Textile dyeing production quality management system

Abstract

The present application relates to the field of textile printing and dyeing intelligent manufacturing and production quality control technology, in particular to a textile dyeing production quality management system; the system is communicatively connected with a dyeing equipment bottom controller, comprising a data sensing input module, a physical layer mapping processing module, a multi-dimensional state prediction module and a decision intervention management module; real-time process state data, optical deviation data and basic attribute constraint data are collected, microstructure damage index and dynamic environmental load cost data are calculated, a comprehensive health index and predicted state data are generated through a state simulation submodel and a health degree evaluation submodel, and threshold value is issued to continue to execute, degrade to receive or fuse alarm instructions, realizing comprehensive control of color quality, fiber performance and environmental cost.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and production quality control technology in textile printing and dyeing, specifically to a textile dyeing production quality management system. Background Technology

[0002] With the textile printing and dyeing industry continuously raising its requirements for product consistency, finished product physical properties, and environmental disposal, quality management in the dyeing production process is facing higher demands. Especially in the production scenario of multiple varieties, small batches, and high-standard orders running in parallel, how to ensure color compliance while also taking into account fabric feel, strength retention, and wastewater treatment load control has become an urgent problem to be solved in the dyeing production field. Traditional dyeing production quality management currently relies mainly on the following methods: color difference judgment based on online color measurement results, adjustment of temperature and holding time based on operational experience, and color correction control according to material feeding records; However, methods such as color difference judgment, experience-based adjustment of process parameters, and color correction control based on material input records all have certain drawbacks. For example, relying solely on the current color difference as the main basis can easily overlook the continuous damage to the fiber microstructure caused by high temperature, acid and alkali environments, and heat preservation accumulation. Relying on manual experience to adjust process parameters makes it difficult to simultaneously assess the relationship between subsequent color changes, physical property risks, and environmental load changes. When color correction control is based on material input records, it often only reflects the increase in reagents and cannot accurately characterize the wastewater treatment pressure and overall cost increase caused by repeated color correction. This can lead to problems in the dyeing process where the color is close to meeting the standard but the fabric performance declines, the environmental load increases, or the color difference improvement rate corresponding to continued color correction is lower than the preset threshold. Summary of the Invention

[0003] To solve the above-mentioned technical problems, the present invention provides a textile dyeing production quality management system. Specifically, the technical solution of the present invention includes: The data sensing input module is used to collect real-time process status data, optical deviation data, and basic attribute constraint data, including customer tolerance matrix data. The physical layer mapping processing module is used to calculate the microstructure damage index based on real-time process status data, and to calculate the dynamic environmental load cost data based on the chemical reagent data and basic attribute constraint data in the real-time process status data. The calculation uses the actual feed load represented by the chemical reagent data as the cost basis, and uses the basic attribute constraint data to interpret the boundary and acceptability constraints of the cost basis. The multi-dimensional state prediction module is used to input optical deviation data, microstructure damage index, and dynamic environmental load cost data into a preset prediction model to generate a comprehensive health index and predicted state data. The prediction model includes: a state simulation sub-model, used to extrapolate real-time process state data based on built-in candidate action data, outputting predicted state data containing predicted color difference data, predicted damage increment data, and predicted load increment data; and a health assessment sub-model, used to dynamically match and generate preset weight coefficients based on customer tolerance matrix data in basic attribute constraint data, and based on the preset weight coefficients, to perform weighted summation calculations on the predicted color difference data, predicted damage increment data, and predicted load increment data, outputting a comprehensive health index. The decision intervention management module is used to issue control instructions to the underlying controller based on the comprehensive health index. The control instructions include: if the comprehensive health index is greater than or equal to the first preset health threshold, generating and issuing a continue execution instruction; if it is less than the first preset health threshold but greater than or equal to the second preset health threshold, generating and issuing a downgrade reception instruction; if it is less than the second preset health threshold, generating and issuing a circuit breaker alarm instruction.

[0004] Optionally, the real-time process status data includes temperature curve data, pH data, heat preservation time data, and data on the chemical reagents already added; the optical deviation data includes current color space coordinate data and color difference limit data; and the basic attribute constraint data includes target object material data and customer tolerance matrix data.

[0005] Optionally, the physical layer mapping processing module includes a polymer fatigue integration unit; the polymer fatigue integration unit is used to perform integration calculations on the temperature curve data and the pH data using the heat preservation time data as the integration interval to generate the microstructure damage index; wherein, the integration calculation adopts a time axis based on the heat preservation time, and uses the empirical fatigue decay constant of a specific material at the current pH value as a benchmark, wherein the empirical fatigue decay constant is obtained by matching a pre-constructed material fatigue decay mapping relationship table, and the temperature difference exceeding the material safety tolerance threshold is continuously accumulated and weighted for time, and its specific conversion logic and constant matrix are stored as built-in expert system rules in the central control server connected to the system, thereby converting thermochemical stress into a quantifiable damage index.

[0006] Optionally, the physical layer mapping processing module further includes a cost accounting unit; the cost accounting unit is used to convert the data of the chemical reagents already applied into chemical oxygen demand contribution data, and to map the chemical oxygen demand contribution data into the dynamic environmental load cost data based on the unit processing cost data built into the system.

[0007] Optionally, the decision intervention management module is further configured to: in response to the generation of the circuit breaker alarm command, block the chemical reagent addition channel of the dyeing equipment through the underlying controller and lock the current temperature control parameters of the dyeing equipment; if an abnormal communication of the underlying controller is detected, resulting in the blocking command not being executed, send a secondary alarm to the workshop management terminal and the on-site audible and visual alarm connected to the system, and trigger a safety shutdown procedure.

[0008] Optionally, the decision intervention management module is further configured to: in response to the generation of the downgrade receiving instruction, compare the predicted state data with the customer tolerance matrix data to generate a target customer matching list.

[0009] Optionally, the system is also communicatively connected to a display terminal; the system further includes a quality cost traceability module; the quality cost traceability module is used to generate batch profit contribution dashboard data based on the dynamic environmental load cost data and the microstructure damage index, and output the batch profit contribution dashboard data to the display terminal.

[0010] Optionally, the multi-dimensional state prediction module is further configured to: feed back the predicted state data to the data sensing input module to update the real-time process state data and form a closed-loop control link.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. Existing technologies typically adjust processes based solely on current color differences, which can easily lead to internal fiber damage or overload of downstream wastewater treatment. This system, through a data sensing input module and a physical layer mapping processing module, goes beyond superficial optical deviation data. Instead, it calculates real-time process status data into a microstructure damage index using a polymer fatigue integral unit, and maps data on added chemical agents into chemical oxygen demand (COD) contribution data using a cost accounting unit. Combined with the accounting boundary and acceptability constraints given by the basic attribute constraint data, it calculates dynamic environmental load cost data. With the comprehensive health index generated by the multi-dimensional state prediction module, the system can simultaneously anticipate color changes, physical performance risks, and environmental costs during the color correction process, effectively avoiding the problem of excessive color correction leading to color compliance but fabric failure or uncontrolled pollution load.

[0012] 2. Traditional color correction control lacks equipment-level hard constraints and flexible degradation processing mechanisms. This system's decision intervention management module issues instructions based on a comprehensive health index: when the index deteriorates and triggers a circuit breaker alarm, the system directly blocks the chemical agent addition channel and locks the current temperature control parameters through the underlying controller, transforming the warning into equipment-level mandatory execution, effectively preventing irreversible damage amplification caused by on-site human error. When a batch is in a quality load imbalance zone and triggers a degradation acceptance instruction, the system no longer uses the original order's extreme parameters as the control target, but compares the predicted status data with the customer tolerance matrix data and generates a target customer matching list. This accurately matches acceptable secondary customers or grade requirements for fabrics where color correction is to be stopped, avoiding serious waste of energy and materials caused by scrapping the entire batch.

[0013] 3. Existing manual experience-based interventions are prone to state fragmentation and control lag. This system, on the one hand, feeds back predicted state data to the data sensing input module to update real-time process state data, forming a closed-loop control link. This allows the system to continuously verify the consistency between predictions and actual conditions throughout the continuous production cycle and correct control deviations in a timely manner. On the other hand, through the quality cost traceability module, it integrates in-depth dynamic environmental load cost data with microstructure damage index to generate batch profit contribution dashboard data and output it on the display terminal. This mechanism transforms potential environmental loads and quality losses in the production process into intuitive quantitative data, providing a reliable data traceability system and improvement basis for enterprises to subsequently optimize formulation processes and adjust order acceptance constraints. Attached Figure Description

[0014] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0016] like Figure 1 As shown, a textile dyeing production quality management system is provided. The system is communicatively connected to the underlying controller of the dyeing equipment. The system includes: The data sensing input module is used to collect real-time process status data, optical deviation data, and basic attribute constraint data, including customer tolerance matrix data. The physical layer mapping processing module is used to calculate the microstructure damage index based on real-time process status data, and to calculate the dynamic environmental load cost data based on the chemical reagent data and basic attribute constraint data in the real-time process status data. The calculation uses the actual feed load represented by the chemical reagent data as the cost basis, and uses the basic attribute constraint data to interpret the boundary and acceptability constraints of the cost basis. The multi-dimensional state prediction module is used to input optical deviation data, microstructure damage index, and dynamic environmental load cost data into a preset prediction model to generate a comprehensive health index and predicted state data. The prediction model includes: a state simulation sub-model, which extrapolates real-time process state data based on built-in candidate action data and outputs predicted state data containing predicted color difference data, predicted damage increment data, and predicted load increment data; and a health assessment sub-model, which dynamically matches and generates preset weight coefficients based on customer tolerance matrix data in the basic attribute constraint data, and performs weighted summation calculations on the predicted color difference data, predicted damage increment data, and predicted load increment data based on these preset weight coefficients to output a comprehensive health index. The decision intervention management module is used to issue control instructions to the underlying controller based on the comprehensive health index. The control instructions include: if the comprehensive health index is greater than or equal to the first preset health threshold, generating and issuing a continue execution instruction; if it is less than the first preset health threshold but greater than or equal to the second preset health threshold, generating and issuing a downgrade reception instruction; if it is less than the second preset health threshold, generating and issuing a circuit breaker alarm instruction.

[0017] This embodiment provides a quality management mechanism for textile dyeing production. Specifically, the system is deployed on the central control server in the dyeing workshop and is connected to the bottom controller of the high-temperature and high-pressure dyeing vat, the online spectrophotometer terminal, the feeding and metering unit, and the workshop management terminal. It is used to continuously manage the quality of the same dyeing batch throughout the entire dyeing cycle. For ease of explanation, the following implementation methods all revolve around the same main scenario: A dyeing factory receives an order for a batch of dark navy blue knitted fabric for sportswear. The main body of the fabric is a blend of cotton and modal. The customer has high requirements for color consistency, but also requires the finished product to maintain a soft hand feel and qualified breaking strength. In addition, the supply chain assessment includes wastewater treatment cost constraints. The data sensing input module collects three types of information from the workshop: The first type is real-time process status data reflecting the operating status of the dyeing vat. This type of data corresponds to the current thermal, chemical, and temporal environment of the fiber, reflecting the stress history that the fiber continuously endures in the dye bath; The second type is optical deviation data, which corresponds to the degree of deviation between the current fabric sample color and the standard sample. Essentially, it reflects the result of dye adsorption and dyeing uniformity in terms of apparent color; The third type is basic attribute constraint data, which corresponds to order boundaries, material tolerance, and customer acceptance boundaries, used to prevent the system from making control decisions based solely on the single objective of color. After collecting the above data, the physical layer mapping processing module does not directly stop at the single apparent attribute judgment of whether the color difference is qualified. Instead, it further maps the process state to the microstructure damage index and maps the information related to feeding and treatment load to dynamic environmental load cost data. The microstructure damage index here is used as a specific quantitative parameter to characterize the cumulative fatigue degree experienced by the fiber molecular chain under high temperature, acid and alkali and heat preservation. For fibers such as cotton and modal, if they are exposed to high temperature or unsuitable acid and alkali environment for a long time, the polymer chains inside the fiber will undergo hydrolysis, degradation or destruction of crystallization zone. This will manifest as a decrease in strength, hardening of the hand feel or increased risk of pilling in the finished product. The dynamic environmental load cost data corresponds to the pressure of subsequent wastewater treatment and compliance disposal for this batch. Especially in the case of multiple color corrections and repeated chemical additions, the improvement of color difference may not be proportional to the pollution load. The common situation is that the improvement in color becomes smaller and smaller, while the treatment load continues to rise. Furthermore, to make the data source relationships clearer in this embodiment, dynamic environmental load cost data is calculated based on basic attribute constraint data. In this embodiment, this means that the basic attribute constraint data is used to provide the accounting boundary, acceptance limit, and warning scope of the environmental cost for this batch, such as whether the customer is sensitive to green labels, whether the order sets a single batch environmental cost limit, and whether different materials correspond to different post-processing warning lines; while the pollution load directly related to material feeding can be provided by the reagent dosing records in the real-time process status data. In other words, dynamic environmental load cost data is not determined by a single type of input in isolation. Instead, it is based on the actual load generated by material input, and then the basic attribute constraint data is used to interpret the boundary of this cost base, amplify the risk, or impose acceptability constraints. This ensures that the subsequent cost evaluation reflects the actual material input pressure on site and also meets the business constraints of orders and customers. After this processing, the overall input-output relationship in the embodiment and the detailed accounting caliber in the subsequent subordinate embodiments can remain consistent under the same data link; the multi-dimensional state prediction module receives the current color difference, microstructure damage index and dynamic environmental load cost data on this basis, and calls the preset prediction model to generate predicted state data and comprehensive health index; among them, the state simulation sub-model is used to perform prospective simulation of candidate actions; the candidate actions can be common workshop control actions such as continuing the current heat preservation, adding a small amount of dye, adding leveling agent, terminating color repair and removing from the vat; the system does not only determine which action is most likely to reduce the current color difference, but also predicts the subsequent impact of the action on color, fiber damage and environmental cost at the same time; For example, adding dye may help reduce color difference in the short term, but if there is already a high accumulation of ineffective chemicals in the dye bath, the dyeing efficiency of the additional dye will drop below the preset efficiency threshold. In this case, continuing to correct the color often means that the fiber continues to be exposed to heat and chemical environment, and the color difference improvement is not sufficient. The health assessment sub-model will comprehensively evaluate the predicted color difference data, predicted damage increment data, and predicted load increment data to output a comprehensive health index. This index can be understood as the overall health of the current batch between continuing to correct the color and maintaining the overall delivery quality. The process can be illustrated using a simplified data flow example. Assume that the system receives three candidate actions at a certain moment, denoted as the first candidate action, the second candidate action, and the third candidate action. The first candidate action indicates continued heat preservation, the second candidate action indicates the addition of color-correcting additives, and the third candidate action indicates stopping color correction and preparing to leave the vat. The state simulation sub-model generates three sets of prediction results for these three actions: the color difference improvement corresponding to the first candidate action is within the first preset range, but the damage increment is greater than the first increment threshold, and the cost increment is within the second preset range. The second candidate action corresponds to a color difference improvement that reaches the second preset range, but the cost increase is too large and the damage continues to accumulate; the third candidate action corresponds to a color difference that remains unchanged, a damage increase that is lower than the preset safety lower limit, and a cost increase that is the minimum among all candidate actions; the health assessment sub-model gives three health results based on this and selects the control conclusion corresponding to the action with the highest health assessment value; the focus of this deduction is to show the process of data flow from the current state to the candidate action simulation and then to the comprehensive evaluation, which aims to reflect the data processing logic of multi-dimensional state prediction; Furthermore, to avoid the misinterpretation that multiple candidate actions corresponding to multiple health results and the output comprehensive health index in the embodiment are two data objects at different levels, the following unified approach can be adopted in this embodiment: the state simulation sub-model first outputs a set of predicted state data for each candidate action, and the health assessment sub-model then calculates the corresponding candidate action health result for each set of predicted state data. The multidimensional state prediction module determines a target action from all candidate action health results according to a preset selection rule, and outputs the health result corresponding to the target action as the comprehensive health index at the current moment to the decision intervention management module. The preset selection rule can be to prioritize the candidate action with the highest health result. When the health results of multiple candidate actions are equal or the difference is less than the preset comparison threshold, the candidate action with smaller fiber damage increment and lower prediction cost increment is prioritized. Therefore, at the module output level, the system always maintains a consistent relationship between a current moment, a comprehensive health index, a target action, and a control conclusion to be issued. Multiple intermediate health results at the candidate action level are only used as internal comparison bases for forming the comprehensive health index. The decision intervention management module issues control instructions in stages according to the comprehensive health index. If the comprehensive health index is higher than the first preset health threshold, it indicates that the current batch still maintains a good balance between color, fiber tolerance, and environmental cost. At this time, a continue execution instruction is issued, and the underlying controller continues to run according to the original process. If the index is between two thresholds, it indicates that the batch has entered the quality load imbalance zone. That is, continuing to pursue smaller color differences may cause physical performance indicators to exceed the preset decay threshold or environmental costs. At this time, a downgrade receiving instruction is issued to leave room for decision-making for subsequent customer matching or grade adjustment. If the index is below the second preset health threshold, it indicates that continuing to correct the color has been significantly detrimental to the overall quality and environmental indicators of the batch. The system generates a circuit breaker alarm instruction to prevent further high-risk operations. Furthermore, although the aforementioned downgrade receiving instruction carries the meaning of business processing, it does not exist independently from the equipment side in terms of system linkage. On the one hand, the instruction can be sent to the workshop management terminal to trigger the subsequent customer matching and level adjustment process; on the other hand, it can also be simultaneously sent to the underlying controller to execute and stop color correction operations that exceed the preset intervention intensity, maintain the current safe process status, and prepare for the corresponding equipment actions of the transition to the cylinder discharge or conservative finishing steps. In other words, in this embodiment, the downgraded reception instruction does not mean that the controller directly completes the production-level control decision. Rather, it means that the controller stops further color tracking operations and switches the equipment status to a conservative operation mode that is compatible with the downgraded reception handling. This avoids the disconnect where the business side suggests stopping color tracking, but the equipment side continues to operate at high risk along the original path. In the fault tolerance mechanism, if the online color measurement terminal is temporarily offline, the system can use the most recent valid color measurement result and the trend of process status changes to make a compensation judgment based on a preset time window. However, this compensation is only allowed to maintain conservative operation and is not allowed to be used as a basis for continuing to add reagents beyond the preset dosage limit. If there are local missing data in the process status data, such as short-term drift of the pH probe, the system marks the data as low confidence and increases the damage risk weight to avoid making overly aggressive color correction instructions when the information is incomplete; if the health results of each candidate action are similar, the system prioritizes the action that is more conservative to the fiber and the environment to prevent irreversible damage from occurring in working conditions close to the boundary. When the aforementioned dark navy blue knitted fabric order entered the final color correction stage, online measurements showed that there was still a deviation between the current fabric sample and the standard sample that was less than the preset first color difference tolerance value. The workshop operator initially tended to continue adding chemicals and extend the heat preservation time. Simultaneously, the system detected that the dyeing vat had been running in the high-temperature zone for more than the preset exposure time threshold, and two color correction feeds had already occurred. The microstructure damage index showed an increase exceeding the preset slope, and the dynamic environmental load cost also showed an increase exceeding the preset load threshold. After state simulation, the system determined that if color correction continued, although the apparent color might get closer to the standard sample, the fabric strength and hand feel risks would continue to increase. Therefore, it concluded that the overall health had decreased, and output the corresponding instructions in the "continue execution," "downgrade reception," or "fuse alarm" based on the results. The purpose of this step is to transform the traditional dyeing management method that only focuses on meeting color difference standards into a comprehensive decision-making mechanism that takes into account color quality, fiber physical properties, and environmental disposal costs. This will enable the early identification and control of high-risk color correction behaviors and reduce potential defective products with accurate colors but unsatisfactory physical properties of the fabric. In this embodiment, the real-time process status data includes temperature curve data, pH data, heat preservation time data, and data on the chemical reagents already added; the optical deviation data includes current color space coordinate data and color difference limit data; and the basic attribute constraint data includes target object material data and customer tolerance matrix data.

[0018] This embodiment provides a data refinement acquisition mechanism for dyeing batches. Specifically, under the aforementioned overall management framework, if the judgment is based solely on the current color difference value and whether to continue color correction, it is easy to overlook the differences in the tolerance of different materials to temperature and acid / alkali environments, and it is also impossible to distinguish the actual subsequent application impact of the same color difference deviation in different customer scenarios. Therefore, this embodiment further limits the composition of the input data, so that the subsequent risk identification has a clear physical orientation and business boundary. The following is a detailed explanation: the temperature curve data in the real-time process status data is not the temperature at a single moment, but the temperature trajectory of the dyeing vat from heating, holding, to possible re-coloring; for fibers, the effect of temperature has obvious time cumulative characteristics: short-term high temperature may not cause serious damage, but long-term exposure to high temperature plateau will cause continuous disturbance to the internal structure of the fiber; pH data reflects the parameter fluctuation range of the chemical environment of the dye bath, especially after stripping, color repair, or the addition of different auxiliaries, pH fluctuations will change the charge state of the fiber surface, the diffusion behavior of dye, and the stability of fiber molecular chains; The heat preservation time data is used to characterize the length of time the fiber is continuously exposed to a given thermochemical environment, which is directly related to whether the damage has moved from the reversible stage to the irreversible stage; the data on the chemical agents already applied is used to characterize the material load that has accumulated in the dye bath, which affects both the subsequent dyeing efficiency and the wastewater post-treatment pressure; the current color space coordinate data in the optical deviation data is used to describe the current color position of the fabric sample, and the color difference limit data is used to describe the acceptable boundary of the order; The significance of using these two together is that the system can not only quantify the specific degree of color difference deviation, but also determine whether the deviation exceeds the preset constraint boundary; the target object material data in the basic attribute constraint data is used to define the tolerance of the fiber itself. For example, cotton, modal, polyester or their blends have different tolerance to high temperature, acid and alkali and the number of times color correction is repeated; the customer tolerance matrix data is used to define the acceptance boundary of subsequent processes. It can be understood as a multi-dimensional acceptance rule table, reflecting the tolerance of different customers to factors such as color difference, hand feel, strength, and environmental label. To illustrate the data structure flow, a simplified example can be used: the system records the same batch as target data packets. The process subset of the target data packet includes the first temperature curve P, the first pH sequence T, the first heat preservation time H, the first reagent dosing record D, the first current chromaticity coordinate C, and the first color difference limit L; the constraint subset includes the first material identifier M and the first customer tolerance matrix R. After reading the target data packet, the system does not process the above parameters in isolation, but inputs them into subsequent modules according to the three-layer relationship of process history—current color—receiving boundary. For example, when the material identifier M shows cotton / modal blend and the customer tolerance matrix R indicates that a customer is more sensitive to the decrease in feel, even if the current color difference has not yet fully met the standard, the system will increase the caution of high-temperature delayed color correction. Furthermore, the letters P, T, H, D, C, L, E, M, and R mentioned above are used only as data item identifiers in this simplified example, and their meanings are based on the corresponding text mentioned above. If the same letters are mentioned again in the following text, they refer to the same defined object, not newly added undefined variables. In the fault tolerance mechanism, if the material data of the target object is missing, the system can call the internal material archive of the enterprise for supplementary matching. If no specific material can be matched, a more conservative tolerance parameter is adopted to prevent sensitive fibers from being mistakenly treated as high-temperature resistant materials. If the customer tolerance matrix has not yet been entered, the system can first adopt the default order level strategy, allowing only limited adjustments near the color difference limit and disallowing direct triggering of downgrade acceptance recommendations, in order to avoid giving inappropriate handling suggestions when the business boundaries are unclear; if the temperature curve sampling interval is uneven, the system will reconstruct continuous process segments according to the time axis in subsequent processing to prevent high temperature exposure from being underestimated. In the aforementioned dark navy blue knitted fabric order, the system collected temperature curves showing that the dyeing vat had entered the high-temperature holding stage twice, and the pH level shifted after the second color correction. The added chemicals included reactive dyes, leveling agents, and color-fixing auxiliaries. The online color measurement terminal indicated that the current fabric sample's chromaticity coordinates were close to the standard sample, but still slightly exceeded the color difference limit set by the first type of target customer. Simultaneously, the material file showed that this batch was a cotton and modal blend. The customer tolerance matrix indicated that the first type of target customer had a higher tolerance weighting coefficient for feel and environmental labels than the preset sensitivity threshold, while the second type of target customer had a higher tolerance range for color difference boundaries than the preset standard threshold. Therefore, the system no longer considered continued color matching as the sole objective, but instead provided sufficient data for subsequent comprehensive evaluation. The purpose of this step is to integrate the process environment, color status, and order acceptance constraints of the dyeing site into data inputs that can be used for unified decision-making, so as to ensure that the subsequent prediction results are consistent with both the material mechanism and the actual business boundary. In this embodiment, the physical layer mapping processing module includes a polymer fatigue integration unit. The polymer fatigue integration unit is used to perform integration calculations on temperature curve data and pH data using the heat preservation time data as the integration interval to generate a microstructure damage index. The integration calculation adopts a time axis based on the heat preservation time, using the empirical fatigue decay constant of a specific material at the current pH value as a benchmark. The empirical fatigue decay constant is obtained by matching a pre-constructed material fatigue decay mapping relationship table. The temperature difference exceeding the material's safety tolerance threshold is continuously accumulated and weighted over time. The specific conversion logic and constant matrix are stored as built-in expert system rules in the central control server connected to the system, thereby converting thermochemical stress into a quantifiable damage index.

[0019] This embodiment provides a polymer fatigue integral mechanism for identifying microscopic damage to fibers. Specifically, when relying solely on the current temperature or pH for risk assessment, a defect can easily occur: the temperature of the dyeing vat at a certain moment may not seem abnormal, but if it has already undergone long-term heat preservation or multiple rounds of chemical environment fluctuations, the actual damage to the fiber may have already approached the irreversible range. To address this issue, this embodiment introduces a polymer fatigue integral unit. Using the holding time as the observation interval, it performs continuous cumulative analysis of temperature curves and pH changes, thereby forming a microstructural damage index that better reflects the fiber damage pattern. The core idea of ​​this unit is to map the stress history of the fiber in the dye bath as a continuous process, rather than a single discrete state. For fibers such as cotton and modal, increased temperature enhances the mobility of molecular chain segments, which is beneficial for dye diffusion and dyeing under moderate conditions. However, if the high temperature is maintained for too long, the stable internal structure of the fiber will be continuously disturbed. pH affects the chemical stability of the fiber molecular chains. When the dye bath environment deviates from the material's suitable range, the fiber is more prone to hydrolysis, chain breakage, or surface roughening. This damage is cumulative; the microscopic weakening formed in the previous stage makes the fiber more sensitive to the same thermochemical stimuli in the subsequent stage. Therefore, this embodiment does not treat temperature and pH as two independent numbers, but rather as stress sources that work together over the duration of heat preservation. For ease of understanding, a simplified process segment can be used to illustrate the flow of data after it enters the unit. Assume that the heat preservation interval of a certain batch is divided into a first heat preservation stage, a second heat preservation stage, and a third heat preservation stage, where S1 is the first high-temperature heat preservation, S2 is the second heat preservation after color correction, and S3 is the stabilization stage before leaving the tank. The temperature curve is higher in S2 than in S1, and the pH is also further away from the suitable range for the material in S2. The polymer fatigue integral unit will treat S1 to S3 as a continuous exposure history, and the output is not a local judgment of a certain stage, but a cumulative damage result. The result typically manifests as follows: if subsequent color correction causes temperature and pH to continue to exert their effects on the already damaged foundation, the damage index will increase to above the preset safety benchmark value. The integral calculation here emphasizes the continuous accumulation of exposure history. In its specific implementation, a basic attenuation constant can be set, and the absolute value of the temperature difference exceeding the standard temperature and the pH deviation value corresponding to each time step of the heat preservation time can be used as independent variables. The cumulative damage result is obtained by summing the values ​​using a preset multivariate linear or nonlinear formula. Compared to simply observing the final color difference, this mechanism can characterize the following objective laws of the process: during the process of color convergence to the standard sample, the internal structure of the fiber may be continuously deteriorating. This deterioration will not be immediately reflected in the apparent color, but will often show its performance defects in the finishing, garment washing, or customer use stages. By first forming a microstructure damage index, the system can intervene before the quality is out of control. In the fault tolerance mechanism, if the heat preservation time data is interrupted, such as due to equipment shutdown or manual intervention causing discontinuity in the process segment, the unit will accumulate the segments before and after the interruption separately and insert a state recovery mark in the middle to prevent the shutdown cooling stage from being mistakenly counted as continuous high temperature exposure. If the pH sensor drifts, the system can correct the interval by combining the feeding record and manual test results. If it cannot be corrected, the interval will be assigned a high uncertainty mark and a conservative strategy will be adopted in the subsequent comprehensive evaluation. If the material is a multi-component blend, the system can determine the dominant damage boundary according to the component that is more sensitive to the thermochemical environment to prevent the risk of strong resistant components masking weak resistant components. In the aforementioned dark navy blue knitted fabric order, the first heat preservation stage was mainly used to complete the basic dyeing, while the second heat preservation stage occurred after the color correction and addition. Although the second color measurement showed that the color was close to the standard sample, the system, combined with the heat preservation time, found that this batch had accumulated a long time in the high-temperature zone, and the acid and alkaline environment during the second heat preservation was further away from the neutral range than during the first heat preservation. Based on this, the polymer fatigue integral unit determined that the fiber molecular chains had signs of continuous fatigue, and the microstructure damage index was significantly higher than the level at the end of the first heat preservation, thus providing a key basis for the subsequent comprehensive health evaluation. The purpose of this step is to transform the combined effects of temperature, pH, and heat treatment time on the fiber into a continuously trackable damage characterization, thereby enabling early identification of risks where the surface color is close to meeting the standard but the internal structure is close to failure. In this embodiment, the physical layer mapping processing module further includes a cost accounting unit; the cost accounting unit is used to convert the data of the chemical reagents that have been applied into chemical oxygen demand contribution data, and to map the chemical oxygen demand contribution data into dynamic environmental load cost data based on the unit treatment load assessment benchmark data built into the system.

[0020] This embodiment provides a dynamic cost accounting mechanism for environmental disposal pressure; specifically, if the cost is still only understood as the amount of dye or auxiliaries added, there is a significant defect: the subsequent impact of adding agents in the workshop is not limited to the agents themselves, but also includes the increased load on the wastewater treatment system, the increased pressure to meet discharge standards, and the increased load on co-treatment due to high-concentration wastewater. Therefore, this embodiment further introduces a cost accounting unit to convert the data of added chemical agents into chemical oxygen demand (COD) contribution data and map it into dynamic environmental load cost data. Specifically, the COD contribution data can be understood as how much oxidizable pollution load this batch has transferred to the downstream wastewater treatment process. In dyeing production, different agents have different efficiencies in color adjustment and different pressures on wastewater treatment. The utilization rate of certain color-correcting or auxiliary chemicals in the dye bath is lower than the preset utilization rate lower limit, and the portion not adsorbed by the fibers eventually enters the wastewater system. When the number of color correction cycles increases, this ineffective residue may accumulate rapidly. Therefore, the system no longer uses the absolute mass of the added chemical agents as the sole cost observation, but further quantifies and assesses the actual pollution load that the chemical agents ultimately introduce to the downstream wastewater treatment system; the unit treatment load assessment benchmark data can be maintained and updated by the enterprise based on the wastewater treatment plant's operating parameters, chemical consumption, power consumption, sludge disposal volume, or external disposal parameters; through this mapping, the system can identify that a certain color correction operation corresponds not only to changes in feed but also to changes in environmental disposal load; To illustrate this data flow, a simplified example can be used. Assume the reagent record contains a first dye (C1), a first leveling agent (C2), and a first color-correcting auxiliary agent (C3). The cost accounting unit reads the dosage and category of the first dye, the first leveling agent, and the first color-correcting auxiliary agent, and then converts them into corresponding pollution load contributions according to the built-in mapping table, forming a set of first load contribution values ​​(Q1), second load contribution values ​​(Q2), and third load contribution values ​​(Q3), which are summarized as the current total chemical oxygen demand contribution data for this batch. The specific summary process is limited to: the system extracts the absolute dosage of each reagent, multiplies it by the preset ineffective residual conversion rate at this process stage, multiplies it by the standard chemical oxygen demand equivalent coefficient of the corresponding reagent, and finally adds up the various ineffective loads to obtain the total chemical oxygen demand contribution data for this batch. Then, the system maps the total chemical oxygen demand contribution data obtained from the unit processing cost table to the dynamic environmental load cost data K; if color correction agent C3 is added again, the corresponding contribution data Q and environmental load assessment data will be updated synchronously; in this way, the multi-dimensional state prediction module can perceive the immediate impact of the new color correction on the environmental load characterization when assessing the continued color correction. Furthermore, C1, C2, and C3 correspond to different reagent record items in the aforementioned example, Q1, Q2, and Q3 correspond to the pollution load contribution values ​​converted from these three types of reagents, Q represents the aggregated chemical oxygen demand contribution data, and K represents the dynamic environmental load cost data further mapped from Q; the aforementioned letters are only used to illustrate the hierarchical mapping relationship in this data stream, and will not be given any other meanings in the following text; furthermore, in order to avoid the chemical oxygen demand contribution being understood as a fixed value detached from the process scenario, the conversion relationship in this embodiment can be stratified according to reagent type, dosage, and its effective utilization rate in the current process stage; In other words, the residual proportion of the same weight of reagent entering the wastewater system can be different in the basic dyeing stage and the subsequent low-efficiency color-correction stage. The system can select the corresponding load coefficient from the built-in mapping table for conversion based on the reagent type label, whether it is currently in the repeated color-correction stage, and the historical dyeing response. This process emphasizes the traceability of the conversion logic: first identify the reagent type, then determine the process stage, and then select the corresponding load coefficient to obtain the chemical oxygen demand contribution data, rather than simply converting all reagents according to a uniform ratio. Therefore, the subsequently generated dynamic environmental load cost data can more accurately distinguish the differences in environmental load characterization between the initial normal feeding and the inefficient repeated color correction feeding; furthermore, in this embodiment, the dynamic environmental load cost data can represent both the quantitative data corresponding to the real-time disposal load and the comprehensive cost characterization with order constraint interpretation; when the former is used, it mainly reflects the load intensity of wastewater treatment and compliant disposal itself. When used in conjunction with basic attribute constraint data, the load intensity can also be superimposed with order-level warning significance. For example, if a customer is more sensitive to environmental labels, the same increase in processing load will be given a higher risk level. This is consistent with the cost accounting path in this embodiment and also connects with the overall description of cost boundaries and warning scope given by basic attribute constraint data in the implementation of the embodiment. This mechanism has clear significance in industry. In traditional workshops, when the color difference is only less than the preset tolerance threshold, operators tend to continue color correction because the material input records show only a small amount of additional material is needed. However, from the perspective of wastewater treatment, this additional action may happen precisely when the dye bath utilization rate is low and the ineffective residue is high, and the increase in the new treatment pressure is greater than the improvement in the color. The introduction of dynamic environmental load cost data allows the system to move the back-end treatment pressure to the front-end decision-making. In the fault tolerance mechanism, if a complete chemical oxygen demand mapping relationship has not yet been established for a certain new agent, the system can first make a conservative estimate based on the upper limit load of similar agents and mark the record as pending calibration; if the unit treatment cost is updated due to changes in the operating conditions of the wastewater treatment plant or adjustments to external treatment parameters, only subsequent batches will be calculated according to the new parameters, while the original calculation version will be retained for archived batches to ensure traceability consistency; if there is a delay in manual supplementation of agent dosing records, the system can automatically recalculate the dynamic environmental load cost of the batch after receiving the supplementary information and update the comprehensive health status simultaneously. In the aforementioned dark navy blue knitted fabric order, since a certain amount of leveling agent and color-correcting auxiliaries had been added during the two color corrections, the system identified that the contribution of chemical oxygen demand for this batch was increasing based on the data of the added chemical agents. Although the third color correction only corresponded to a small increase in agents, the cost accounting unit showed that the wastewater treatment load corresponding to this additional material input was already close to the preset cost warning line for this batch. At this point, in subsequent comprehensive assessments, the system no longer considers small-scale additions as low-impact actions, but rather identifies them as high-risk actions that may lead to environmental load imbalance. The purpose of this step is to establish a direct link between the material feeding behavior at the dyeing site and the pressure of back-end environmental treatment, thereby achieving dynamic perception of the actual load status of the batch and avoiding the neglect of back-end environmental treatment pressure in front-end production decisions. In this embodiment, the decision intervention management module is also used to: in response to the generation of the circuit breaker alarm command, block the chemical reagent addition channel of the dyeing equipment through the underlying controller and lock the current temperature control parameters of the dyeing equipment; if an abnormality in the communication of the underlying controller is detected, causing the blocking command to not be executed, send a secondary alarm to the workshop management terminal and the on-site audible and visual alarm connected to the system, and trigger the safety shutdown process.

[0021] This embodiment provides a device-level safety intervention mechanism after a circuit breaker is triggered. Specifically, if the system can output a circuit breaker alarm command, but only an alarm is displayed on the interface without further constraints on the field equipment, there is still a major defect: the workshop operator may continue to add reagents or continue to increase the temperature due to human non-standard operation or illegal intervention, which will result in the high-risk state identified by the system not being truly contained. Therefore, in this embodiment, after the fuse command is generated, the chemical agent addition channel is directly blocked by the underlying controller, and the current temperature control parameters are locked, so that the risk control is upgraded from the prompt level to the execution level. The significance of blocking the chemical agent addition channel is to cut off the source of further expansion of the chemical load of the dye bath. Once the system has determined that continued color correction will have a significant adverse effect on the fiber structure or environmental load, the continued addition of dyes, leveling agents or color correction auxiliaries will usually not bring a proportional quality improvement, but will instead increase the pressure of subsequent processing. The significance of locking the current temperature control parameters is to prevent the temperature from being increased again on-site in pursuit of faster dyeing or stronger color correction, or to extend the high-temperature plateau when the condition is already at risk. For fibers, when they are at the melting point, what needs to be avoided most is not the current state itself, but the trend of the state continuing to deteriorate. Therefore, it is safer to keep the temperature at the current controlled level than to continue to increase the temperature. At the underlying implementation level, blocking actions can be accomplished by turning off the automatic metering pump enable signal, closing the feeding solenoid valve, or preventing the host computer from issuing new dosing formula tasks; temperature locking can be achieved by freezing the current set value, blocking manual adjustment permissions, or limiting the temperature controller to only allow cooling and not heating; to ensure reproducibility, a simplified logic example can be adopted: when the system receives a judgment result that the overall health level is lower than the low threshold, it synchronously outputs a first control bit for closing the dosing channel and a second control bit for locking the temperature parameter to the underlying controller. Afterward, even if the operation interface still retains the manual viewing function, it will no longer allow the execution of new color correction and material feeding and heating commands until the fuse state is lifted with management authorization; this mechanism has a clear industrial necessity; the risks in the dyeing process have a cumulative and amplified effect, especially in the later color correction stage, although the single parameter adjustment amount of an extra chemical addition and an extra heating is less than the preset warning value, it may be decisive for fibers that are already close to the damage boundary; by blocking at the equipment level, the disconnect problem of the system having issued a warning but the equipment continuing to operate at high risk can be avoided; In the fault tolerance mechanism, if a communication failure of the underlying controller causes the blocking command to be not confirmed and executed, the system should immediately send a level-two alarm to the workshop management terminal and the on-site audible and visual alarm, and require manual switching to the safe shutdown procedure; if certain special processes require the need for heat preservation and slowing down, temperature locking is not equivalent to immediate heating stop, but rather maintaining a controlled state based on the current parameters to prevent the temperature from continuing to rise; if an incomplete mechanical safety operation is detected in the dyeing tank after the melt is broken, such as the need for the circulating pump to maintain short-term operation to prevent local overheating, the system allows the necessary safety maintenance actions to be retained, but does not allow any additional operations that change the chemical environment of the dye bath; In the aforementioned dark navy blue knitted fabric order, the system's comprehensive evaluation revealed that if the third color correction was continued, it would further deepen the microstructural damage to the cotton and modal blended fabric, while the wastewater treatment load would increase significantly, and the current room for color difference improvement was already very limited. Therefore, the system generated a circuit breaker alarm command and immediately shut down the execution channel of the color correction auxiliary metering pump through the underlying controller, while freezing the current temperature setting of the dyeing vat to prevent on-site personnel from raising the temperature further. In this way, even if the operator still wants to continue to correct the color by extending the heat preservation time or adding a small amount of reagent, they cannot bypass the risk control decision already made by the system. The purpose of this mechanism is to directly translate the risk management conclusion into executable actions at the equipment level, thereby achieving hard constraints on high-risk color correction behaviors and preventing batches from sliding from a recoverable state to an irreversible damage state. In this embodiment, the decision intervention management module is also used to: in response to the generation of the downgrade receiving instruction, compare the predicted state data with the customer tolerance matrix data, and generate a target customer matching list.

[0022] This embodiment provides a customer matching mechanism for downgraded receiving scenarios. Specifically, if the aforementioned system can identify that the evaluation gain brought by the continued color correction operation is lower than the preset threshold but has not reached the intermediate state that must be circuit-broken, and if it can still only output a general conclusion to stop color correction, there is a problem of insufficient business implementation: although the enterprise avoids further damage to the fabric, it may not be able to quickly determine which orders the batch can still serve, thus causing unnecessary inventory backlog or scrapping of the entire batch. Therefore, in this embodiment, after the downgrade acceptance instruction is generated, the predicted status data is compared with the customer tolerance matrix data to generate a target customer matching list. As described below, downgrade acceptance does not simply mean that the product quality is unqualified. Rather, it means that although the batch has not fully met the strictest boundary of the original order, it may still meet the secondary level requirements of other customers, other product lines, or the same customer in terms of color, feel, strength, or environmental label. Therefore, the system needs to transform the predicted status data into an acceptance description that can be used for process flow or downgrade acceptance judgment; the predicted status data here includes at least predicted color difference data, predicted damage increment data, and predicted load increment data; the customer tolerance matrix data can be understood as a multi-condition acceptance table, where each row corresponds to a customer or an order level, and each column corresponds to a key dimension, such as color difference upper limit, hand feel degradation tolerance range, strength lower limit, green label requirements, etc. To illustrate the comparison process, a simplified example can be used. Assume there are three sets of receiving rules in the customer tolerance matrix, denoted as the first receiving rule, the second receiving rule, and the third receiving rule. R1 corresponds to the original brand customer, requiring strict color difference and minimal change in feel. R2 corresponds to a secondary brand within the same group, allowing a color difference tolerance range greater than the first-level threshold. R3 corresponds to tooling customers, requiring a lower consistency threshold for color batch consistency than the first level, but with higher strength requirements. After generating the downgrade receiving instruction, the system reads the prediction status data packet Y, which includes the first predicted color difference item (Y1), the predicted damage item (Y2), and the predicted cost item (Y3). The system sequentially compares the predicted status data packet Y with the receiving rules R1, R2, and R3. If the predicted status data packet Y does not satisfy R1 but satisfies R2, the corresponding customer is added to the target customer matching list. If the predicted status data packet Y is uncertain about the strong conditions in R3, it is not added to the official list but to the list awaiting manual review. In this way, the system can convert stopping color correction into a specific business action of suggesting which acceptable orders to transfer. Furthermore, to avoid unclear comparison benchmarks due to multiple candidate action prediction results after the degradation receiving instruction is generated, the prediction status data in this embodiment preferably adopts the prediction status data corresponding to the target action that is actually linked to the degradation receiving instruction. Specifically, when the multi-dimensional status prediction module has selected the target action from the candidate actions to stop further aggressive color correction, maintain the current safe process state, prepare for cylinder discharge, or conservatively finish, and triggers the degradation receiving instruction accordingly, the customer matching mechanism reads the prediction color difference data, prediction damage increment data, and prediction load increment data corresponding to the target action, rather than the prediction results corresponding to other unadopted candidate actions. After this processing, the customer-side matching is based on the process path that the equipment side will actually execute, avoiding the disconnect between the customer matching based on one prediction status and the equipment actually operating according to another action. Furthermore, to avoid semantic gaps between the predicted damage increment data and the finished product acceptance conditions such as strength and feel in the customer matrix, this embodiment performs a state interpretation transformation before comparison; that is, the system does not directly compare the numerical results of the damage increment with the customer rules, but generates corresponding physical property risk labels based on the material type of the batch, the existing microstructure damage index, and the predicted damage increment, such as low risk of strength reduction, medium risk of hardening of feel, and high risk of pilling, and then compares these physical property risk labels with the acceptance conditions in the customer tolerance matrix item by item. After this processing, a clear data interpretation chain is formed between the predicted status data, the customer tolerance matrix data, and the final customer matching list, which can avoid the abruptness caused by directly using pure process quantities for business acceptance judgment. Furthermore, the generated result of the target customer matching list can be divided into three levels: formal matching, conditional matching, and non-matching. Formal matching means that the predicted status has met the corresponding customer rules under the current information completeness. Conditional matching means that the main indicators are basically compatible, but there are still items that need to be manually confirmed, such as the strong actual test has not yet been returned, and the environmental label needs to be reviewed by the business. Non-matching means that even if the color correction is stopped and the final step is conservative, the batch still does not meet the corresponding customer boundary. Through this tiered output, the downgraded receiving instructions not only serve the conservative control on the equipment side, but also simultaneously serve the order conversion decisions on the business side. The technical significance of this mechanism lies in its connection between quality management and order fulfillment capacity. For dyeing and printing enterprises, the worst-case scenario is not that all batches meet the standards perfectly, but that even a slight deviation in a batch means the entire batch must be scrapped. Through the customer matching mechanism, the system can provide disposal suggestions that are more in line with the constraints of the next process while protecting the overall physical properties of the fabric. In the fault tolerance mechanism, if some customer rules in the customer tolerance matrix have not been updated for a long time, the system can add a mark in the rule validity period verification to the matching results of the customer and require business personnel to confirm before actually placing the order; if there is a large uncertainty in a certain item in the forecast status data, for example, it is difficult to judge accurately because the strong test has not been completed, the system can put the customer into the condition matching category instead of directly including it in the formal matching list; if no customer rules are compatible with the current forecast status, the system retains a manual evaluation interface outside of the circuit breaker, which is used to consider the decision of repurposing, downgrading inventory, or rework. In the aforementioned dark navy blue knitted fabric order, the system determined that continuing color correction would be significantly detrimental, but the current batch did not reach the severity requiring immediate cessation. Therefore, a downgraded acceptance instruction was issued. At this point, the prediction results showed that if color correction was stopped, the final color difference would exceed the requirements of the original sports brand's first-tier target customer, but the fabric's hand feel and strength could still be maintained within the preset performance safety range. After comparing this prediction status with the customer tolerance matrix, the system found that the first-tier target customer could not accept the order, but the secondary casual brand to which the second-tier target customer belonged allowed the color difference range and its hand feel requirements matched the current batch. Therefore, the system automatically generated a target customer matching list that included the second-tier target customer and prompted the business personnel on the management terminal to prioritize order transfer. The purpose of this mechanism is to establish an executable intermediate disposal path between continuing color correction and failing to meet comprehensive indicator requirements and scrapping the entire batch, thereby maximizing the preservation of batch value and flexibly matching order resources. In this embodiment, the system is also connected to a display terminal; the system also includes a quality cost traceability module; the quality cost traceability module is used to generate batch profit contribution dashboard data based on dynamic environmental load cost data and microstructure damage index, and output the batch profit contribution dashboard data to the display terminal.

[0023] This embodiment provides a quality cost traceability mechanism for batch process assessment; specifically, after the aforementioned system is already able to make operational decisions for a single batch, if the relevant results only remain at the real-time control level and are not further transformed into batch-level analysis data, then the management side will find it difficult to systematically identify which categories, which process segments, and which customer constraint combinations are more likely to cause high damage and high environmental load problems. Therefore, this embodiment further sets up a quality cost traceability module and outputs batch profit contribution dashboard data through a display terminal. The quality cost traceability module does not simply interpret profit contribution as the difference between the order output value and the basic material input record, but incorporates dynamic environmental load cost data and microstructure damage index into the batch comprehensive evaluation system. The logic is that even if a batch has completed the delivery process, if excessive color correction during the dyeing process causes significant fiber damage, it may manifest as rework, complaints, or quality risks during subsequent garment processing, acceptance, or use. Simultaneously, if this batch significantly increases the burden of wastewater treatment and compliant disposal during the production stage, its corresponding profit contribution dashboard data should also reflect the impact of such processes. Therefore, this module moves the aforementioned process impacts forward and forms a visualized batch dashboard. The dashboard data may include, but is not limited to, the following information: basic batch identity, target customer or order transfer customer, main process segment, microstructure damage index level, dynamic environmental load cost range, current acceptance level, whether continued execution, downgraded acceptance, or circuit breaker actions have been triggered, and the profit contribution status after comprehensive judgment. The display terminal can be a workshop central control screen, process engineer workstation, or management dashboard interface. Through unified display, enterprises can quickly identify batches with high color achievement but high process costs, as well as process strategies that adopt downgraded acceptance paths but have a more stable overall status. To illustrate the data formation process, a simplified example can be used: The system archives the first batch (B1), the second batch (B2), and the third batch (B3); The quality cost traceability module reads the dynamic environmental load cost data K1, K2, K3 and the microstructure damage index D1, D2, D3 of these three batches respectively, and generates three Kanban records based on the order destination and acceptance level of the batch; If B1 has the best color difference performance, but the cost data K1 and damage index D1 are both high, the Kanban can mark it as having high color achievement and high process load; If B2 has a slightly wider color difference but has been successfully transferred to a secondary customer and the cost data K2 and damage index D2 are within a reasonable range, the Kanban can mark it as having downgraded acceptance and stable status; In this way, management can review production decisions on a batch basis to see if they are truly effective; this mechanism has direct value for continuous improvement of enterprises; after long-term accumulation, enterprises can identify from the dashboard data which color systems and material combinations are most likely to have limited color benefits, damage and high environmental impact during the later stage of color correction, thereby providing an optimization basis for the front-end formulation, process route and order constraint settings. In the fault tolerance mechanism, if the dynamic environmental load cost data of a certain batch has not yet been finalized, such as the parameters of the sewage treatment plant for the day not yet being returned, the system can first display the provisional value and mark it as pending settlement in the dashboard; if the microstructure damage index deviates from the subsequent physical test results, the system retains the backtracking correction interface for calibrating the material model, but does not overwrite the original historical records to ensure the authenticity of the traceability; if the display terminal is temporarily offline, the module should continue to generate and cache the dashboard data on the server side, and resend the display after the terminal restores the connection. After the aforementioned dark navy blue knitted fabric order was completed, the system summarized the batch's color correction history, microstructure damage index, dynamic environmental load cost, and final disposal result (transferred to the second-category target customer) into a profit contribution dashboard record. The dashboard showed that although the batch did not fully meet the initial standards of the first-category target customer, the system timely prevented further high-risk color correction in the later stages, avoiding further strong color reduction and a significant increase in wastewater treatment load. Ultimately, the batch was disposed of through the order transfer path, and its dashboard status was better than that of excessive color-tracking operations that ignored constraints. Managers could intuitively see that timely downgraded acceptance was not simply abnormal disposal, but a traceable process preservation strategy. The purpose of this mechanism is to precipitate real-time process control results into batch-level analysis data, thereby achieving unified traceability and continuous optimization between production, quality, environmental protection, and process status. In this embodiment, the multi-dimensional state prediction module is also used to: feed back the predicted state data to the data sensing input module to update the real-time process state data and form a closed-loop control link.

[0024] This embodiment provides a closed-loop control mechanism for continuous regulation. Specifically, after the aforementioned system is able to generate prediction results and intervention instructions based on the current state, if each judgment only relies on the current instantaneous data and does not feed the prediction results back to the subsequent sensing links, there is a defect: although the system can make a single judgment, it is difficult to maintain contextual consistency during continuous color correction, continuous heat preservation, or continuous degradation, which may lead to repeated evaluation, state fragmentation, or control lag. Therefore, in this embodiment, the predicted state data is fed back to the data sensing input module to update the real-time process state data, forming a closed-loop control link. This is explained in detail below. The closed loop does not simply treat the predicted result as the final fact, but rather as a priori reference for the state evolution at the next moment. In the dyeing process, the state of the dyeing vat is continuously changing. Once a certain control action is executed, the subsequent measured changes in temperature, pH, and color are not randomly discrete, but closely related to the expected action at the previous moment. After feeding the predicted state data back to the input module, the system can synchronously compare the consistency between the actual changes and the predicted changes in the next round of data collection, thereby promptly identifying problems such as model deviations, abnormal equipment responses, or inadequate process execution. For example, the system originally predicted that after stopping the addition of the drug and maintaining the current temperature, the color difference would be basically stable and the damage increment should be low; if the next round of collection finds abnormal color fluctuations or pH changes that do not match the expectations, it indicates that there may be residual drug release, sensor deviation or human intervention, which requires further verification. For ease of explanation, a simplified closed-loop example can be used. Assume the system acquires the current actual state at the current sampling time. Based on this, the system predicts that after executing the first candidate action for the target, it will form the predicted state for the next sampling time. After the first candidate action for the target is actually issued, the data sensing input module re-acquires the actual state for the next sampling time. The system updates the predicted state for the next time with the actual state for the next time: if X2 and Y1 are generally consistent, X2 is used as the new real-time process state to continue the next round of prediction; if the two deviate significantly, an anomaly flag is added when updating X2, and the level of caution for subsequent decisions is increased. Through this feedback loop, the system can not only evaluate the current state but also verify the consistency of the previous round of prediction, thus forming a truly continuous control process. Furthermore, t and t+1 represent two adjacent sampling times, X1 represents the current state record at the current sampling time, Y1 represents the predicted state at the next time based on X1 and combined with the first candidate action, X2 represents the actual state obtained by re-collecting at the next sampling time, and the first candidate action represents the target action actually issued and executed in this round of control; the above letters and symbols are only timing and state identifiers in the closed-loop process description, and their meanings are subject to the aforementioned text bindings; Furthermore, to ensure that the predicted state data is fed back to the data sensing input module to update the real-time process state data in accordance with the aforementioned input data standards, the update in this embodiment preferably adopts an enhanced state recording method. That is, the data sensing input module retains the original temperature curve data, pH data, heat preservation time data, and chemical reagent data as basic real-time process state data in each sampling cycle. At the same time, the predicted color difference data, predicted damage increment data, and predicted load increment data fed back from the previous cycle are written into the current batch state record as prior markers associated with the basic real-time process state data. When entering the next round of processing, the physical layer mapping processing module prioritizes using the new original acquired values ​​for calculation, while the multidimensional state prediction module simultaneously reads the prior label and the actual acquisition results of this round to determine whether the previous round of prediction has been fulfilled, whether the priority of candidate actions needs to be adjusted, or whether the risk weight needs to be increased. In other words, updating the real-time process state data does not require the predicted value to directly replace the sensor measured value, but rather to form a continuous state record of measured state + predicted prior + consistency label on the same time axis, so as to ensure that the closed-loop control link can utilize the previous round of prediction information without destroying the authenticity of the original field data. Furthermore, when both directly observable and indirectly observable quantities coexist, the system can employ stratified processing of feedback data. For directly collectable quantities such as temperature, pH, insulation duration, and added chemical reagents, subsequent status updates are based on newly sampled and measured values. For derived quantities used for prior judgment, such as microstructural damage trends, predicted cost change trends, and predicted color difference evolution directions, the previous round of prediction results are retained and await verification in the next round of data. If the verification results show that the actual change direction is consistent with the prediction, the derived quantity maintains its normal weight in subsequent control. If the verification results deviate for a long period of time, the corresponding derived quantity will be marked as low confidence and the model will be re-verified or the sensor will be inspected. After this processing, the feedback and update in the closed loop have a clear data hierarchy and will not cause confusion by directly covering the actual on-site measurement with the predicted quantity. This mechanism is particularly important in the dyeing industry because the dye bath system has obvious hysteresis and inertia. The effect of a certain chemical addition or heat preservation is often not immediately fully manifested, but is gradually transmitted over several subsequent sampling cycles. Without closed-loop feedback, the system may underestimate the hysteresis effect, leading to continuous adjustments in the same direction. With a closed-loop mechanism, the system can identify that the aftereffects of previous actions have not yet been released, thus avoiding excessive intervention. In the fault tolerance mechanism, if the actual collected value is found to deviate too much from the predicted value after the predicted state data feedback, the system can automatically trigger the model re-verification or sensor inspection process and temporarily switch to a more conservative control mode. If a round of feedback is missing, for example, if the online colorimetric device has not yet completed the next sampling, the system can update part of the status based only on the available temperature and pH data, while keeping the previous round of color prediction in a pending state to avoid breaking the closed loop due to incomplete data. If an emergency operation is manually inserted, such as a temporary change in circulation flow due to equipment maintenance, the system should record the external intervention event and include it as an additional explanation in subsequent status updates to prevent human factors from being misjudged as model distortion. In the aforementioned dark navy blue knitted fabric order, the system predicted after the second color correction that if no more reagents were added and the current temperature was maintained, the color difference would not improve significantly in the next sampling period, but the increase in microstructural damage would tend to slow down; this prediction result was fed back to the data sensing input module as a reference for the next round of state updates. The system re-collected online color measurement and process data, finding that the actual color change was basically consistent with expectations, and that there were no abnormal drifts in temperature and pH. Therefore, the current control path was confirmed to be effective, and conservative operation was maintained. If the actual data suddenly showed an abnormal rise in pH, the system could immediately identify it as inconsistent with the previous prediction and further investigate whether there was residual material feeding, valve leakage, or sensor failure. The purpose of this mechanism is to establish a continuous control link of prediction-execution-re-sensing-re-correction, so as to achieve continuous tracking and timely correction of the state evolution of the dyeing process and reduce the lag and deviation caused by single judgment.

[0025] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A quality management system for textile dyeing production, characterized in that, The system communication connection includes the underlying controller of the staining equipment, including: The data sensing input module is used to collect real-time process status data, optical deviation data, and basic attribute constraint data, including customer tolerance matrix data. The physical layer mapping processing module is used to calculate the microstructure damage index based on real-time process status data, and to calculate the dynamic environmental load cost data based on the chemical reagents already applied and the basic attribute constraint data in the real-time process status data. The multi-dimensional state prediction module is used to input optical deviation data, microstructure damage index, and dynamic environmental load cost data into a preset prediction model to generate a comprehensive health index and predicted state data. The prediction model includes: a state simulation sub-model, used to extrapolate real-time process state data based on built-in candidate action data, outputting predicted state data containing predicted color difference data, predicted damage increment data, and predicted load increment data; and a health assessment sub-model, used to dynamically match and generate preset weight coefficients based on customer tolerance matrix data in basic attribute constraint data, and based on the preset weight coefficients, to perform weighted summation calculations on the predicted color difference data, predicted damage increment data, and predicted load increment data, outputting a comprehensive health index. The decision intervention management module is used to issue control instructions to the underlying controller based on the comprehensive health index. The control instructions include: if the comprehensive health index is greater than or equal to the first preset health threshold, generating and issuing a continue execution instruction; if it is less than the first preset health threshold but greater than or equal to the second preset health threshold, generating and issuing a downgrade reception instruction; if it is less than the second preset health threshold, generating and issuing a circuit breaker alarm instruction. The physical layer mapping processing module includes a polymer fatigue integration unit. The polymer fatigue integration unit is used to perform integration calculations on temperature curve data and pH data using the heat preservation time data as the integration interval to generate the microstructure damage index. The integration calculation adopts a time axis based on the heat preservation time, and uses the empirical fatigue decay constant of a specific material at the current pH as a benchmark. The empirical fatigue decay constant is obtained by matching a pre-constructed material fatigue decay mapping relationship table. The temperature difference exceeding the material safety tolerance threshold is continuously accumulated and weighted over time. The specific conversion logic and constant matrix are stored as built-in expert system rules in the central control server connected to the system, thereby converting thermochemical stress into a quantifiable damage index. The decision intervention management module is also used to: in response to the generation of the downgrade receiving instruction, compare the predicted state data with the customer tolerance matrix data to generate a target customer matching list; The customer tolerance matrix data is a multi-condition acceptance table, where each row corresponds to a customer or an order level, and each column corresponds to a key dimension, including the upper limit of color difference, the tolerance range for decreased feel, the lower limit of strength, and the green label requirement.

2. The textile dyeing production quality management system according to claim 1, characterized in that, The real-time process status data includes temperature curve data, pH data, heat preservation time data, and data on the chemical reagents added; the optical deviation data includes current color space coordinate data and color difference limit data; the basic attribute constraint data includes target object material data and customer tolerance matrix data.

3. The textile dyeing production quality management system according to claim 2, characterized in that, The physical layer mapping processing module also includes a cost accounting unit; the cost accounting unit is used to convert the data of the chemical reagents already applied into chemical oxygen demand contribution data, and to map the chemical oxygen demand contribution data into the dynamic environmental load cost data based on the unit processing cost data built into the system.

4. The textile dyeing production quality management system according to claim 1, characterized in that, The decision intervention management module is also used to: in response to the generation of the circuit breaker alarm command, block the chemical reagent addition channel of the dyeing equipment through the underlying controller, and lock the current temperature control parameters of the dyeing equipment; if an abnormal communication of the underlying controller is detected, causing the blocking command to not be executed, a secondary alarm is sent to the workshop management terminal and the on-site audible and visual alarm connected to the system, and a safety shutdown procedure is triggered.

5. A textile dyeing production quality management system according to claim 1, characterized in that, The system is also connected to a display terminal; the system also includes a quality cost traceability module; the quality cost traceability module is used to generate batch profit contribution dashboard data based on the dynamic environmental load cost data and the microstructure damage index, and output the batch profit contribution dashboard data to the display terminal.

6. A textile dyeing production quality management system according to claim 1, characterized in that, The multidimensional state prediction module is also used to: feed back the predicted state data to the data sensing input module to update the real-time process state data and form a closed-loop control link.

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