-time data and the dyeing process parameter template.

[0032] Furthermore, the present application S500 includes:

[0033] The real-time conveying speed of the yarn is obtained, and a response lag analysis is performed according to the real-time conveying speed to establish a first lag constraint; a historical conveying data set of the yarn is called, and a conveying stability prediction is performed according to the historical conveying data set, and a second lag constraint is established according to the conveying stability prediction; a lag compensation is generated using the first lag constraint and the second lag constraint, and the response parameter standard is established through the lag compensation.

[0034] Specifically, a set of parameter standards is established based on the yarn real-time data and the dyeing process parameter template to guide the adjustment and optimization of the dyeing process. The response parameter standard is a control standard determined based on the yarn real-time data and the dyeing process parameter template, which is used to guide the real-time adjustment of the conveying speed and other process parameters. The allowable adjustment range of each parameter is determined based on the yarn real-time data and the dyeing process parameter template, and the response parameter standard adapted to the current production line is established. The real-time conveying speed is obtained from the yarn real-time data, and the response lag analysis is performed on the real-time conveying speed to determine the time difference between the yarn conveying speed from the current state to the target state. During the dyeing process, there may be a lag between the setting of the target (such as the target conveying speed of the yarn) and the actual time achieved. The purpose of the response lag analysis is to determine the degree of lag and whether compensation is required. According to the results of the response lag analysis, a lag upper limit is set to limit the maximum allowable time of the equipment response speed to ensure that during the dyeing process, the adjustment of the yarn conveying will not lag too much, thereby affecting the dyeing quality.

[0035] Obtain the historical data set of yarn delivery, that is, retrieve all the yarn delivery speed data and other related parameters collected in the past, analyze the historical delivery data set, and use statistical analysis methods, machine learning models or regression analysis to predict the stability of yarn delivery in the future, including the expected delivery speed range and possible fluctuations. For example, a stable prediction model is constructed through a long short-term memory network, which is a deep learning model suitable for processing time series data and can capture long-term dependencies. Perform data preprocessing on the historical delivery data set and divide it into a training set and a test set. Usually 80% of the data is used for training and 20% of the data is used for verification and testing.

[0036] The data set is divided into multiple time windows through a sliding window to predict the data of the next time window. The model structure is constructed, including the input layer, multiple LSTM layers, and a fully connected layer. The training data is input into the model and the model parameters are adjusted until the error on the test set is minimized. According to the loss curve during the training process, the hyperparameters (such as the number of neurons in the LSTM layer, the learning rate, etc.) are adjusted to optimize the performance of the model. After the training is completed, the prediction ability of the model is evaluated using the test set, and evaluation indicators such as the mean square error (MSE) are calculated to understand the prediction effect of the model. The actual value is compared with the predicted value to determine the prediction accuracy of the model. Once the model is trained and evaluated, it can be used to predict future delivery speeds.

[0037] The data of the current time window of the historical transport data set is input into the model for stable prediction, and the future transport speed changes are predicted. According to the fluctuation range of the prediction results, the second hysteresis constraint is set. The second hysteresis constraint takes into account the influence of historical data and prediction errors, and is usually set according to the standard deviation or mean square error (MSE) of the prediction. The second hysteresis constraint usually focuses more on long-term stability than the first hysteresis constraint to avoid dyeing quality problems caused by unstable transport speed.

[0038] Combined with the first hysteresis constraint and the second hysteresis constraint, the standard parameters generated by hysteresis compensation are used as the control standard to guide the yarn delivery during the dyeing process. The first hysteresis constraint and the second hysteresis constraint are combined to generate hysteresis compensation, including adjusting parameters such as delivery speed, temperature setting, pH value, etc. Based on the results of hysteresis compensation, the expected response range is determined, taking into account the effects of hysteresis effect, error correction and stability prediction, which is used to guide the dyeing process to ensure that the dyeing quality remains consistent even when the yarn delivery speed changes. By generating hysteresis compensation and establishing response parameter standards, it is ensured that the dyeing process can adapt to changes in yarn delivery speed, which helps to improve the consistency of dyeing quality and reduce scrap rate.

[0039] S600: generating a time-sequential micro-additive release strategy according to the response parameter standard and the linkage data set, and automatically adjusting additive delivery parameters according to the time-sequential micro-additive release strategy.

[0040] Furthermore, the present application S600 includes:

[0041] The additive release response accuracy of the yarn is obtained, and the Wiener release point is configured according to the additive release response accuracy and the regional segmentation result; the response control channel is initialized using the Wiener release point; the response parameter standard and the linkage data set are sent to the initialized response control channel to generate a time-sequential micro-additive release strategy, which includes the selection of the Wiener release point, the control time and the release strategy.

[0042] Specifically, in order to ensure the accurate release of additives during the dyeing process, it is necessary to obtain the additive release response accuracy of the yarn, which is achieved by detecting the release rate, concentration fluctuations and control errors of the additives. High response accuracy means that the release of additives can accurately follow the needs of the dyeing equipment to avoid excess or insufficient amounts. According to the additive release response accuracy, combined with the flow velocity distribution and area segmentation results in the dyeing tank, control theory algorithms (such as Wiener filter design) are used to determine the optimal additive release point. Wiener filters are often used to estimate the true value of a signal in a noisy environment. The Wiener release point refers to the optimal time and location for additive release determined by Wiener filtering to ensure accurate additive release and reduce lag or errors.

[0043] Wiener filtering can accurately predict the optimal additive release position and time in different regions to minimize the concentration change and error in the dyeing tank. Wiener filtering is a classic signal processing algorithm, which is mainly used to predict or recover signals in noisy signals. According to the correlation between the input signal and the expected signal, an optimized filtering result is generated to minimize the prediction error. The flow velocity distribution in the dyeing tank, the yarn trajectory distribution, and the real-time data of the additive release response accuracy in the regional segmentation results are input into the Wiener filter to determine the parameters of the Wiener filter, including the gain and cutoff frequency of the filter. The gain determines the degree of signal amplification of the filter, while the cutoff frequency determines the signal frequency range allowed by the filter. Flow velocity fluctuations and instability of the additive release rate will introduce noise. By analyzing historical data, the noise characteristics (such as variance, mean) are estimated. According to the data characteristics in each region (such as flow velocity, trajectory density, etc.), the weight coefficient of Wiener filtering is calculated. According to the weight calculated by Wiener filtering, the time and position of the additive release are optimized, and the optimal additive release position and time are accurately predicted in different regions to minimize the concentration change and error in the dyeing tank.

[0044] After configuring the Wiener release point, use the release point to initialize the response control channel, which is used to control the channel for additive release. Each channel corresponds to a Wiener release point. The response control channel receives and processes the Wiener release point information to control the time, position and amount of additive release. The response parameter standard and linkage data set are sent to the initialized response control channel to generate a time-series micro-additive release strategy. The time-series micro-additive release strategy determines how much additive to release at each time point based on the data set and standard. It usually includes the release time point, the quantified release amount and the control method to ensure the accurate release of the additive during the dyeing process. The release strategy will comprehensively consider factors such as the target color number, process requirements, and additive reaction of the dyeing to ensure the best dyeing effect. Through the configuration of the Wiener release point, the release time and position of the additive can be accurately controlled to ensure that the additive concentration in each area can meet the dyeing requirements, avoid excessive or insufficient additives, and thus improve the dyeing effect.

[0045] According to the response parameter standards established by the yarn real-time data and the dyeing process parameter template, the conditions for the delivery of additives are defined. The response parameter standards and the linkage data set are input into the initialized response control channel to generate the additive release strategy, including the selection of the Wiener release point, the control time and the release strategy. Using the time-series micro-additive release strategy, the additive delivery parameters are dynamically adjusted, including the delivery rate, delivery concentration, release time and position, etc., to ensure that the additive release meets the actual needs in the tank and improve the utilization rate of the additive. By dynamically adjusting the delivery rate, concentration and time, it is ensured that the additive release meets the actual needs in the tank and improves the utilization rate of the additive.

[0046] Furthermore, the present application also includes the following steps:

[0047] The dyeing effect of the yarn is monitored and feedback indicators are established, wherein the feedback indicators include a dyeing uniformity indicator and a dyeing color fastness indicator; and the feedback indicators are used to generate and optimize a time-sequential micro-auxiliary agent release strategy.

[0048] Specifically, the effect of yarn dyeing is monitored in real time, and performance data related to dyeing is collected. Use a colorimeter or a spectral camera to obtain the color data of the yarn and calculate the color deviation value. The dyed yarn is subjected to friction, washing or light tests to determine the color fastness. Based on the test results, quantitative indicators of dyeing uniformity and color fastness are established. The dyeing uniformity index is an indicator used to measure the color consistency of the dyeing area, usually expressed as a standard deviation or color deviation ΔE, which can be calculated by subtracting 1 from the ratio of the color deviation standard deviation and the average color value. The dyeing color fastness index is an indicator used to measure the color stability of the yarn under conditions such as friction, washing or light, and is usually expressed by a score (such as 1-5).

[0049] Modeling is performed based on the synergistic relationship of multiple parameters (such as flow rate parameters, pH value parameters, additive concentration parameters, and temperature parameters). By analyzing historical data and real-time monitoring data, a synergistic relationship model between these parameters is established. Based on the multi-parameter synergistic relationship model, dynamic synergistic weights are generated, which reflect the relative importance of different parameters in the dyeing process and change as the dyeing process proceeds. The dynamic synergistic weights and feedback indicators are used to optimize the time-sequential micro-additive release strategy to control the time and dosage of the additive release device. For example, when the ΔE value is high, it indicates uneven dyeing, and the additive release position or dosage should be adjusted; when the color fastness score is low, the additive ratio or release frequency should be optimized. The optimization of the time-sequential micro-additive release strategy refers to adjusting the time, dosage and position of the additive release to make the use of additives more accurate, maximize the dyeing effect and reduce resource waste. Strategy optimization based on feedback data can dynamically adapt to changes in production to ensure a continuous and stable dyeing process.

[0050] Furthermore, the present application also includes the following steps:

[0051] Execute multi-parameter synergistic relationship modeling, wherein the multi-parameter synergistic relationship modeling includes flow rate parameters, pH value parameters, additive concentration parameters, and temperature parameters; generate dynamic synergistic weights according to the multi-parameter synergistic relationship modeling; and use the dynamic synergistic weights and the feedback indicators to perform time-series micro-additive release strategy generation optimization.

[0052] Specifically, through multi-parameter modeling, the synergistic effects of key parameters (flow rate, pH value, auxiliary concentration, temperature) on dyeing quality are analyzed, including flow rate parameters, pH value parameters, auxiliary concentration parameters and temperature parameters. According to the characteristics of the parameters and the target effect, appropriate modeling methods are selected, such as regression analysis, random forest regression, deep learning models, etc. For example, the random forest algorithm is used to capture the nonlinear interactions between parameters, automatically select important features, capture complex synergistic relationships, and capture complex interactions by averaging the results of multiple decision trees. Analyze the impact of single parameters and the interaction of multiple parameters, and establish a synergistic relationship model between these parameters.

[0053] According to the results of multi-parameter synergistic relationship modeling, the contribution of each parameter to the dyeing effect is determined, and the dynamic influence weight is determined to reflect the importance of each parameter under different process conditions. The dynamic synergistic weight is calculated based on the results of multi-parameter synergistic relationship modeling. The dynamic influence weight of each parameter on the dyeing process will be updated with time or changes in process conditions. The dynamic synergistic weight and feedback index are used to optimize the time-series micro-additive release strategy. The dynamic synergistic weight and feedback index are input into the optimization model to adjust the additive release strategy. Prioritize the area corresponding to the parameter with high dynamic weight, and adjust the release point, time and concentration according to the feedback index.

[0054] A comprehensive optimization function with dyeing uniformity and color fastness as the goal is constructed, and the multi-parameter influence is mapped to the optimization goal using the synergistic weight. A new release strategy is generated according to the optimization goal, including release point selection, control time, and release strategy. Optimization algorithms (such as particle swarm optimization and genetic algorithm) are used for strategy optimization. The particle swarm is initialized, and each particle represents a set of strategy parameters. The strategy optimization is achieved by iteratively finding the optimal parameter configuration. For example, if the feedback indicator shows that the dyeing uniformity is poor, the release amount and timing of the auxiliary agent are adjusted according to the dynamic synergistic weight to improve the uniformity. Similarly, if the color fastness index is not up to standard, the dye formula or dyeing process parameters are adjusted to improve the color fastness. Multi-parameter synergistic relationship modeling helps to deeply understand the common influence of different process parameters on the dyeing effect. By optimizing the time-series micro-auxiliary release strategy, the dyeing uniformity and color fastness can be further improved, resource waste can be reduced, and the overall production efficiency and quality can be improved.

[0055] Furthermore, the present application also includes the following steps:

[0056] Establishing an abnormal warning threshold; performing warning trigger identification of the yarn real-time data and the linkage data set through the abnormal warning threshold, and establishing a warning trigger identification result; and issuing a pre-alarm according to the warning trigger identification result.

[0057] Specifically, the abnormal warning threshold is set according to the process requirements, safety standards or expert experience of the yarn, and the normal abnormal warning threshold is determined. When the monitored data exceeds the abnormal warning threshold, a warning signal is issued. The yarn monitoring sensor and linkage sensor are used to collect the real-time data and linkage data set of the yarn in real time. The real-time data and linkage data set are compared with the warning threshold. If the data exceeds the threshold range, an early warning is triggered. If a single parameter exceeds the threshold, a single parameter early warning is triggered; if multiple parameters exceed the threshold at the same time, a joint early warning (serious abnormality) is triggered. The specific parameters, time and location information that trigger the early warning are recorded to form an early warning record.

[0058] When the early warning is triggered, the operator is alerted through an alarm device (such as a buzzer, alarm light, SMS notification, etc.). According to the level of abnormality, the level of the early warning is determined, the abnormal parameters and the specific values ​​out of range are displayed on the control interface, and a sound alarm is issued, and an email or SMS is sent to notify the relevant person in charge. By analyzing the parameter deviation in real time, the abnormal behavior in the yarn dyeing process is quickly captured, and an early warning is issued when the problem just occurs to prevent the problem from further deteriorating.

[0059] In summary, the method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing provided in the present application has the following technical effects:

[0060] After reading the input yarn material, target color number and process requirements, the database is called to configure the dyeing process parameter template; after the dyeing equipment is initialized using the dyeing process parameter template, the yarn dyeing is performed based on the dyeing equipment; the monitoring sensor in the dyeing equipment is activated, and the monitoring sensor is used to collect yarn data to establish the real-time data of the yarn; the flow analysis in the dyeing tank is performed to establish the regional segmentation result, and the linkage sensor is configured for the regional segmentation result, and the temperature, PH and concentration monitoring are performed to establish the linkage data set; the response parameter standard is established using the real-time data of the yarn and the dyeing process parameter template; the time-series micro-auxiliary agent release strategy is generated according to the response parameter standard and the linkage data set, and the auxiliary agent delivery parameters are automatically adjusted according to the time-series micro-auxiliary agent release strategy. In other words, by calling the corresponding dyeing process parameter template, the dyeing equipment is initialized, the real-time data of the yarn is collected, the flow analysis in the dyeing tank is performed to establish the regional segmentation result, and the delivery of the auxiliary agent is accurately adjusted according to the actual needs of different areas in the dyeing tank, thereby improving the control accuracy of the auxiliary agent delivery parameters, thereby ensuring the color fastness and uniformity of the dyeing quality.

[0061] Embodiment 2, based on the same inventive concept as the method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing in the aforementioned embodiment 1, the present application also provides an automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing, please refer to the attached Figure 2 , the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing comprises:

[0062] A process parameter configuration module 11, wherein the process parameter configuration module 11 is used to read the input yarn material, target color number and process requirements, and then call the database to configure the dyeing process parameter template; an equipment initialization module 12, wherein the equipment initialization module 12 is used to initialize the dyeing equipment using the dyeing process parameter template, and then perform yarn dyeing based on the dyeing equipment; a real-time data acquisition module 13, wherein the real-time data acquisition module 13 is used to activate the monitoring sensor in the dyeing equipment, use the monitoring sensor to collect yarn data, and establish real-time yarn data; a region segmentation module 14, wherein the region segmentation module 14 is used to perform in-tank flow analysis on the dyeing tank, establish a region segmentation result, configure linkage sensors for the region segmentation results, perform temperature, pH and concentration monitoring, and establish a linkage data set; a parameter standard establishment module 15, wherein the parameter standard establishment module 15 is used to establish a response parameter standard using the yarn real-time data and the dyeing process parameter template; a strategy generation module 16, wherein the strategy generation module 16 is used to generate a time-series micro-additive release strategy according to the response parameter standard and the linkage data set, and automatically adjust the additive delivery parameters according to the time-series micro-additive release strategy.

[0063] Furthermore, the region segmentation module 14 in the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing is also used for:

[0064] The real-time conveying speed of the yarn is obtained, and a response lag analysis is performed according to the real-time conveying speed to establish a first lag constraint; a historical conveying data set of the yarn is called, and a conveying stability prediction is performed according to the historical conveying data set, and a second lag constraint is established according to the conveying stability prediction; a lag compensation is generated using the first lag constraint and the second lag constraint, and the response parameter standard is established through the lag compensation.

[0065] Furthermore, the parameter standard establishing module 15 in the auxiliary agent delivery parameter automatic adjustment system for yarn dyeing is also used for:

[0066] The real-time conveying speed of the yarn is obtained, and a response lag analysis is performed according to the real-time conveying speed to establish a first lag constraint; a historical conveying data set of the yarn is called, and a conveying stability prediction is performed according to the historical conveying data set, and a second lag constraint is established according to the conveying stability prediction; a lag compensation is generated using the first lag constraint and the second lag constraint, and the response parameter standard is established through the lag compensation.

[0067] Furthermore, the strategy generation module 16 in the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing is also used for:

[0068] The additive release response accuracy of the yarn is obtained, and the Wiener release point is configured according to the additive release response accuracy and the regional segmentation result; the response control channel is initialized using the Wiener release point; the response parameter standard and the linkage data set are sent to the initialized response control channel to generate a time-sequential micro-additive release strategy, which includes the selection of the Wiener release point, the control time and the release strategy.

[0069] Furthermore, the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing further comprises a dyeing effect monitoring module, which is used to:

[0070] The dyeing effect of the yarn is monitored and feedback indicators are established, wherein the feedback indicators include a dyeing uniformity indicator and a dyeing color fastness indicator; and the feedback indicators are used to generate and optimize a time-sequential micro-auxiliary agent release strategy.

[0071] Furthermore, the dyeing effect monitoring module in the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing is also used for:

[0072] Execute multi-parameter synergistic relationship modeling, wherein the multi-parameter synergistic relationship modeling includes flow rate parameters, pH value parameters, additive concentration parameters, and temperature parameters; generate dynamic synergistic weights according to the multi-parameter synergistic relationship modeling; and use the dynamic synergistic weights and the feedback indicators to perform time-series micro-additive release strategy generation optimization.

[0073] Furthermore, the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing further comprises an abnormal warning module, and the prediction and warning module is used to:

[0074] Establishing an abnormal warning threshold; performing warning trigger identification of the yarn real-time data and the linkage data set through the abnormal warning threshold, and establishing a warning trigger identification result; and issuing a pre-alarm according to the warning trigger identification result.

[0075] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The method and specific examples of the automatic adjustment of auxiliary agent delivery parameters for yarn dyeing in the first embodiment are also applicable to the automatic adjustment system of auxiliary agent delivery parameters for yarn dyeing in the present embodiment. Through the above detailed description of the automatic adjustment method of auxiliary agent delivery parameters for yarn dyeing, those skilled in the art can clearly know the automatic adjustment system of auxiliary agent delivery parameters for yarn dyeing in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0076] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for automatically adjusting the delivery parameters of auxiliary agents for yarn dyeing, characterized in that: include: After reading the input yarn material, target color number and process requirements, call the database to configure the dyeing process parameter template; After the dyeing equipment is initialized by using the dyeing process parameter template, yarn dyeing is performed based on the dyeing equipment; Activate the monitoring sensor in the dyeing equipment, use the monitoring sensor to collect yarn data, and establish real-time yarn data; Perform flow analysis on the dyeing tank, establish regional segmentation results, configure linkage sensors for the regional segmentation results, perform temperature, pH and concentration monitoring, and establish linkage data sets; Establishing a response parameter standard using the yarn real-time data and the dyeing process parameter template; A time-sequential micro-additive release strategy is generated according to the response parameter standard and the linkage data set, and the additive delivery parameters are automatically adjusted according to the time-sequential micro-additive release strategy.

2. The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to claim 1, characterized in that: The process of performing flow analysis in the dyeing tank and establishing a region segmentation result includes: Use flow rate sensors to monitor dye liquor at multiple points and establish flow rate distribution in the dyeing tank; The yarn trajectory distribution is established by optically tracking and recording the movement path and speed of the yarn in the dyeing tank; The position association clustering in the dyeing tank is performed according to the flow velocity distribution and the yarn trajectory distribution, and the region segmentation result is established according to the association clustering result.

3. The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to claim 2, characterized in that: The step of generating a time-sequential micro-adjuvant release strategy according to the response parameter standard and the linkage data set includes: Acquiring the additive release response accuracy of the yarn, and configuring the Wiener release point according to the additive release response accuracy and the region segmentation result; Initializing a response control channel using the Wiener release point; The response parameter standard and the linkage data set are sent to the initialized response control channel to generate a time-sequential micro-adjuvant release strategy, which includes the selection of Wiener release points, control time and release strategy.

4. The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to claim 1, characterized in that: The method of establishing a response parameter standard by using the yarn real-time data and the dyeing process parameter template includes: Acquiring a real-time conveying speed of the yarn, performing a response hysteresis analysis according to the real-time conveying speed, and establishing a first hysteresis constraint; Calling a historical delivery data set of the yarn, performing a delivery stability prediction according to the historical delivery data set, and establishing a second hysteresis constraint according to the delivery stability prediction; A hysteresis compensation is generated by using the first hysteresis constraint and the second hysteresis constraint, and the response parameter standard is established by using the hysteresis compensation.

5. The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to claim 1, characterized in that: The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing also includes: Monitoring the dyeing effect of the yarn and establishing feedback indicators, wherein the feedback indicators include a dyeing uniformity index and a dyeing color fastness index; The feedback index is used to generate and optimize the time-sequential micro-adjuvant release strategy.

6. The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to claim 5, characterized in that: The method of utilizing the feedback index to generate and optimize the time-sequential micro-adjuvant release strategy includes: Executing multi-parameter collaborative relationship modeling, wherein the multi-parameter collaborative relationship modeling includes flow rate parameters, pH value parameters, additive concentration parameters, and temperature parameters; Generate dynamic collaborative weights based on the multi-parameter collaborative relationship modeling; The dynamic synergy weight and the feedback index are used to perform optimization of the generation of a time-sequential micro-adjuvant release strategy.

7. The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to claim 1, characterized in that: The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing also includes: Establish abnormal warning thresholds; Performing warning trigger identification of the yarn real-time data and the linkage data set through the abnormal warning threshold, and establishing a warning trigger identification result; A pre-warning alarm is issued according to the pre-warning trigger identification result.

8. Automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing, characterized in that: The method for automatically adjusting the auxiliary agent delivery parameters for yarn dyeing according to any one of claims 1 to 7 is used to implement the steps, wherein the automatic adjustment system for auxiliary agent delivery parameters for yarn dyeing comprises: A process parameter configuration module, which is used to read the input yarn material, target color number and process requirements, and then call the database to configure the dyeing process parameter template; An equipment initialization module, wherein the equipment initialization module is used to initialize the dyeing equipment using the dyeing process parameter template and then perform yarn dyeing based on the dyeing equipment; A real-time data acquisition module, which is used to activate the monitoring sensor in the dyeing equipment, collect yarn data using the monitoring sensor, and establish real-time yarn data; A region segmentation module, wherein the region segmentation module is used to perform flow analysis in the dyeing tank, establish region segmentation results, configure linkage sensors for the region segmentation results, perform temperature, pH and concentration monitoring, and establish a linkage data set; A parameter standard establishing module, wherein the parameter standard establishing module is used to establish a response parameter standard using the yarn real-time data and the dyeing process parameter template; A strategy generation module is used to generate a time-sequential micro-adjuvant release strategy according to the response parameter standard and the linkage data set, and automatically adjust the adjuvant delivery parameters according to the time-sequential micro-adjuvant release strategy.

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