Intelligent management system and method for canvas production

Abstract

The invention relates to the technical field of textile production intellectualization, and discloses an intelligent management system and method for canvas production. The system comprises a parameter distribution unit, a tension optimization unit, a dye adjustment unit, an exception handling unit and a quality evaluation unit. The system analyzes equipment operation data through a parameter distribution unit and dynamically distributes regulation and control parameters; and the tension optimization unit corrects tension balance accordingly. The dye adjusting unit modulates input parameters in real time by detecting dye adsorption and diffusion characteristics. And the exception handling unit monitors temperature and tension fluctuation, analyzes the influence of the temperature and tension fluctuation on the dyeing rate, and dynamically calibrates a production path and a parameter ratio. And the quality evaluation unit finally optimizes the production path and generates a report. According to the system, through multi-parameter cooperative control and dynamic calibration based on the real-time process state, the problems of parameter coupling interference and uneven dye distribution in the production process are solved, and the production stability and the dyeing uniformity and quality consistency of canvas products are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligentization of textile production, in particular to a canvas production intelligent management system and method. BACKGROUND

[0002] The existing canvas production process, especially in the dyeing link, relies heavily on preset fixed parameters for control. The equipment control value and process balance coefficient are usually set according to experience, lacking the ability to dynamically adjust according to the real-time running state of the production line. When the characteristics of raw materials fluctuate or the production environment changes, this static control mode is difficult to maintain the stability of the production process, which can easily lead to batch differences in product quality. For the inevitable temperature fluctuations or tension deviations in the production process, the conventional technical means usually use independent feedback loops for processing. This isolated control method cannot cope with the complex working conditions in which temperature and tension parameters are coupled and jointly affect the dyeing effect. The interaction between parameters often leads to a vicious cycle of production state, ultimately destroying the established dye adsorption and diffusion balance, causing color difference or uneven dyeing.

[0003] The existing dye addition control is mainly based on fixed time and flow curves, without considering the actual dynamic behavior of the dye on the canvas surface under specific production conditions. Due to the inability to real-time perceive the adsorption rate and diffusion uniformity of the dye, the system cannot make targeted adjustments to the dye input strategy. When adsorption is too fast or diffusion is not smooth, fixed input parameters can cause uneven distribution of dyes on the canvas surface, and even cause defects such as insufficient penetration or surface color. The quality evaluation of the entire production process is mostly post-detection, and quality inspection is performed after the completion of canvas production. This lag makes it too late to find quality problems, and real-time intervention and correction cannot be made during the production process, resulting in high scrap rate and production cost. SUMMARY

[0004] The purpose of the present application is to provide a canvas production intelligent management system and method to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides a canvas production intelligent management system, which comprises: The parameter allocation management unit obtains the running state data of the canvas production equipment, processes the production rate index, temperature difference reading and material flow rate information, allocates the equipment control value and process balance coefficient by calculating the correlation between production parameter distribution and production stability, and outputs the parameter control set; The tension optimization management unit receives the parameter control set, measures the tension reading and gradient change trend in the weaving area, evaluates the effect of tension device power output on production stability, corrects the tension distribution balance, and generates a tension optimization set; The dye adjustment management unit detects the adsorption characteristics and diffusion mode of the dye on the canvas surface according to the tension optimization set, adjusts the dye input rate and flow rate distribution ratio, reconfigures the dye concentration distribution and dynamic parameters, and generates a dye adjustment output; The abnormality processing management unit uses the dye adjustment output to monitor the temperature fluctuation data and tension offset information in the production process, analyzes the influence of the fluctuation range on the dyeing rate, dynamically calibrates the dye path and temperature-tension ratio, and forms an abnormality processing set; The quality evaluation management unit investigates the canvas quality distribution characteristics and production time data based on the abnormality processing set, adjusts the production path parameters, and generates a quality detection report.

[0006] Preferably, the operation of the parameter distribution management unit includes: Collecting the running state data of the canvas production equipment, screening the production rate indicators, temperature difference readings and material flow rate information, removing outliers and normalizing the data format to obtain clean running state data; Analyzing the time series change of the clean running state data, calculating the correlation coefficient between the production rate and the temperature difference, adjusting the production parameter distribution combined with the material flow rate, and evaluating the distribution uniformity; According to the distribution uniformity, compare the production stability threshold, determine the matching degree of production parameters and stability, and output the parameter control set by weighted average distribution of equipment control values and process balance coefficients.

[0007] Preferably, the obtaining step of the clean running state data includes: Real-time collection of running state data of canvas production equipment, identification of outliers in production rate indicators, temperature difference readings and material flow rate information, application of filtering algorithm to smooth data fluctuations, and verification of data integrity and consistency; Segmented processing of the verified data, calculation of statistical characteristics in each time window, including mean, variance and trend slope, and integration of characteristic values to form clean running state data.

[0008] Preferably, the operation of the tension optimization management unit includes: Analyzing the parameter control set, extracting the tension reading sequence and gradient change trend in the equipment weaving area, dividing the area grid and calculating the tension value of each grid point; Analyzing the time sequence characteristics of the tension reading sequence, detecting the turning point of the gradient change trend, and calculating the tension distribution uniformity combined with the equipment structure parameters; According to the tension distribution uniformity, adjust the tension device power output parameters, optimize the tension distribution balance, and iteratively calculate until the stability condition is met, and output the tension optimization set.

[0009] Preferably, the tension distribution uniformity calculation step comprises: Based on the tension reading sequence, calculate the standard deviation and coefficient of variation of the tension at each point in the area, compare the deviation from the ideal distribution; Introduce a time decay factor to correct the gradient change trend, evaluate the cumulative impact of tension fluctuations on production stability, and generate a uniformity index; according to the uniformity index, dynamically adjust the control parameters of the tension device, redistribute the tension value, and ensure balanced distribution.

[0010] Preferably, the operation of the dye adjustment and management unit comprises: Receive the tension optimization set, monitor the dye adsorption rate and diffusion range on the canvas surface, and record the influence of environmental temperature and time factors on the adsorption process; Calculate the dynamic relationship between the dye input rate and the flow rate distribution ratio, adjust the ratio parameter to optimize the dye coverage uniformity; evaluate the dye concentration distribution trend, correct the dynamic parameter value, and calibrate the dye adjustment output through a feedback loop.

[0011] Preferably, the optimization step of the dye coverage uniformity comprises: According to the dye adsorption rate and diffusion range data, establish a dye distribution model to simulate the coverage effect under different input rates; compare the simulation results with the target uniformity, adjust the flow rate distribution ratio, and iterate optimization until the coverage uniformity meets the standard; record the parameter changes during optimization and update the dye adjustment output.

[0012] Preferably, the operation of the abnormal processing management unit comprises: Using the dye adjustment output, real-time collect the temperature fluctuation rate and tension offset during the production process, calculate the statistical characteristics of the fluctuation range; analyze the influence degree of fluctuation characteristics on the canvas dyeing rate, set an abnormal threshold and trigger an intervention mechanism; dynamically adjust the dye distribution path and temperature-tension ratio, correct the parameters according to real-time data feedback, and generate an abnormal processing set.

[0013] Preferably, the setting of the abnormal threshold and the triggering of the intervention mechanism comprises: Based on historical production data, train a fluctuation range model to determine the normal range of temperature fluctuation rate and tension offset; when the real-time data exceeds the normal range, start the intervention process, adjust the dye path and temperature-tension ratio, record the intervention log and update the abnormal processing set.

[0014] Preferably, the present application further comprises a canvas production intelligent management method applied to the canvas production intelligent management system as described above, the method comprising: Obtaining the running state data of the canvas production equipment, processing the production rate index, temperature difference reading and material flow rate information, distributing the equipment control values and process balance coefficients by calculating the correlation between production parameter distribution and production stability, and outputting a parameter control set; Receiving the parameter control set, measuring the tension reading and gradient change trend in the weaving area, evaluating the effect of tension device power output on production stability, correcting the tension distribution balance, and generating a tension optimization set; According to the tension optimization set, detecting the adsorption characteristics and diffusion mode of the dye on the canvas surface, adjusting the dye input rate and flow rate distribution ratio, reconfiguring the dye concentration distribution and dynamic parameters, and generating a dye adjustment output; Using the dye adjustment output, monitoring the temperature fluctuation data and tension deviation information in the production process, analyzing the influence of the fluctuation range on the dyeing rate, dynamically calibrating the dye path and temperature-tension ratio, and forming an abnormal treatment set; Based on the abnormal treatment set, investigating the canvas quality distribution characteristics and production time data, adjusting the production path parameters, and generating a quality detection report.

[0015] Compared with the prior art, the beneficial effects of the present application are: By analyzing the coupling relationship between temperature fluctuation data and tension deviation information and quantitatively evaluating the comprehensive influence of the coupling on the dyeing rate, the dye path and temperature-tension ratio are dynamically calibrated. This method correlates the two originally independent control variables of temperature and tension for correlation analysis, which can predict the potential risks of parameter interaction on the final dyeing effect. When a parameter drifts, the system does not correct it alone, but adjusts another parameter associated with it and the dye supply path for collaborative compensation. This multivariable feedforward-feedback control mechanism effectively suppresses the production process oscillation caused by parameter chain reaction, minimizes the influence of abnormal conditions on dyeing uniformity, improves the stability and continuity of the production process, and thus guarantees the consistency of product quality.

[0016] By detecting the adsorption characteristics and diffusion mode of the dye on the canvas surface, the dye input rate and flow rate distribution ratio are adjusted. This technology promotes the dye control from macro flow management to responsive management of interfacial chemical processes. The system dynamically reconfigures the dye concentration distribution and input dynamic parameters based on real-time feedback of adsorption rate and diffusion uniformity, so that the dye supply can accurately match the instantaneous state of the canvas material. This reverse modulation based on actual process effect ensures that the dye can be uniformly adsorbed and fully diffused at the optimal rate, overcoming problems such as uneven dyeing, insufficient color fastness, etc. caused by raw material batch differences or environmental changes, achieving an improvement from process control to result control, and enhancing the uniformity and reliability of the dyeing effect. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The working principle diagram of the canvas production intelligent management system described in the application; Figure 2 The flowchart of the parameter allocation management unit operation; Figure 3 The flowchart of the tension distribution uniformity calculation; Figure 4 The temperature fluctuation rate intervention effect comparison chart; Figure 5 The abnormal treatment stage temperature tension ratio PI controller dynamic adjustment curve. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the application.

[0019] Please refer to Figure 1 The application provides a canvas production intelligent management system, which comprises: a parameter allocation management unit, which first collects running state data of a canvas production device, and the data comprises production rate indicators, temperature difference readings and material flow rate information; the unit processes the original data, distributes device control values and process balance coefficients by calculating the correlation between production parameter distribution and production stability, and finally outputs a parameter control set; a tension optimization management unit, which receives the parameter control set, measures tension readings and gradient change trends in a weaving area, corrects the balance of tension distribution by evaluating the effect of tension device power output on production stability, and thus generates a tension optimization set; a dye adjustment management unit, which detects the adsorption characteristics and diffusion mode of dyes on the surface of the canvas according to the tension optimization set, reconfigures dye concentration distribution and dynamic parameters by modulating dye input rate and flow rate distribution ratio, and generates dye adjustment output; an abnormal treatment management unit, which uses the dye adjustment output to monitor temperature fluctuation data and tension offset information in the production process in real time, analyzes the influence of fluctuation range on dyeing rate, dynamically calibrates the dye path and the temperature tension ratio, and forms an abnormal treatment set; and a quality evaluation management unit, which investigates canvas quality distribution characteristics and production time data based on the abnormal treatment set, adjusts production path parameters, and finally generates a quality detection report.

[0020] Embodiment 1: Please refer to Figure 2In a specific implementation, the parameter distribution management unit is connected to a sensor network on the canvas production equipment in real time through a data acquisition interface. The sensor network continuously monitors the operating parameters of the production line, including production rate indicators, temperature difference readings, and material flow rate information. The production rate indicators are derived from the fabric output speed measured by an encoder. The temperature difference readings are obtained by a temperature sensor that measures the difference between the heating area and the ambient temperature. The material flow rate information is recorded by a flow meter that records the delivery rate of the dye or raw materials. The parameter distribution management unit has a data preprocessing module built-in. The preprocessing module performs preliminary screening on the original operating state data. It uses a statistical outlier detection algorithm to identify abnormal values in the production rate indicators, temperature difference readings, and material flow rate information. The abnormal value determination standard is three times the standard deviation range of the historical data mean value. The identified outliers are marked and excluded. In a specific implementation, the preprocessing module further uses the min-max normalization method to convert the remaining data into a unified numerical scale, eliminating dimensional differences. The normalized data format ensures that all indicators are within the interval of zero to one, forming an initial clean data set.

[0021] In some embodiments, the step of obtaining clean operating state data includes a data verification and feature extraction phase. The data verification phase applies filtering processing to the initial clean data set. The filtering algorithm uses a moving average method to smooth short-term fluctuations in production rate indicators and temperature difference readings. The moving average window size is dynamically adjusted according to the production cycle, usually set to five to ten sampling points. At the same time, the consistency check of the material flow rate information is achieved by comparing the flow rate change rate at adjacent time points. If the change rate exceeds the threshold, a data review process is triggered. The review process involves manual confirmation or cross-validation of redundant sensor data. The verified data enters the segmentation processing link. The segmentation processing divides the data stream into fixed time windows. Each window length is set to the duration of a production batch. The arithmetic mean of the production rate indicators, the variance of the temperature difference readings, and the trend slope of the material flow rate information within each window are calculated. The trend slope is obtained by linear regression fitting of the data points within the window, reflecting the direction of parameter change. These statistical characteristic values are integrated into a structured array. The array elements include mean, variance, and slope values, thus forming complete clean operating state data.

[0022] In a specific implementation, the analysis module of the parameter distribution management unit loads the clean operating state data, and the analysis module parses the time series structure, which is composed of production rate indicators, temperature difference readings, and material flow rate information corresponding to consecutive time stamps. The analysis module calculates the correlation coefficient between the production rate and the temperature difference, which is calculated using the Pearson product-moment correlation coefficient formula. The coefficient value ranges from negative one to positive one, and the greater the absolute value of the coefficient, the stronger the linear relationship. When adjusting the production parameter distribution in combination with the material flow rate information, the adjustment logic is based on the flow rate stability weight. If the material flow rate variation coefficient is less than a threshold value, the production rate weight is increased, otherwise the temperature difference influence is prioritized. The distribution uniformity evaluation is achieved by calculating the spatial or temporal dispersion of the production parameters. The dispersion index is the coefficient of variation, which is the ratio of the standard deviation to the mean. If the ratio is lower than a preset threshold value, such as zero point one, it is determined that the distribution is uniform. In a specific implementation, the production stability threshold value is derived from the statistical data of historical high-quality production batches. The threshold value is set to the historical median of the distribution uniformity index. The parameter distribution management unit compares the current distribution uniformity with the threshold value, and the matching degree is expressed as a percentage. The higher the percentage, the better the stability.

[0023] It can be understood that the distribution of the equipment control value and the process balance coefficient adopts a weighted average strategy, and the weighted weight is dynamically allocated based on the matching degree. When the matching degree is higher than 90%, the weight of the equipment control value is increased, and the weight of the process balance coefficient is reduced. The equipment control value includes motor speed set value, heater power gear, etc., and the process balance coefficient involves production rhythm coordination parameters, such as conveyor belt synchronization factor. In the weighted average calculation, the weight of each parameter is its contribution to stability, which is pre-calibrated through sensitivity analysis. The parameter distribution management unit finally outputs the parameter control set, which is a key-value pair data structure, with the key as the parameter name and the value as the allocated value. The set is sent to the downstream unit through an industrial communication protocol. Optionally, the parameter distribution management unit integrates a log recording function, which records all data processing steps and intermediate results for auditing and optimization.

[0024] In some embodiments, the real-time collection of clean operating state data involves multi-threaded data processing, with the main thread responsible for data reception and buffering, and the sub-threads performing outlier identification and filtering operations. Outlier identification uses the Isolation Forest algorithm, which trains time series data to generate anomaly scores, with scores above a threshold marked as outliers. The filtering algorithm can be replaced with Kalman filtering, which is suitable for dynamic system state estimation, but moving average is preferred due to its simplicity. Data integrity and consistency verification includes checksum checking and timestamp alignment, which ensures data packet transmission without loss and prevents different sensor data from being misplaced. The time window for segmented processing can be dynamically adjusted based on changes in production rhythm, such as shortening to ten minutes when production accelerates. After calculating statistical features, feature values are stored in a ring buffer, with the buffer size adapted to the maximum window requirement. The integration process involves feature value normalization and serialization, generating a clean operating state data stream.

[0025] In specific implementations, the analysis module of the parameter allocation management unit includes time series change analysis, which includes trend decomposition that splits the sequence into trend, periodic, and residual terms, with the trend term revealing the long-term change direction. The correlation calculation between production rate and temperature difference considers the lag effect, which is detected through cross-correlation analysis, with the maximum lag time set to five sampling points. When adjusting the production parameter distribution based on material flow rate, the distribution model uses a Gaussian mixture model that fits the joint probability distribution of parameters, with uniformity evaluation based on model overlap area. The production stability threshold update mechanism is based on machine learning, with the machine learning model training a classifier using historical data to predict whether the current state is stable. The matching degree calculation involves Euclidean distance measurement, with a smaller distance between the current distribution and the ideal distribution indicating a higher matching degree. It can be understood that the weight optimization in weighted average allocation is iterated through gradient descent, which minimizes allocation error. The parameter control set output format supports JSON, which is more commonly used due to its lightweight. Optionally, the parameter allocation management unit can be configured with an alarm function that triggers when data is abnormal or the matching degree is too low, notifying the operator to intervene.

[0026] In specific implementations, the filtering algorithm of the obtaining step of the clean operating state data smooths data fluctuations, and the smoothing strength is adjustable, and the smoothing strength parameter is set according to a noise level, and the noise level is estimated from historical data signal-to-noise ratio. The data integrity and consistency verification method includes range check and logical constraint, the range check ensures that the data is within the physical limit, and the logical constraint verifies the causal relationship between parameters, such as the production rate should rise when the flow rate increases. The statistical feature mean value calculation of the segmented processing uses weighted mean, and the weighted mean gives higher weight to recent data, and the variance calculation uses an unbiased estimator. The trend slope fitting uses robust regression, and the robust regression reduces the influence of outliers. When integrating the feature values to form the clean operating state data, the data compression algorithm such as Delta encoding reduces storage occupation. The time series change analysis of the analysis module of the parameter allocation management unit also includes mutation point detection, and the mutation point detection is based on the CUSUM algorithm, and the CUSUM algorithm identifies mean shift points. After calculating the correlation coefficient, the significance test uses t-test, and the correlation is considered significant when the p-value is less than zero point zero five. The spatial correlation is added to the distribution uniformity evaluation, and the spatial correlation is quantified by the Moran index, and the Moran index evaluates the aggregation degree of the geographical distribution of parameters.

[0027] It can be understood that the parameter allocation management unit has the operation closed-loop control characteristic, the closed-loop control is realized through feedback adjustment, and the feedback signal comes from the stability report of the downstream unit. After the device adjusts the numerical value allocation, the actual effect monitoring is realized through sensor feedback, and the feedback data is used to adjust the weight allocation strategy. The process balance coefficient optimization involves multi-objective optimization, and the multi-objective optimization balances production efficiency and quality indicators. The transmission of the parameter control set is encrypted to ensure industrial safety, and the encryption algorithm uses the AES standard. In specific implementations, the real-time collection frequency of the obtaining step of the clean operating state data is configurable, and the frequency is set according to the criticality of production, and high-frequency collection is used for critical process sections. The out-of-range point identification algorithm can be switched to DBSCAN clustering, and the DBSCAN clustering identifies density abnormal points. The integrity check of the data verification includes a heartbeat packet mechanism, and the heartbeat packet sends a confirmation signal periodically. The window overlap of the segmented processing is allowed, and the overlapping window increases data continuity. The statistical feature trend slope calculation supports polynomial fitting, and the polynomial fitting captures nonlinear trends. After integrating the feature values, the data quality score is output, and the score is based on feature consistency. The analysis module of the parameter allocation management unit supports plug-in algorithms, and the plug-in algorithms allow replacement of correlation calculation methods. The production parameter distribution adjustment rule can be edited, and the rule is configured through a graphical interface. The matching degree calculation integrates fuzzy logic, and the fuzzy logic handles uncertainty. The weight of the weighted average allocation is dynamically learned, and the dynamic learning uses a Q-learning algorithm. The version management of the parameter control set supports rollback, and the rollback deals with allocation failure scenarios.

[0028] Example 2: see Figure 3In a specific implementation, the tension optimization management unit receives a parameter control set from the parameter allocation management unit through a data interface, the parameter control set contains structured data such as device control values and process balance coefficients, the analysis module of the tension optimization management unit first analyzes the data format of the parameter control set, extracts the control instructions related to the weaving area, and these instructions are mapped to specific physical actuators such as tension roller motors or brakes. After analysis, the tension optimization management unit activates the sensor array deployed on the weaving area, which includes multiple high-precision tension sensors that collect real-time tension readings of the woven material on the transmission path at a fixed frequency, usually set to one hundred samples per second to ensure capturing dynamic changes. The tension reading sequence is stored in the cache area in timestamp order, forming a time series data set, while the unit calculates the gradient change trend, which is obtained by the difference value of adjacent sampling points, and the central difference method is used to reduce phase lag. In a specific implementation, the weaving area is divided into a uniform grid structure, the grid division is based on the physical layout of the production equipment, for example, the width direction is divided into ten grid units, and the length direction is divided by one unit per meter. The tension value of each grid point is calculated by an interpolation algorithm, which generates a continuous tension distribution map based on the measured data of adjacent sensors using the inverse distance weighted average method.

[0029] In some embodiments, the timing characteristic analysis of the tension reading sequence involves signal processing techniques, including periodicity, stability, and mutation characteristics. The analysis module performs a fast Fourier transform on the tension reading sequence to identify the dominant frequency component, which corresponds to the periodic characteristics of the device mechanical motion. The turning point detection of the gradient change trend uses the sliding window variance method, with a sliding window length of fifty sampling points. When the variance of the gradient values within the window exceeds twice the historical average variance, it is marked as a potential turning point, which needs to be verified by three consecutive windows to be confirmed. The device structure parameters include the diameter of the tension roller, the spacing, and the material elastic modulus, which are loaded from the device database for calculating the theoretical tension distribution. The calculation of tension distribution uniformity is based on the tension values of the grid points, and the uniformity is quantified using spatial statistical indicators, such as calculating the standard deviation and coefficient of variation of all grid point tensions. The coefficient of variation is the ratio of the standard deviation to the mean. The ideal distribution model is uniform distribution, i.e., the tension value of each point is equal to the average tension of the area, and the deviation is measured by the root mean square error. When introducing a time decay factor to correct the gradient change trend, the time decay factor is set to an exponential decay form, and the decay coefficient is related to the material relaxation time constant. Recent gradient data has a higher weight, and long-term data has a decreasing weight. The cumulative effect of tension fluctuations on production stability is calculated by integral summation, and the cumulative effect value is used to generate the uniformity index, which ranges from zero to one, with a higher value indicating a more uniform distribution.

[0030] In a specific implementation, the calculation step of tension distribution uniformity includes initializing a mesh model, the mesh model vertices corresponding to sensor positions, and the vertex tension values filled by real-time readings. The tension values of missing grid points are completed by bilinear interpolation, which calculates the internal point values using the data of four adjacent vertices. The standard deviation calculation uses the sample standard deviation formula, and the coefficient of variation is then derived. The deviation from the ideal distribution is compared using the relative error percentage, and a percentage lower than five percent is considered qualified. When the time decay factor corrects the gradient change trend, the decay factor function is: wherein: represents the decay factor, is the decay coefficient, is the difference between the current time and the data collection time.

[0031] The cumulative influence calculation is the cumulative sum of the product of the gradient absolute value and the decay factor. The uniformity index is finally mapped to the inverse of the coefficient of variation and normalized. According to the uniformity index, the control parameters of the tension device are dynamically adjusted, including the PID gain of the servo motor and the pressure set value of the brake. The adjustment strategy is proportional control, and the adjustment amount is proportional to the uniformity deviation. Redistributing the tension value is achieved by modifying the grid point target tension, which is adjusted by the uniformity index feedback to ensure balanced distribution. Iterative calculation uses the gradient descent method to minimize the difference between the uniformity index and the target value, with adaptive adjustment of the iteration step size until the stability condition is met. The output tension optimization set is the new parameter set of the tension device.

[0032] It can be understood that the operation of the tension optimization management unit is highly dependent on real-time data feedback, which is transmitted through an industrial Ethernet with a transmission delay of less than 10 milliseconds. The granularity of mesh division can be configured, and the refinement of the granularity improves the accuracy but increases the computational load. The interpolation algorithm may introduce errors in the sparse area of the sensor, and the errors are alleviated by increasing the redundant sensor. The fast Fourier transform in the timing characteristic analysis needs to process non-stationary signals, and the non-stationary signals are analyzed by short-time Fourier transform segmentation. The variance threshold of the turning point detection can be adaptively learned, and the adaptive learning is based on the historical turning point mode. The device structure parameter update mechanism supports dynamic loading, which should be adapted to the device modification scene. The uniformity index calculation can introduce a weight matrix, which gives higher importance to the key area. The decay coefficient of the time decay factor can be adjusted, and the decay coefficient is set according to the material type. The cumulative influence calculation ignores small fluctuations, which are removed by high-pass filtering. When dynamically adjusting the control parameters, the anti-over-tuning strategy uses saturation limitation to set the upper and lower boundaries of parameter changes. The execution of the tension value reassignment needs to synchronize multiple tension devices, and the synchronization protocol uses a precise clock synchronization protocol. The convergence condition of the iterative calculation can be set as the maximum number of iterations, which avoids infinite loop. The tension optimization set output contains a version identifier, which is used to track the adjustment history.

[0033] In some embodiments, the tension optimization management unit's analysis parameter control set module supports multiple data protocols, and the analysis module automatically identifies the protocol format. The tension reading collection of the sensor array may be affected by electromagnetic interference, which is suppressed by shielding lines and digital filtering. The calculation of the gradient change trend can use high-order difference, which improves the trend smoothness. The non-uniform mesh of the mesh division is allowed, and the non-uniform mesh is encrypted in the high gradient area. The inverse distance weighting exponent of the interpolation algorithm can be optimized, and the exponent optimization is based on cross-validation. The timing characteristic analysis integrates a machine learning model, which predicts tension drift. The turning point detection combines waveform matching, and the waveform matching template comes from typical abnormal patterns. The data verification of the device structure parameters includes range checking, which prevents parameter errors. The spatial autocorrelation evaluation of the uniformity index calculation is quantified by a geographic statistical method. The decay coefficient of the time decay factor is dynamically calibrated according to the change of the ambient temperature. The integral window of the cumulative influence calculation is variable, which adapts to the production rhythm. The safety interlock mechanism of the dynamic adjustment of the control parameters prevents device conflicts. The execution record of the tension value reassignment logs an audit log, which is used for fault tracing. The initial value setting strategy of the iterative calculation uses the last optimization result to accelerate convergence. The transmission of the tension optimization set encrypts the data security.

[0034] In a specific implementation, after the grid model initialization of the tension distribution uniformity calculation step, the model vertex data is updated in real time, and the update frequency is consistent with the acquisition frequency. The bilinear interpolation uses extrapolation method in the boundary area, and the extrapolation method assumes that the boundary gradient is zero. The standard deviation calculation ignores the null grid points, which are marked as invalid. The coefficient of variation calculation prevents division by zero error, which is avoided by adding a small offset. The ideal distribution model can be replaced by the stress optimal distribution, which is based on the material mechanics model. The time decay factor modifies the decay coefficient λ of the gradient change trend, which can be set in segments, corresponding to different production stages. The cumulative effect calculation uses the numerical integration method, which is the trapezoidal rule. The uniformity index is normalized to the zero interval using the minimum-maximum scaling. When dynamically adjusting the control parameters of the tension device, the parameter adjustment step is nonlinearly related to the uniformity deviation, with small deviation fine-tuning and large deviation fast tuning. The algorithm for redistributing tension values includes average distribution and weighted distribution, with the weighted distribution based on the importance of grid points. The gradient descent method of iterative calculation can be replaced by the Newton method, which converges faster but is more complex to calculate. The threshold value of the stability condition can be adaptively adjusted based on historical convergence data. The data structure of the tension optimization set contains a check code, which is used to verify the data integrity.

[0035] In a specific implementation, the dye conditioning management unit receives the tension optimization set from the tension optimization management unit through the industrial bus, which contains the optimized tension values of each grid point in the fabric area and the corresponding control parameters. The interface module of the dye conditioning management unit parses these data and converts them into input instructions for the dye control system. The dye conditioning management unit activates the optical monitoring system installed above the canvas surface, which uses a high-resolution spectral imager to capture the dye adsorption process on the canvas surface at a rate of thirty frames per second. The adsorption characteristics are quantified by analyzing the chroma change of the image pixels, specifically the growth rate of the dye coverage area per unit time. The diffusion pattern is calculated by tracking the movement trajectory of the dye front, and the trajectory data is filtered to generate the diffusion rate and direction vector. The environmental temperature data is collected by thermocouples distributed in the dye vat and canvas path, and the time factor is recorded by the system clock. The optical monitoring system stores the adsorption rate, diffusion range, and environmental temperature, time stamp in association, forming a time series database for analyzing the dynamic influence of temperature and time on the adsorption process.

[0036] In a specific implementation, the dynamic relationship between the dye input rate and the flow rate distribution ratio is calculated by an in-built algorithm module of the dye regulation management unit, the dye input rate refers to the volume of dye pumped into the dye vat per unit time, and the flow rate distribution ratio refers to the flow distribution ratio of the dye in multiple delivery branches. The algorithm module establishes a multiple regression model of the input rate and the flow rate distribution ratio, the regression model is trained with historical production data, and the model output predicts the dye coverage uniformity index. When adjusting the ratio parameter, the dye regulation management unit sends control signals to the metering pump and the proportional valve, the control signals are dynamically corrected based on the model prediction results, for example, when the prediction shows that the edge area is not covered enough, the flow rate distribution ratio of the corresponding branch is increased. The objective function of optimizing the dye coverage uniformity is defined as the minimization of the standard deviation of the dye concentration on the entire canvas surface, in the iterative optimization process, the algorithm module continuously simulates the dye distribution under different input rates, and compares it with the ideal uniform distribution, the ratio parameter is adjusted in the gradient descent direction until the coverage uniformity reaches the predetermined threshold. It can be understood that evaluating the trend of dye concentration distribution involves spatiotemporal data analysis, the trend of concentration distribution is obtained by fitting the spatiotemporal field of surface concentration measurements, the fitting method uses the Kriging interpolation method to generate a continuous concentration cloud map, and dynamic parameter values such as the dye viscosity compensation coefficient are corrected according to the trend slope, and the correction logic is to increase the viscosity compensation to suppress the flow when the trend slope exceeds the limit. The feedback loop calibration mechanism is realized by comparing the dye regulation output with the real-time readings of the optical monitoring system, and the parameter re-adjustment is triggered when the deviation exceeds the tolerance range, the dye regulation output is generated in the form of a parameter set and is delivered to the actuator.

[0037] In some embodiments, the optimization step of dye coverage uniformity is specifically implemented as follows: a dye distribution model is constructed based on fluid mechanics and mass transfer principles, the model discretizes the canvas surface into a finite number of calculation units, and the mass balance equation of each unit includes convection and diffusion terms. When simulating the coverage effect under different input rates, the model solves the concentration field under steady-state conditions, and the input rate boundary condition is defined by the metering pump set value. The target uniformity is defined as the coefficient of variation of all unit concentrations being less than zero point zero five, and the difference degree index is used to compare the simulation results with the target uniformity, the difference degree index is calculated as the root mean square error between the simulated concentration field and the target field. Adjusting the flow rate distribution ratio is achieved by modifying the flow set value of each branch, and the opening of the proportional valve is mapped to the flow value in a linear relationship. The iterative optimization uses the conjugate gradient method, which updates the simulation input after each iteration until the root mean square error converges. Recording parameter changes during the optimization process is completed by a log module, which stores the input rate, flow rate distribution ratio, simulation results and difference degree index of each iteration, and when updating the dye regulation output, the system selects the parameter combination with the smallest difference degree as the final output.

[0038] In practical implementation, the dye distribution model for optimizing dye coverage uniformity is solved using the finite volume method. The discrete equations of the finite volume method are integrated on the computational unit, and the boundary conditions include the zero-flux condition at the canvas edge. When simulating the coverage effect, the time step is set to 0.1 seconds, and the total simulation time covers the entire dyeing cycle. The coefficient of variation threshold for the target uniformity is configurable, and the threshold is adjusted according to the product grade. Spatial weights are introduced into the calculation of the difference index, giving higher importance to visually critical areas. The response time of the actuator adjusting the flow rate distribution ratio needs to be compensated; this compensation is achieved by sending instructions in advance through a predictive control algorithm. The conjugate gradient method for iterative optimization has a maximum iteration count of one hundred to prevent non-convergence. Differential encoding is used for data compression and storage in the log module to reduce storage space usage. It is understood that the operation of the dye adjustment management unit is highly dependent on model accuracy, which is maintained through periodic experimental data calibration. The calibration process involves comparing simulated values ​​with measured concentration values ​​and correcting the transfer coefficients in the model. The calibration cycle of the feedback loop is set to once every five seconds, synchronized with the frame rate of the optical monitoring system. The parameter set of the dye adjustment output includes a version number and a timestamp. The version number is used to track the parameter change history.

[0039] In practical implementation, the model parameter initialization for the dye coverage uniformity optimization step is based on material properties, including dye density and surface tension, which are loaded from the database. The finite volume method discretized mesh is aligned with the mesh of the tension optimization management unit, ensuring data consistency. The computational resource allocation for simulating coverage effects is adjustable, dynamically adjusted according to simulation complexity. A fuzzy tolerance can be introduced for comparing target uniformity, handling measurement uncertainties. The spatial weight matrix of the difference index is defined by an expert system trained on historical quality data. The proportional valve control signal for adjusting the flow rate distribution ratio uses pulse width modulation, improving control accuracy. The convergence condition for iterative optimization combines absolute and relative errors, with the absolute error set at a concentration difference of less than 0.1 gram per square meter. Data stored in the log module can be exported to a standard format for offline analysis. The dye adjustment management unit has a fault detection function, alarming when the model deviates continuously from the measured values. A feedback loop calibration parameter learning function uses recursive least squares to optimize model coefficients. Encrypted transmission of dye adjustment outputs ensures process security.

[0040] In practical implementation, the convection velocity field of the dye distribution model in the optimization step of dye coverage uniformity is derived from the tension optimization set. The velocity field is linearly related to the fabric tension distribution, and the relationship is as follows: in: This represents the dye flow velocity vector at cell (i,j). It is the velocity-tension coupling coefficient. This represents the tension value at unit (i,j).

[0041] The diffusion term coefficient is determined by the dye properties and is experimentally measured. The initial condition for simulating coverage is a uniform concentration field with an initial concentration of zero. Statistical hypothesis testing is used to compare the uniformity of the target field, verifying the consistency between the simulated and target field distributions. The optimization variables for adjusting the flow rate distribution ratio are constrained within physical limits, handled using the projected gradient method. A linear search strategy is used for the step size selection in iterative optimization, ensuring the objective function decreases. Real-time data stream processing in the log module uses a sliding window to aggregate optimization history. The dye adjustment management unit employs a model update mechanism, triggering recalibration when the dye batch is changed. A delay compensation algorithm for the feedback loop estimates system response lag and adjusts it in advance. Digital signature verification of the dye adjustment output prevents parameter tampering.

[0042] Example 4: In specific implementation, the anomaly handling management unit receives dye adjustment output from the dye adjustment management unit via a data interface. This output includes dye input rate, flow rate distribution ratio, and concentration distribution parameters. The anomaly handling management unit immediately activates the temperature sensor array and tension sensor network. The temperature sensor array measures the temperature values ​​of the dyeing vat environment and the canvas surface at a sampling frequency of fifty times per second. The temperature fluctuation rate is obtained by calculating the ratio of the absolute value of the temperature difference between adjacent sampling points to the time interval. The tension offset is calculated by comparing the real-time tension reading with the target tension value in the tension optimization set; the offset is defined as the absolute difference. The real-time collected temperature fluctuation rate and tension offset data are cached in a circular buffer with a capacity of the most recent 1,000 sampling points to ensure data continuity. The calculation module of the anomaly handling management unit then processes this data, calculating the statistical characteristics of the fluctuation range. These characteristics include the moving average of the temperature fluctuation rate, the standard deviation of the tension offset, and the fluctuation range. The moving average window size is 100 sampling points, the standard deviation is calculated using an unbiased estimation formula, and the range is the difference between the maximum and minimum values. These statistical characteristics are used to quantify the stability level of the production process.

[0043] In practical implementation, the impact of fluctuation characteristics on canvas dyeing rate is analyzed using multiple regression analysis. The multiple regression model is trained based on historical production data. The model input variables include the moving average of temperature fluctuation rate, the standard deviation of tension offset, and the fluctuation range. The output variable is the percentage change in dyeing rate. The dyeing rate is calculated by measuring the change in canvas color depth per unit time using an optical sensor, and the percentage change is standardized relative to the baseline rate. The degree of impact is assessed by calculating the regression coefficient of each fluctuation characteristic; the larger the absolute value of the regression coefficient, the more significant the impact. When setting an anomaly threshold and triggering the intervention mechanism, the anomaly threshold is determined based on statistical process control principles. The threshold is divided into two levels: a warning threshold and an action threshold. The warning threshold is ±2 standard deviations of the historical normal fluctuation range, and the action threshold is ±3 standard deviations. When the real-time fluctuation characteristic exceeds the warning threshold, the system logs and increases the monitoring frequency; when it exceeds the action threshold, the intervention mechanism is triggered immediately. The intervention mechanism includes automatically adjusting the dye distribution path and the temperature-tension ratio. The dye distribution path is modified by controlling the switching sequence of the dye nozzles. The temperature-tension ratio is the ratio of temperature to tension value, and the adjustment target is to stabilize the ratio within the optimal range. The dynamic adjustment process continuously corrects parameters based on real-time data feedback. The correction algorithm employs proportional-integral-derivative (PID) control, with the PID control parameters adaptively tuned according to fluctuation characteristics. When generating the anomaly handling set, the set includes the adjusted path parameters, temperature-tension ratio values, and intervention timestamps.

[0044] In some embodiments, the data preprocessing steps for real-time acquisition of temperature fluctuation rate and tension offset during production include signal filtering and outlier removal. Signal filtering uses a low-pass Butterworth filter with a cutoff frequency set to 5 Hz to suppress high-frequency noise. Outlier removal is based on a box plot method, where data points outside the upper and lower bounds of the box plot are marked and replaced with nearest-neighbor interpolations. After calculating the statistical characteristics of the fluctuation range, the characteristic values ​​are stored in a time-series database, which supports fast querying and trend analysis. When analyzing the impact of fluctuation characteristics on the canvas dyeing rate, the impact score is calculated using the following formula: in: The score indicates the degree of influence. The moving average representing the temperature fluctuation rate. The standard deviation of the tension offset is represented by... Indicates extremely poor volatility. , , These are weighting coefficients, derived from historical data through principal component analysis. A score I above a threshold is considered a significant influence. Refer to Table 1 for the threshold table for setting anomaly thresholds and triggering intervention mechanisms.

[0045] Table 1: Threshold for Abnormalities and Triggering Intervention Mechanisms Table 1 is stored in the configuration file and loaded when the system starts. After the intervention mechanism is triggered, the logic for dynamically adjusting the dye distribution path is based on a graph search algorithm. The graph search algorithm models the dye path as a directed graph, with nodes corresponding to nozzle positions and edge weights dynamically updated according to the temperature-tension ratio. The shortest path algorithm recalculates the dye flow sequence. The temperature-tension ratio is adjusted by modifying the setpoints of the heater and tension device, and the step size of the setpoint change is proportional to the degree of influence score.

[0046] In practical implementation, the anomaly handling management unit operates with a highly integrated real-time control loop, with the loop period set to 100 milliseconds to ensure rapid response. Temperature fluctuation rate calculation considers spatial distribution, obtained through a weighted average of multiple sensor points, with weights assigned based on the proximity of the sensors to the canvas. Tension offset monitoring includes trend prediction using an autoregressive integral moving average model, whose parameters are learned online. The calculation of fluctuation range statistical characteristics supports rolling updates, recalculating feature values ​​each time new data arrives. The multivariate regression model analyzing the degree of influence is periodically retrained every 24 hours, updating coefficients with the latest production data. Dynamic adjustment functions for warning and action thresholds are included, automatically scaling the threshold range based on seasonal factors or material batches. Trigger conditions for intervention mechanisms are configurable, with a configuration interface allowing operators to customize threshold levels. A graph search algorithm optimizes path length and uniformity for adjusting dye distribution paths, using Pareto front selection for multi-objective optimization. Temperature-tension ratio control incorporates feedforward compensation, predicting external disturbances and acting in advance. The adaptive tuning of the proportional-integral-derivative control parameters is based on the Ziegler-Nichols method, which allows for online gain tuning. The generation of the exception handling set includes digital signatures, which ensure data integrity and source authentication.

[0047] Optionally, real-time data verification includes CRC checksum, ensuring error-free transmission. The moving average window size for calculating statistical features is adjustable, dynamically set according to production rhythm. Weighting coefficients α, β, and γ for influence scores can be manually overridden for specific process requirements. The anomaly threshold table supports multi-version management, allowing for retrospective setting of historical thresholds. Intervention mechanisms include trigger delay measurement, using a high-precision clock to calibrate response time. The dye distribution path adjustment algorithm can be switched to a genetic algorithm, handling complex path optimization. Temperature-tension ratio adjustments are limited to a safe range defined by equipment specifications. A proportional-integral-derivative control integral term anti-saturation mechanism prevents integral windup. The anomaly handling set's transmission protocol supports redundancy backup, improving system reliability.

[0048] See Figure 4 In the anomaly handling phase of the intelligent management system for canvas production, the intervention effect of temperature fluctuation rate is quantitatively analyzed through comparisons at different operational stages. Specifically, temperature fluctuation rate is defined as the ratio of the absolute value of the temperature difference between adjacent sampling points to the time interval, and its value characterizes the dynamic stability of the dyeing vat environment and the canvas surface temperature. The chart uses a bar graph to distinguish the temperature volatility before intervention (red bars) and after intervention (blue bars): During the "normal operation" phase, the temperature volatility before and after intervention was 0.32℃ / second, within the historical normal fluctuation range (baseline state); when the "warning threshold" was triggered, the temperature volatility before intervention rose to 0.65℃ / second, exceeding the warning threshold (±0.5℃ / second). After intervention, by adjusting the temperature expansion ratio and dye path, the volatility decreased to 0.42℃ / second, returning to the normal range; when the "action threshold" was reached and a strong intervention was triggered, the temperature volatility before intervention further climbed to 1.15℃ / second (far exceeding the action threshold ±1.0℃ / second), and was quickly adjusted to 0.35℃ / second after intervention; during the "post-intervention recovery" phase, the temperature volatility remained stable at 0.28℃ / second, consistent with the pre-intervention level. The threshold division in this figure (with dashed lines indicating a warning threshold of 0.5℃ / second and an action threshold of 1.0℃ / second) is based on the principle of statistical process control. By comparing the changes in volatility before and after intervention, it intuitively reflects the effectiveness of the proportional-integral-derivative control and path adjustment strategy in the anomaly handling management unit, and verifies the intervention mechanism's ability to regulate temperature stability.

[0049] Example 5: In specific implementation, the data acquisition module of the anomaly handling management unit first retrieves normal production data from the factory's historical database for the past twelve months. Normal production data refers to the production process records corresponding to products judged as qualified by the quality assessment system. Data fields include temperature fluctuation rate time series, tension offset curve, and corresponding equipment status logs. After preprocessing, the historical data is divided by production batch, with each batch constituting a sample. The total number of samples usually exceeds one thousand to ensure model reliability. The fluctuation range model is trained using a Gaussian mixture model algorithm, which decomposes multidimensional data into a weighted sum of multiple Gaussian distributions. The training process iteratively solves for model parameters using the expectation-maximization algorithm, including the mean vector, covariance matrix, and mixing coefficients of each Gaussian component. After training, the normal range is defined as the data region where the probability density function value is greater than a threshold. The threshold is set as the probability density quantile that covers 95% of the normal samples, specifically calculated using the integral probability density function. The normal range of temperature fluctuation rate is represented as the interval between the mean and three standard deviations. The normal range of tension offset is derived through a joint probability distribution, forming a multidimensional confidence ellipse.

[0050] In practice, an intervention process is initiated when real-time data exceeds the normal range. Real-time data comes from continuous readings from online sensors, with the data stream updating every 100 milliseconds. The system combines the current temperature fluctuation rate and tension offset into a feature vector. This feature vector is input into a trained Gaussian mixture model to calculate the log-likelihood value. An anomaly is identified when the log-likelihood value is below a threshold. The initiation of the intervention process immediately triggers a system interruption of the current control loop. The interruption mechanism is implemented via hardware signals with a response time of less than ten milliseconds. Adjusting the dye path first analyzes the anomaly type, categorized by the direction of the feature vector's deviation from the normal range: if the temperature fluctuation rate deviates positively while the tension offset deviates negatively, it is determined to be overheating combined with insufficient tension. In this case, the intervention logic is to reduce the flow distribution in the high-temperature region of the dye path. The dye path adjustment is accomplished by reconstructing the nozzle control sequence. The control sequence is replanned based on a directed graph model, where nodes represent nozzles, and edge weights are dynamically updated according to the real-time temperature-tension ratio. The path search uses Dijkstra's algorithm to find the optimal coverage path. The adjustment target for the temperature-tension ratio is to bring the ratio back to the median of historical normal values. The adjustment amount is calculated using a proportional-integral controller, with the proportional coefficient and integral time adaptively adjusted according to the degree of anomaly. Intervention logs are recorded in a structured format, with log entries including timestamps, anomaly feature vectors, details of intervention actions, and parameter comparisons before and after adjustment. Logs are written to blockchain storage in real time to ensure immutability. When updating the anomaly handling set, the system encapsulates the effective parameters of this intervention into a new parameter package, which is distinguished by a version number and broadcast to downstream units.

[0051] In some embodiments, the process of training a fluctuation range model based on historical production data includes data cleaning and feature engineering. Data cleaning removes invalid records caused by sensor malfunctions, and feature engineering constructs time-series features such as sliding window statistics. The number of components in the Gaussian mixture model is determined using the Bayesian information criterion, which balances model complexity and goodness of fit. Threshold optimization within the normal range employs cross-validation, which divides the data into training and test sets to adjust threshold parameters. A latency compensation mechanism is introduced in the real-time data comparison stage, taking into account the transmission latency from the sensor to the controller. The intervention process is initiated with a safety confirmation step, which verifies the consistency of multiple sensor readings. The algorithm for adjusting the dye path supports a gradual adjustment mode, which avoids production fluctuations caused by sudden parameter changes. The control of the temperature-tension ratio introduces a feedforward compensation term, which predicts the impact of external disturbances. The blockchain storage of intervention logs adopts a lightweight node mode, which stores only hash values ​​to reduce resource consumption. Version management of the anomaly handling set supports a rollback function, which restores the system to the previous version when the intervention effect is unsatisfactory.

[0052] It is understandable that a regular update mechanism for the fluctuation range model is crucial. The update cycle is set to a full retraining every seven days, using the latest data within the sliding time window. A buffer zone is set for real-time data exceeding the normal range, with the buffer being a transition band of ±5% around the threshold to avoid frequent switching. The intervention process has tiered access control, allowing different levels of automation for different anomaly levels. The optimization objective for adjusting the dye path involves multi-objective trade-offs, considering uniformity, efficiency, and energy consumption simultaneously. The temperature-to-tension ratio adjustment curve is smoothed using a moving average filter. The intervention log analysis function integrates visualization tools, which generate anomaly trend maps. The transmission of the anomaly processing set uses differential encoding, which only sends the changes to improve efficiency.

[0053] In practice, the feature standardization process for training the fluctuation range model using historical production data employs Z-score normalization, which ensures that the mean of each feature is zero and the variance is one. The covariance matrix of the Gaussian mixture model is constrained to a diagonal matrix, which reduces the number of parameters and prevents overfitting. The boundary of the normal range is calculated using the following formula: in: Indicates the boundary of the normal range. Represents the historical mean of the feature. It is the coverage factor. Indicates the historical standard deviation of the feature. Indicates the number of training samples. Coverage factor. The sample size can be adjusted according to quality requirements. Impact on boundary tightness. Real-time data comparison uses Mahalanobis distance, which calculates the distance between the current feature vector and the mean of each component of the Gaussian mixture model. The intervention process has a delayed trigger function, activated only if the anomaly persists for more than three seconds. Adjusting the cost function of the dye path introduces a weight matrix, which assigns higher priority to critical areas. An anti-saturation mechanism is added to the temperature-tension ratio control loop to prevent excessive accumulation of integral terms. Intervention log storage supports multiple backups, improving data security. The validation of the anomaly handling set includes boundary checks to ensure parameters remain within physically feasible ranges.

[0054] Optionally, historical production data training can incorporate transfer learning techniques, which accelerate training by utilizing model parameters from similar production lines. Real-time data exceeding normal range detection supports multi-scale analysis, simultaneously monitoring short-term spikes and long-term drift. A manual intervention interface for the intervention process allows operators to override automated decision-making. A simulation verification stage for adjusting the dye path tests the path's effectiveness in a virtual environment. Adaptive tuning of the temperature-tension ratio employs fuzzy logic control, which handles nonlinear relationships. A compressed storage algorithm for intervention logs reduces storage space usage. An encrypted transmission protocol for the exception handling set prevents process parameter leakage. In some embodiments, an incremental learning function for the fluctuation range model updates model parameters online upon acquiring new data. Cache optimization for real-time data comparison preloads frequently used data to reduce latency. Redundancy design for the intervention process improves reliability through a backup controller. Real-time rendering of the adjusted dye path helps monitoring personnel intuitively understand the adjustment's effect. A fail-safe mode for the temperature-tension ratio enables default values ​​when sensors fail. Automatic analysis reports of the intervention logs periodically generate intervention effectiveness assessments. The exception handling set uses digital signature verification, which ensures that the parameter source is trustworthy.

[0055] See Figure 5In the temperature-elevation ratio control process of the anomaly handling management unit, the dynamic adjustment of the PI controller relies on a phased control strategy. Specifically, the initial value of the actual temperature-elevation ratio (blue curve) is significantly higher than the target temperature-elevation ratio (red dashed line). The system first enters a rapid adjustment phase (pink area): by increasing the proportional gain of the PI controller, the actual temperature-elevation ratio decreases rapidly, and the actual ratio error band (purple shading) narrows synchronously with the decrease in deviation. After entering the stable adjustment phase (yellow area), the controller switches to a smaller proportional gain and a longer integral time, and the actual temperature-elevation ratio approaches the target value with smaller fluctuations, further compressing the error band. Finally, it enters the stable operation phase (light green area), where the controller maintains a low gain state, the actual temperature-elevation ratio oscillates slightly around the target value, and the error band stabilizes within a very small range. During parameter configuration, the proportional gain of the rapid adjustment phase is set to 3 times that of the stable phase, and the integral time is half that of the stable phase. On the adjustment timeline, the rapid adjustment phase lasts approximately 15 seconds, the stable adjustment phase lasts approximately 30 seconds, and then the system enters the stable operation phase.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A canvas production intelligent management system, characterized in that, The system comprises: The parameter distribution management unit acquires the running state data of the canvas production equipment, processes the production rate indicators, temperature difference readings and material flow rate information, distributes the equipment control values and process balance coefficients by calculating the correlation between the production parameter distribution and production stability, and outputs the parameter control set; The tension optimization management unit receives the parameter control set, measures the tension readings and gradient change trend in the weaving area, evaluates the effect of tension device power output on production stability, corrects the tension distribution balance, and generates a tension optimization set; The dye adjustment management unit detects the adsorption characteristics and diffusion mode of the dye on the canvas surface based on the tension optimization set, adjusts the dye input rate and flow rate distribution ratio, reconfigures the dye concentration distribution and dynamic parameters, and generates a dye adjustment output; The abnormality processing management unit uses the dye adjustment output to monitor the temperature fluctuation data and tension deviation information in the production process, analyzes the influence of the fluctuation range on the dyeing rate, dynamically calibrates the dye path and temperature-tension ratio, and forms an abnormality processing set; The quality evaluation management unit investigates the canvas quality distribution characteristics and production time data based on the abnormality processing set, adjusts the production path parameters, and generates a quality detection report.

2. The intelligent management system for canvas production according to claim 1, wherein, The operation of the parameter distribution management unit comprises: Collect the running state data of the canvas production equipment, filter the production rate indicators, temperature difference readings and material flow rate information, remove outliers and normalize the data format to obtain clean running state data; Analyze the time series change of the clean running state data, calculate the correlation coefficient between the production rate and temperature difference, adjust the production parameter distribution combined with the material flow rate, and evaluate the distribution uniformity; According to the distribution uniformity, compare the production stability threshold value, determine the matching degree of production parameters and stability, distribute the equipment control values and process balance coefficients by weighted average, and output the parameter control set.

3. The intelligent management system for canvas production according to claim 2, wherein, The acquisition step of the clean running state data comprises: Collect the running state data of the canvas production equipment in real time, identify outliers in the production rate indicators, temperature difference readings and material flow rate information, smooth data fluctuations by applying a filtering algorithm, and verify data integrity and consistency; Segment the verified data, calculate the statistical characteristics in each time window, including mean, variance and trend slope, and integrate the characteristic values to form clean running state data.

4. The intelligent management system for canvas production according to claim 3, wherein, The operation of the tension optimization management unit comprises: Analyze the parameter control set, extract the tension reading sequence and gradient change trend in the equipment weaving area, divide the area grid and calculate the tension value of each grid point; Analyze the time series characteristics of the tension reading sequence, detect the turning points of the gradient change trend, and calculate the tension distribution uniformity combined with the equipment structure parameters; According to the tension distribution uniformity, adjust the tension device power output parameters, optimize the tension distribution balance, and iteratively calculate until the stability condition is met to output the tension optimization set.

5. The intelligent management system for canvas production according to claim 4, wherein, The calculation step of the tension distribution uniformity comprises: Based on the tension reading sequence, calculate the standard deviation and coefficient of variation of the tension at each point in the area, and compare the deviation from the ideal distribution; The time decay factor is introduced to correct the gradient change trend, evaluate the cumulative impact of tension fluctuation on production stability, and generate a uniformity index; according to the uniformity index, the control parameters of the tension device are dynamically adjusted, the tension value is redistributed, and the distribution balance is ensured.

6. The intelligent management system for canvas production according to claim 5, wherein, The operation of the dye adjustment management unit includes: Receiving the tension optimization set, monitoring the dye adsorption rate and diffusion range on the canvas surface, recording the influence of environmental temperature and time factors on the adsorption process; Calculate the dynamic relationship between the dye input rate and the flow rate distribution ratio, adjust the ratio parameter to optimize the dye coverage uniformity; evaluate the dye concentration distribution trend, correct the dynamic parameter value, and calibrate the dye adjustment output through feedback loop.

7. The intelligent management system for canvas production according to claim 6, wherein, The optimization step of the dye coverage uniformity includes: According to the dye adsorption rate and diffusion range data, establish a dye distribution model to simulate the coverage effect under different input rates; compare the simulation results with the target uniformity, adjust the flow rate distribution ratio, and iterate optimization until the coverage uniformity meets the standard; record the parameter changes in the optimization process and update the dye adjustment output.

8. The intelligent management system for canvas production according to claim 7, wherein, The operation of the abnormal processing management unit includes: Using the dye adjustment output, real-time collection of temperature fluctuation rate and tension offset in the production process, calculation of statistical characteristics of fluctuation range; analyze the influence degree of fluctuation characteristics on canvas dyeing rate, set abnormal threshold and trigger intervention mechanism; dynamically adjust dye distribution path and temperature-tension ratio, correct parameters according to real-time data feedback, and generate abnormal processing set.

9. The intelligent management system for canvas production according to claim 8, wherein, The setting of abnormal threshold and triggering of intervention mechanism includes: Based on historical production data, train fluctuation range model to determine the normal range of temperature fluctuation rate and tension offset; when the real-time data exceeds the normal range, start the intervention process, adjust the dye path and temperature-tension ratio, record the intervention log and update the abnormal processing set.

10. A canvas production intelligent management method applied to the canvas production intelligent management system according to any one of claims 1 to 9, characterized in that, The method includes: Obtain the running state data of the canvas production equipment, process the production rate index, temperature difference reading and material flow rate information, calculate the correlation between production parameter distribution and production stability, distribute the equipment control value and process balance coefficient, and output the parameter control set; Receive the parameter control set, measure the tension reading and gradient change trend in the weaving area, evaluate the effect of tension device power output on production stability, correct the tension distribution balance, and generate the tension optimization set; According to the tension optimization set, detect the adsorption characteristics and diffusion mode of the dye on the canvas surface, modulate the dye input rate and flow rate distribution ratio, reconfigure the dye concentration distribution and dynamic parameters, and generate the dye adjustment output; Using the dye adjustment output, monitor the temperature fluctuation data and tension offset information in the production process, analyze the influence of fluctuation range on dyeing rate, dynamically calibrate the dye path and temperature-tension ratio, and form the abnormal processing set; Based on the abnormal processing set, investigate the canvas quality distribution characteristics and production time data, adjust the production path parameters, and generate the quality detection report.

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