Multi-process cooperative control system and method based on open-width processing process chain

Abstract

The invention belongs to the technical field of fabric process chain control, and particularly relates to a multi-process cooperative control system and method based on an open-width processing process chain. According to the method, the downstream is compensated in advance through disturbance source identification and feed-forward prediction, response lag and overshoot caused by disturbance transmission are remarkably reduced, the cloth cover is restored to a target state more quickly, and full-line tension fluctuation and breadth deviation are effectively reduced by coupling feed-forward and feedback and combining self-adaptive control of fabric humidity and temperature weighting; the defect rates of breadth breaking, wrinkling, edge warping and the like are reduced, the qualified rate and consistency of finished products are improved, a good control effect can be kept under different raw material, humidity and temperature conditions based on dynamic weight adjustment of historical data and fabric states, manual intervention and frequent parameter resetting are reduced, a disturbance source positioning mechanism can rapidly indicate the problem occurrence procedure, and the working efficiency is improved. And maintenance and root cause analysis are facilitated.

Description

Technical Field

[0001] This invention belongs to the field of fabric process chain control technology, specifically relating to a multi-process collaborative control system and method based on a flat-width processing process chain. Background Technology

[0002] In the textile processing industry, the flat-width processing chain typically includes multiple continuous steps such as desizing, scouring, bleaching, setting, stretching, and drying. These steps form a continuous production chain through fabric transfer, during which the fabric is affected by various process parameters such as tension, temperature, humidity, and speed. Due to the strong coupling between these steps, even minor fluctuations in upstream parameters can be transmitted to downstream steps, causing uneven fabric tension, width variations, or wrinkling, ultimately leading to a decline in the flat-width quality of the fabric.

[0003] Traditional process control methods typically employ a single-process independent control strategy, where fabric tension or width parameters are independently detected within each process unit and adjusted through local feedback loops. However, this approach fails to adequately consider the dynamic coupling relationships between processes, cannot effectively compensate for upstream disturbances, and is prone to control lag or over-adjustment, resulting in accumulated tension fluctuations and amplified width errors throughout the entire production line. Furthermore, it generally lacks information fusion mechanisms between multiple processes, making it impossible to perform global optimization control based on changes in the fabric's state across different processes. When fabric humidity, temperature, or tension distribution is uneven, local adjustments in a single process are insufficient to achieve overall fabric consistency, affecting the stability of flat-width processing and the quality of the finished product. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-process collaborative control system and method based on the flat fabric processing chain, which can realize the overall dynamic coordination control of the process chain, reduce tension fluctuations and width deviations, and improve the flat fabric processing quality and production efficiency.

[0005] The specific technical solution adopted by this invention is as follows: A multi-process collaborative control method based on a flat-panel processing chain includes: Real-time acquisition of fabric surface status parameters from each process unit, including warp tension value, weft width value and running speed value of the fabric, and acquisition of fabric abnormal parameters based on the fabric surface status parameters; Based on abnormal fabric parameters, identify the source process in the current process chain that will cause disturbance to downstream processes; Obtain the disturbance prediction value of the source process to the downstream process, and generate feedforward control instructions for the control of the downstream process based on the disturbance prediction value; Based on the deviation between the downstream fabric state parameters of the downstream process and the set target obtained from the feedforward control command, feedback control command is generated. Obtain fabric condition information from the source process, including moisture content and temperature. Obtain a comprehensive weight based on the fabric condition information, including tension weight and width weight. By integrating feedforward control commands and feedback control commands, and obtaining coordinated control commands based on tension fluctuations, width deviations, and overall weights, the coordinated control commands are sent to the actuators of the corresponding process units to adjust process parameters.

[0006] In a preferred embodiment, fabric surface condition parameters from each process unit are acquired in real time. These parameters include the warp tension, weft width, and running speed of the fabric. Abnormal fabric surface parameters are then obtained based on these parameters, including: The fabric surface condition parameters from each process unit are acquired in real time. These parameters include the warp tension value, weft width value, and running speed value of the fabric. Obtain the baseline safety status parameters, which include the baseline safety warp tension value range, the baseline safety weft width value range, and the baseline safety operating speed value range of the fabric; Determine whether the fabric condition parameters exceed the baseline safety condition parameters; If all values ​​in the fabric condition parameters are within the corresponding value range in the baseline safety condition parameters, then each process control is considered normal. If all values ​​in the fabric condition parameters are outside the corresponding value range in the baseline safety condition parameters, the corresponding process control is determined to be abnormal. Values ​​outside the corresponding value range in the baseline safety condition parameters are marked as the corresponding abnormal values ​​and summarized as fabric abnormal parameters. Among them, fabric abnormal parameters include warp tension abnormal values, weft width abnormal values, and running speed abnormal values.

[0007] In a preferred embodiment, based on fabric anomaly parameters, the source processes in the current process chain that will disrupt downstream processes are identified, including: Obtain the reference fabric parameters, which include the reference warp tension value, the reference weft width value, and the reference running speed value; The corresponding process disturbance coefficient is obtained based on the abnormal fabric parameters and the reference fabric parameters. Obtain the fabric summary dataset corresponding to the abnormal fabric parameters in the current process chain. The fabric summary dataset includes summary warp tension data corresponding to abnormal warp tension values, summary weft width data corresponding to abnormal weft width values, and summary running speed data corresponding to abnormal running speed values. The summary warp tension data includes multiple summary warp tension values, the summary weft width data includes multiple summary weft width values, and the summary running speed data includes multiple summary running speed values. Obtain the corresponding state change coefficients based on the fabric summary dataset; The comprehensive disturbance value is obtained based on the corresponding process disturbance coefficient and the corresponding state change coefficient; Identify the source process in the current process chain that will cause disturbances to downstream processes based on the comprehensive disturbance value.

[0008] In a preferred embodiment, the source process in the current process chain that will cause disturbances to downstream processes is identified based on the overall disturbance value, including: Obtain the source table set, which includes the meridional source table, the latitudinal source table, and the velocity source table. Each source table includes multiple disturbance comprehensive value intervals, as well as the disturbance process and the corresponding disturbance level for each disturbance comprehensive value interval. Based on the disturbance comprehensive value range corresponding to the disturbance comprehensive value, obtain the corresponding disturbance process and the corresponding disturbance level from the source table set; The disturbance levels corresponding to the disturbance processes are sorted in descending order to obtain a disturbance sorting table; Select the first disturbing process from the disturbance ranking list; this is the source process in the current process chain that will cause disturbances to downstream processes.

[0009] In a preferred embodiment, the disturbance prediction value of the source process on the downstream process is obtained, and based on the disturbance prediction value, feedforward control instructions for controlling the downstream process are generated, including: Obtain the current fabric state parameters, historical fabric state parameters, and source weights of the source process. The current fabric state parameters include the current warp tension value, the current weft width value, and the current running speed value. The historical fabric state parameters include multiple historical warp tension values, multiple historical weft width values, and multiple historical running speed values. The source weights include warp weights, weft weights, and speed weights. The disturbance prediction value is obtained based on the current fabric state parameters, historical fabric state parameters and source weights; Obtain the feedforward control table, which includes multiple disturbance prediction value intervals and the corresponding feedforward control command for each disturbance prediction value interval; Based on the disturbance prediction value range corresponding to the disturbance prediction value, the feedforward control instructions for downstream process control are obtained from the feedforward control table.

[0010] In a preferred embodiment, the deviation between the downstream fabric state parameters of the downstream process and the set target is obtained based on the feedforward control command, and a feedback control command is generated, including: Control commands for downstream processes are adjusted based on feedforward control commands; After obtaining the control command for adjusting the downstream process, the fabric surface state parameters are obtained and marked as downstream fabric surface state parameters. The downstream fabric surface state parameters include the downstream warp tension value, the downstream weft width value, and the downstream running speed value. Obtain the fabric surface condition parameters of the target in the downstream process, wherein the fabric surface condition parameters of the target include the target warp tension value, the target weft width value, and the target running speed value. The deviation of the fabric condition is obtained based on the downstream fabric condition parameters and the target fabric condition parameters; Obtain the feedback control table, which includes multiple deviation ranges and the corresponding feedback control instructions for each deviation range; The feedback control command is obtained from the feedback control table based on the deviation range corresponding to the deviation.

[0011] In a preferred embodiment, fabric state information from the source process is obtained, including moisture content and temperature. A comprehensive weight is then obtained based on this fabric state information, comprising tension weight and width weight, including: Obtain fabric condition information from the source process, including moisture content and temperature; The humidity-temperature ratio is obtained based on moisture content and temperature. Obtain the fabric summary table, which includes multiple moisture temperature ratio ranges and the corresponding comprehensive weight for each moisture temperature ratio range. The comprehensive weight includes tension weight and width weight. The corresponding comprehensive weight is obtained from the fabric comprehensive table based on the humidity temperature ratio range corresponding to the humidity temperature ratio.

[0012] In a preferred embodiment, feedforward control commands and feedback control commands are integrated, and coordinated control commands are obtained based on overall tension fluctuations, width deviations, and comprehensive weights. These coordinated control commands are then sent to the actuators of the corresponding process units to adjust process parameters, including: By combining feedforward control commands and feedback control commands, the overall tension fluctuation and width deviation of the fabric can be obtained. The fabric synergy value is obtained based on overall tension fluctuation, width deviation, and comprehensive weighting. Obtain the fabric coordination table, which includes multiple fabric coordination value ranges and the corresponding coordination control instructions for each fabric coordination value range; The corresponding coordination control command is obtained from the fabric coordination table based on the fabric coordination value range corresponding to the fabric coordination value. The collaborative control command is sent to the actuator of the corresponding process unit to adjust the process parameters.

[0013] The present invention also provides a multi-process collaborative control system based on a flat-panel processing technology chain, used in the aforementioned multi-process collaborative control method based on a flat-panel processing technology chain, comprising: The fabric status module is used to acquire fabric status parameters from each process unit in real time. The fabric status parameters include the warp tension value, weft width value and running speed value of the fabric. Abnormal fabric parameters are obtained based on the fabric status parameters. The source process module is used to identify the source processes in the current process chain that will cause disturbances to downstream processes based on abnormal fabric parameters. The feedforward instruction module is used to obtain the disturbance prediction value of the source process to the downstream process, and generate feedforward control instructions for the control of the downstream process based on the disturbance prediction value. The feedback instruction module obtains the deviation between the downstream fabric state parameters of the downstream process and the set target based on the feedforward control instruction, and generates feedback control instructions. The comprehensive weighting module is used to obtain fabric state information from the source process, including moisture content and temperature. Based on the fabric state information, a comprehensive weight is obtained, which includes tension weight and width weight. The process adjustment module integrates feedforward control commands and feedback control commands, and obtains collaborative control commands based on tension fluctuations, width deviations, and overall weights. These collaborative control commands are then sent to the actuators of the corresponding process units to adjust the process parameters.

[0014] And, a multi-process collaborative control terminal based on a flat-panel processing chain, comprising: One or more processors; A storage device on which one or more programs are stored; When one or more programs are executed by one or more processors, the one or more processors implement a multi-process collaborative control method based on the flat-panel processing technology chain.

[0015] The technical effects achieved by this invention are as follows: This invention, through disturbance source identification and feedforward prediction, compensates downstream processes in advance, significantly reducing response lag and overshoot caused by disturbance transmission, allowing the fabric to recover to the target state more quickly. The coupling of feedforward and feedback, combined with adaptive control weighted by fabric humidity and temperature, effectively reduces tension fluctuations and width deviations across the entire line, reduces defects such as width breaks, wrinkles, and curling edges, and improves the finished product qualification rate and consistency. Based on historical data and dynamic weight adjustment of fabric state, it can maintain good control performance under different raw material, humidity, and temperature conditions, reducing manual intervention and frequent parameter resets. The disturbance source location mechanism can quickly indicate the process in which the problem occurs, facilitating maintenance and root cause analysis. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method provided by the present invention; Figure 2 This is a system module diagram provided by the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0020] Furthermore, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, the schematic diagrams are merely examples for ease of explanation and should not limit the scope of protection of the present invention.

[0021] Please see the appendix Figure 1 As shown, a multi-process collaborative control method based on a flat-panel processing technology chain is provided, including: S1. Real-time acquisition of fabric surface status parameters from each process unit, including the warp tension value, weft width value and running speed value of the fabric, and acquisition of fabric abnormality parameters based on the fabric surface status parameters. S2. Based on the abnormal parameters of the fabric surface, identify the source process in the current process chain that will cause disturbance to the downstream process; S3. Obtain the disturbance prediction value of the source process to the downstream process, and generate feedforward control instructions for the control of the downstream process based on the disturbance prediction value. S4. Based on the feedforward control command, obtain the deviation between the downstream fabric state parameters of the downstream process and the set target, and generate feedback control command. S5. Obtain fabric status information from the source process, including moisture content and temperature. Obtain a comprehensive weight based on the fabric status information, including tension weight and width weight. S6. Integrate feedforward control commands and feedback control commands, and obtain collaborative control commands based on tension fluctuations, width deviations, and comprehensive weights throughout the line. Send the collaborative control commands to the actuators of the corresponding process units to adjust process parameters.

[0022] As described in steps S1 to S6 above, each process unit uploads fabric state parameters (warp tension, weft width, and running speed) in real time using devices such as tension sensors, width measuring devices, and speed encoders. The collected values ​​are judged based on preset benchmark safety state parameter ranges. When any parameter of a process exceeds the corresponding range, the value is marked as abnormal and summarized as fabric abnormal parameters (warp tension abnormality, weft width abnormality, and running speed abnormality). Benchmark fabric parameters are obtained and combined with the fabric abnormal parameters to calculate the disturbance coefficient corresponding to each process. Simultaneously, a state change coefficient is calculated for the current fabric data between processes (summarized warp tension data, summarized width data, and summarized speed data). The disturbance coefficient and the state change coefficient are combined to obtain the comprehensive disturbance. The values ​​are determined by looking up a pre-built source table (including the comprehensive disturbance value range, corresponding disturbance process, and disturbance level). All candidate disturbance processes are sorted by disturbance level, and the first one is identified as the source process that will affect downstream processes. For the identified source processes, their current state and multiple historical states are collected, and the disturbance prediction value (i.e., the expected disturbance amplitude and direction to be transmitted downstream) is calculated by combining the source weights of the longitudinal, latitudinal, and velocity directions. The disturbance prediction value is mapped to the feedforward control table to obtain the corresponding feedforward control command, which is then issued to the downstream processes to compensate for the impending disturbance in advance, thereby reducing hysteresis and overshoot. After executing the feedforward command, the actual layout state of the downstream processes is collected and compared with the set target to obtain the deviation. This deviation is then fed back. The control table maps deviations to feedback control commands to eliminate residual deviations caused by insufficient feedforward or model errors, forming a dual-loop control structure that couples feedforward (predictive compensation) and feedback (deviation correction). Since fabric moisture content and temperature significantly affect the transmission characteristics of tension and width, humidity and temperature data from the source process are collected and the moisture-temperature ratio is calculated. The corresponding comprehensive weights (tension weight, width weight) are obtained from the fabric comprehensive table. These weights are used to dynamically adjust the influence of each parameter in disturbance prediction, feedforward / feedback fusion, and collaborative calculation, giving control decisions material-aware capabilities and avoiding over- or under-compensation under humid or high-temperature conditions. After adjusting downstream processes according to feedforward and feedback commands, the overall tension fluctuation, width deviation, and fabric comprehensive weights are extracted. The fabric coordination value is calculated, and the final coordination control command is obtained through the fabric coordination table. This command is issued to the actuators of each process (such as tension servo, cylinder, tension roller speed, and fabric guiding device) according to priority, coordinating and adjusting process parameters to complete cross-process coordination compensation. This achieves rapid return and steady-state maintenance of the overall fabric state. Through disturbance source identification and feedforward prediction, downstream compensation is performed in advance, significantly reducing response lag and overshoot caused by disturbance transmission, allowing the fabric to recover to the target state more quickly. The coupling of feedforward and feedback, combined with adaptive control based on fabric humidity and temperature weighting, effectively reduces tension fluctuations and width deviations across the entire line, reduces defects such as width breaks, wrinkles, and edge curling, and improves the finished product qualification rate and consistency. Dynamic weight adjustment based on historical data and fabric state is also implemented.This ensures good control under varying raw materials, humidity, and temperature conditions, reducing manual intervention and frequent parameter resetting. The disturbance source localization mechanism quickly indicates the process where the problem occurred, facilitating maintenance and root cause analysis.

[0023] In a preferred embodiment, fabric surface condition parameters from each process unit are acquired in real time. These parameters include the warp tension, weft width, and running speed of the fabric. Abnormal fabric surface parameters are then obtained based on these parameters, including: S101. Real-time acquisition of fabric surface status parameters from each process unit, including the warp tension value, weft width value, and running speed value of the fabric. S102. Obtain the reference safety status parameters, wherein the reference safety status parameters include the reference safety warp tension value range, the reference safety weft width value range, and the reference safety operating speed value range of the fabric. S103. Determine whether the fabric condition parameters exceed the baseline safety condition parameters; If all values ​​in the fabric condition parameters are within the corresponding value range in the baseline safety condition parameters, then each process control is considered normal. If all values ​​in the fabric condition parameters are outside the corresponding value range in the baseline safety condition parameters, the corresponding process control is determined to be abnormal. Values ​​outside the corresponding value range in the baseline safety condition parameters are marked as the corresponding abnormal values ​​and summarized as fabric abnormal parameters. Among them, fabric abnormal parameters include warp tension abnormal values, weft width abnormal values, and running speed abnormal values.

[0024] As described in steps S101 to S103 above, a warp tension sensor, a width measuring device (photoelectric / laser width gauge or contact width sensor), and a running speed encoder (or calculated by a spindle / drum encoder) are arranged in each process unit. Real-time sampling is performed using a fixed sampling frequency (e.g., several Hz to tens of Hz, depending on the process speed). The original signal is denoised and calibrated (filtering, zero / range correction, temperature drift compensation, etc.), and short-time statistics (average value) are calculated. The warp tension value, weft width value, and running speed value are extracted. The baseline safety range is jointly determined by process specifications, quality requirements, equipment capabilities, and empirical values. It can be statically set (engineer-set) or adaptively adjusted during operation based on historical data (self-learning, statistical methods). The baseline safety status parameters include the baseline safety warp tension value range, the baseline safety weft width value range, and the baseline safety operating speed value range of the fabric. The pre-processed real-time parameters are compared with the corresponding baseline ranges. If all values ​​are within the range, it is normal. If all values ​​are outside the corresponding range, it is abnormal. Each indicator outside the range is marked as an abnormal value (e.g., abnormal tension, abnormal width, abnormal speed). These abnormal values ​​are summarized as the fabric abnormal parameters for this process. Each abnormal record should include: abnormality type, degree of deviation (difference or percentage from the nearest endpoint), timestamp, duration, etc. Through high-frequency real-time acquisition and strict range judgment, problematic processes can be identified and marked before disturbances spread downstream, facilitating timely handling.

[0025] In a preferred embodiment, based on fabric anomaly parameters, the source process in the current process chain that will cause disturbances to downstream processes is identified, including: S201. Obtain the reference fabric parameters, which include the reference warp tension value, the reference weft width value, and the reference running speed value. S202. Obtain the corresponding process disturbance coefficient based on the abnormal fabric parameters and the reference fabric parameters; S203. Obtain the fabric summary dataset corresponding to the abnormal fabric parameters in the current process chain. The fabric summary dataset includes summary warp tension data corresponding to the abnormal warp tension value, summary weft width data corresponding to the abnormal weft width value, and summary running speed data corresponding to the abnormal running speed value. The summary warp tension data includes multiple summary warp tension values, the summary weft width data includes multiple summary weft width values, and the summary running speed data includes multiple summary running speed values. S204. Obtain the corresponding state change coefficients based on the fabric summary dataset; S205. Obtain the comprehensive disturbance value based on the corresponding process disturbance coefficient and the corresponding state change coefficient; S206. Identify the source process in the current process chain that will cause disturbance to downstream processes based on the comprehensive disturbance value.

[0026] As described in steps S201 to S206 above, baseline values ​​are determined from process specifications or historical steady-state statistics. These baseline warp tension, baseline weft width, and baseline operating speed can be represented by constant values, intervals, or dynamic baselines based on production batches (e.g., the median of hours obtained over N rolling days). For each abnormal parameter (warp tension, width, speed), a process disturbance coefficient (which can be understood as the potential disturbance amplification factor for that abnormality) is calculated. The formula for calculating the process disturbance coefficient is as follows: In the formula, f represents the process disturbance coefficient. This is represented as an abnormal parameter of the fabric surface. This is represented as the value corresponding to the fabric anomaly parameter in the baseline fabric parameters, for example, This is an abnormal value of meridional tension. When the baseline warp tension value is used, f is the process disturbance coefficient of the warp tension. Historical data columns corresponding to abnormal parameters are collected and summarized within the process chain according to time windows (e.g., past T seconds / minutes or past M sampling points). The fabric summary dataset includes summary warp tension data corresponding to abnormal warp tension values, summary weft width data corresponding to abnormal weft width values, and summary running speed data corresponding to abnormal running speed values. The summary warp tension data includes multiple summary warp tension values, the summary weft width data includes multiple summary weft width values, and the summary running speed data includes multiple summary running speed values. The corresponding state change coefficient is calculated based on the fabric summary dataset. The formula for calculating the state change coefficient is... In the formula, g represents the state change coefficient, and i represents the number of multiple aggregated warp tension values, multiple aggregated weft width values, or multiple aggregated running speed values, i=1,2,3…n. This is represented as the i-th summative warp tension value, summative weft width value, or summative running speed value. For example, when G is the summative warp tension value, g is the state change coefficient of the warp tension. The comprehensive disturbance value is calculated based on the corresponding process disturbance coefficient and the corresponding state change coefficient. It can be obtained by multiplying the process disturbance coefficient and the state change coefficient. For example, the comprehensive disturbance value of the warp tension is obtained by multiplying the process disturbance coefficient of the warp tension and the state change coefficient of the warp tension. The total process disturbance values ​​of all processes in the current process chain are sorted, and the maximum value is taken as the source process. By modeling and synthesizing the process disturbance and state change characteristics separately, considering both the severity of instantaneous deviation and time and waveform characteristics, it is possible to more accurately determine which process is the real disturbance starting point and avoid misjudging the downstream passive response as the source.

[0027] In a preferred embodiment, the source process in the current process chain that will cause disturbances to downstream processes is identified based on the overall disturbance value, including: S2061. Obtain the source table set, which includes the meridional source table, the latitudinal source table and the velocity source table. Each source table includes multiple disturbance comprehensive value intervals and the disturbance process and the corresponding disturbance level for each disturbance comprehensive value interval. S2062. Obtain the corresponding disturbance process and the corresponding disturbance level from the source table set according to the disturbance comprehensive value range corresponding to the disturbance comprehensive value; S2063. Sort the disturbance levels corresponding to the disturbance processes in descending order to obtain a disturbance sorting table; S2064. Select the first disturbing process from the disturbance sorting table, which is the source process in the current process chain that will cause disturbance to the downstream process.

[0028] As described in steps S2061 to S2064 above, three preset or online-updated source tables are read: the meridional source table, the latitudinal source table, and the velocity source table. Each source table is divided into several intervals according to the comprehensive disturbance value (e.g., [0, 0.2), [0.2, 0.5), [0.5, 1.0]), and a set of disturbance processes (which may be one or more processes) and a corresponding disturbance level (e.g., light / medium / heavy, or numerical classification 1–5) are predefined in each interval. The table entries can be derived from expert experience, process experimental data, historical fault statistics, or mapping rules obtained through offline training of machine learning. For each type of indicator (meridional, latitudinal, latitude)... The disturbance comprehensive value of the indicator (direction and speed) falls into the corresponding interval. The disturbance process and disturbance level corresponding to the interval are obtained by looking up the table. All candidate disturbance process records from the three types of source tables are summarized and sorted by disturbance level. By default, they are sorted from largest to smallest level. If they are the same level, they can be further sorted by the size of the disturbance comprehensive value or historical priority to generate a disturbance ranking table. The first item (highest level) in the disturbance ranking table is taken as the main source process and identified as the source that will cause disturbance to the downstream. The table lookup and sorting operations have small computational load and fast response, which is suitable for real-time or near real-time online control scenarios. It can quickly convert numerical results into decisions to judge the source.

[0029] In a preferred embodiment, the disturbance prediction value of the source process on the downstream process is obtained, and based on the disturbance prediction value, feedforward control instructions for controlling the downstream process are generated, including: S301. Obtain the current fabric state parameters, historical fabric state parameters and source weight of the source process. The current fabric state parameters include the current warp tension value, the current weft width value and the current running speed value. The historical fabric state parameters include multiple historical warp tension values, multiple historical weft width values ​​and multiple historical running speed values. The source weight includes warp weight, weft weight and speed weight. S302. Obtain the predicted disturbance value based on the current fabric state parameters, historical fabric state parameters, and source weights. S303. Obtain the feedforward control table, wherein the feedforward control table includes multiple disturbance prediction value intervals and the feedforward control command corresponding to each disturbance prediction value interval; S304. Obtain feedforward control instructions for downstream process control from the feedforward control table based on the interval of the disturbance prediction value corresponding to the disturbance prediction value.

[0030] As described in steps S301 to S304 above, three types of real-time and historical data are collected from the source process: current warp tension, current weft width, current running speed, and corresponding historical windows (e.g., data from the most recent N samples or the most recent T seconds). Historical fabric state parameters include multiple historical warp tension values, multiple historical weft width values, and multiple historical running speed values. Source weights include warp weight, weft weight, and speed weight. The raw data is preprocessed, including noise reduction (low-pass / moving average), drift correction, outlier removal, timestamp alignment, and supplementary sampling to ensure stable and reliable prediction input. The set source weights (warp weight, width weight, and speed weight) are obtained. These weights reflect the relative influence of the source on various downstream disturbances and can be preset or adjusted online based on material properties, process structure, or historical sensitivity statistics. The prediction results of the three types of indicators are weighted according to the source weights to synthesize a disturbance prediction value. The formula for calculating the disturbance prediction value is as follows: In the formula, D represents the predicted disturbance value, j represents the number of multiple historical meridional tension values, multiple historical zonal width values, and multiple historical operating speed values, j=1,2,3…m. Represented as the i-th historical meridional tension value, This is represented as the current meridional tension value. Represented as the i-th historical meridional tension value, This is represented as the current meridional tension value. Represented as the i-th historical meridional tension value, The current warp tension value is represented by x, the warp weight by y, the weft weight by v, and the velocity weight by v. x + y + v = 1. The feedforward control table is divided into intervals based on the predicted disturbance values. Each interval is mapped to a specific control action (e.g., a slight disturbance interval corresponds to a small setting offset). The control action can be adjusting the tension setting, adjusting the winding speed, adjusting the guide position, or a combination of actions. By substituting the predicted disturbance value into the feedforward table, the corresponding feedforward control command is retrieved. By predicting future disturbances and adjusting the downstream settings in advance, compensation can be completed before the disturbance arrives, significantly reducing overshoot and oscillation. The lookup-based mapping of the feedforward table is easy to understand and maintain. The source weights and table entries can be gradually optimized based on experience or historical data.

[0031] In a preferred embodiment, the deviation between the downstream fabric state parameters of the downstream process and the set target is obtained based on the feedforward control command, and a feedback control command is generated, including: S401, Control commands for adjusting downstream processes based on feedforward control commands; S402. Obtain the fabric state parameters after adjusting the control command of the downstream process, and mark them as downstream fabric state parameters. The downstream fabric state parameters include the downstream warp tension value, the downstream weft width value, and the downstream running speed value. S403. Obtain the fabric surface state parameters of the downstream process's target, wherein the fabric surface state parameters of the target include the target warp tension value, the target weft width value, and the target running speed value. S404. Obtain the deviation of the fabric condition based on the downstream fabric condition parameters and the target fabric condition parameters; S405. Obtain the feedback control table, wherein the feedback control table includes multiple deviation ranges and the feedback control instruction corresponding to each deviation range; S406. Obtain feedback control instructions from the feedback control table based on the deviation interval corresponding to the deviation.

[0032] As described in steps S401 to S406 above, the feedforward control command is converted into specific control quantities for the downstream actuator (e.g., current / speed setting of the tension servo, speed setting of the master / slave roll, position of the guide cylinder, angle of the tension roller, etc.). After the actuator operates and a delay / response time has elapsed, downstream fabric parameters (warp tension, width, running speed) are continuously collected and timestamped as downstream fabric state parameters. Downstream target values ​​(target warp tension, width, speed) are provided by the process formula / production command or the upper-level MES. The deviation of the fabric state is calculated based on the downstream fabric state parameters and the target fabric state parameters. This deviation can be detected by the downstream fabric... The value in the surface state parameter is subtracted from the corresponding value in the target fabric surface state parameter to obtain the warp tension deviation. For example, the warp tension deviation is obtained by subtracting the set target warp tension value from the downstream warp tension value. The feedback control table is divided into deviation intervals (e.g., [-∞,-2), [-2,-1), [-1,1], (1,2], (2,+∞)). Each interval is mapped to a type of feedback action (switching control mode or triggering manual inspection / stop, etc.). The calculated deviation is mapped to the corresponding interval, and the feedback control command is retrieved (e.g., setting downstream tension, adjusting speed setting, changing servo gain). This avoids production line oscillation or mechanical shock caused by excessively frequent or violent control actions.

[0033] In a preferred embodiment, fabric state information from the source process is obtained, including moisture content and temperature. A comprehensive weight is then obtained based on the fabric state information, comprising tension weight and width weight, including: S501. Obtain fabric condition information from the source process, including moisture content and temperature. S502. Obtain the moisture-temperature ratio based on moisture content and temperature; S503. Obtain the fabric summary table. The fabric summary table includes multiple moisture temperature ratio ranges and the comprehensive weight corresponding to each moisture temperature ratio range. The comprehensive weight includes tension weight and width weight. S504. Obtain the corresponding comprehensive weight from the fabric comprehensive table based on the wet temperature ratio range corresponding to the wet temperature ratio.

[0034] As described in steps S501 to S504 above, online humidity measurement (such as contact or non-contact moisture content sensors) and temperature sensors are arranged at the source process to sample the temperature and moisture content of the fabric surface or near-surface layer in real time. The sampled signals are preprocessed (filtering, zero-point correction, timestamp), and the moisture content value is divided by the temperature to obtain the moisture-temperature ratio. A pre-set or online-learned comprehensive fabric table maps several intervals of the moisture-temperature ratio to corresponding comprehensive weights. Each item contains at least two components: tension weight and width weight. The table items can be sourced from process experience, material manuals, or statistical / regression analysis based on historical data. The real-time moisture-temperature ratio is mapped to a certain interval of the comprehensive fabric table, and the tension weight and width weight corresponding to that interval are read as the comprehensive weight output. The weights of tension and width are dynamically adjusted according to the moisture-temperature ratio, so that the controller can adopt more appropriate strategies under different moisture and temperature conditions, thereby improving the control targeting.

[0035] In a preferred embodiment, feedforward control commands and feedback control commands are integrated, and coordinated control commands are obtained based on overall tension fluctuations, width deviations, and comprehensive weights. These coordinated control commands are then sent to the actuators of the corresponding process units to adjust process parameters, including: S601: Combine feedforward control commands and feedback control commands to obtain the overall tension fluctuation and width deviation of the fabric; S602. Obtain the fabric synergy value based on overall tension fluctuation, width deviation, and comprehensive weighting. S603. Obtain the fabric coordination table, which includes multiple fabric coordination value ranges and the coordination control instructions corresponding to each fabric coordination value range. S604. Obtain the corresponding coordination control instruction from the fabric coordination table according to the fabric coordination value range corresponding to the fabric coordination value; S605. Issue collaborative control instructions to the corresponding process unit's actuator to adjust process parameters.

[0036] As described in steps S601 to S605 above, the feedforward control instructions and feedback control instructions currently being executed in each process (including the expected effects of instructions to be executed and those already executed) are collected and merged. The average tension fluctuation of the entire production line is calculated, and the set of deviations between the width of each process point and the target width is calculated. Then, the overall average deviation is taken, and the indicators of different dimensions are normalized. Based on the overall tension fluctuation, width deviation, and comprehensive weight, the fabric synergy value is calculated. The formula for calculating the fabric synergy value is as follows: In the formula, Q represents the fabric coordination value, b represents the tension weight, B represents the overall tension fluctuation, p represents the width weight, P represents the width deviation, and p+b=1. The fabric coordination table is divided into coordination value intervals, and each interval defines a corresponding coordination control instruction or instruction template (such as tension adjustment, width adjustment, combined adjustment, or triggering manual maintenance / line stop). The calculated coordination value falls into the corresponding interval of the fabric coordination table, and the corresponding coordination control instruction is read and generated. The coordination control instruction is sent to the controller / actuator (servo, frequency converter, pneumatic cylinder, etc.) of the corresponding process unit, and the execution time and version are recorded. The feedforward and feedback instructions are comprehensively decided at the entire line level, which can avoid the mutual cancellation or amplification effect caused by local independent adjustment, thereby more effectively suppressing the propagation of disturbances along the process chain. The control focus (tension or width) is dynamically determined by the comprehensive weight of the fabric, so that the control action is more in line with the actual behavior of the material.

[0037] Please see the appendix Figure 2 As shown, the present invention also provides a multi-process collaborative control system based on a flat-panel processing chain, used in the aforementioned multi-process collaborative control method based on a flat-panel processing chain, comprising: The fabric status module is used to acquire fabric status parameters from each process unit in real time. The fabric status parameters include the warp tension value, weft width value and running speed value of the fabric. Abnormal fabric parameters are obtained based on the fabric status parameters. The source process module is used to identify the source processes in the current process chain that will cause disturbances to downstream processes based on abnormal fabric parameters. The feedforward instruction module is used to obtain the disturbance prediction value of the source process to the downstream process, and generate feedforward control instructions for the control of the downstream process based on the disturbance prediction value. The feedback instruction module obtains the deviation between the downstream fabric state parameters of the downstream process and the set target based on the feedforward control instruction, and generates feedback control instructions. The comprehensive weighting module is used to obtain fabric state information from the source process, including moisture content and temperature. Based on the fabric state information, a comprehensive weight is obtained, which includes tension weight and width weight. The process adjustment module integrates feedforward control commands and feedback control commands, and obtains collaborative control commands based on tension fluctuations, width deviations, and overall weights. These collaborative control commands are then sent to the actuators of the corresponding process units to adjust the process parameters.

[0038] As described above, tension sensors, width measuring devices, and speed encoders installed in each process collect warp tension, weft width, and running speed at a unified sampling period. These data are then pre-processed (noise reduction, calibration, timestamping), and the pre-processed data stream is sent to the system bus (or middleware). Within the fabric status module, fabric anomaly parameters (anomaly marker, deviation, duration, etc.) are generated based on the reference safety range. The source process module receives the fabric anomaly parameters and summarized data, calculates the disturbance coefficient and state change coefficient of each process, synthesizes the comprehensive disturbance value, and maps the comprehensive disturbance value to the disturbing process and disturbance level through the source table set (warp / weft / speed table). The data is then processed according to the disturbance level. The process involves sorting and outputting the source processes. The feedforward instruction module reads the current and historical fabric state and source weight of the identified source processes, calculates the predicted disturbance value, maps the predicted disturbance value to the feedforward control table, generates specific feedforward control instructions (such as speed deviation, tension setting adjustment, and guide action), and sends them to the downstream controller (while recording them for subsequent evaluation). The feedback instruction module continuously collects the downstream fabric state after the feedforward execution, calculates the deviation from the target, and looks up the correction instruction (or generates by the PID / adaptive controller) in the feedback control table. It then combines the feedback instruction and the feedforward instruction according to a predetermined fusion strategy (weighted or priority-based) to form a control command for execution. The system performs safety verification and smoothing. The comprehensive weighting module collects the moisture content and temperature of the fabric from the source process, calculates the moisture-temperature ratio, and reads the tension and width weights from the fabric comprehensive table. These weights are then provided to the feedforward, feedback, and process adjustment modules for weighted synthesis of disturbance prediction, deviation assessment, and synergy value calculation, giving the control strategy material sensing capabilities. The process adjustment module summarizes all feedforward and feedback actions and real-time measurements, calculates the tension fluctuations and width deviations across the entire line, and combines these with the comprehensive weights to calculate the fabric synergy value. This synergy value is mapped to cross-process synergy control instructions (specifying which processes adjust by what magnitude / priority) through the fabric synergy table, and performs conflict scheduling, width limiting, and ramping. After processing, the instructions are sent to the executing mechanism. If the effect of the instructions is not as expected or an anomaly occurs, the system will automatically backtrack or trigger manual intervention. The modular implementation enables the system to coordinate the response of multiple processes after identifying the disturbance source, avoiding mutual conflicts or amplification effects caused by local independent adjustments. This reduces the speed and amplitude of disturbance propagation downstream. The coupling of feedforward prediction and real-time feedback, combined with the adaptive adjustment of fabric property weights, can more accurately control the tension and width of the entire line, reduce defects such as width breaks, wrinkles, and width deviations, and improve product qualification rate and batch consistency. The predictive feedforward compensates before the disturbance arrives, and the feedback can quickly correct the problem, shorten the steady-state recovery time, and reduce the number of shutdowns and line stop adjustments.

[0039] And, a multi-process collaborative control terminal based on a flat-panel processing chain, comprising: One or more processors; A storage device on which one or more programs are stored; When one or more programs are executed by one or more processors, the one or more processors implement a multi-process collaborative control method based on the flat-panel processing technology chain.

[0040] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A multi-process collaborative control method based on a flat-panel processing chain, characterized in that, include: Real-time acquisition of fabric surface status parameters from each process unit, including warp tension value, weft width value and running speed value of the fabric, and acquisition of fabric abnormal parameters based on the fabric surface status parameters; Based on abnormal fabric parameters, identify the source process in the current process chain that will cause disturbance to downstream processes; Obtain the disturbance prediction value of the source process to the downstream process, and generate feedforward control instructions for the control of the downstream process based on the disturbance prediction value; Based on the deviation between the downstream fabric state parameters of the downstream process and the set target obtained from the feedforward control command, feedback control command is generated. Obtain fabric condition information from the source process, including moisture content and temperature. Obtain a comprehensive weight based on the fabric condition information, including tension weight and width weight. By integrating feedforward control commands and feedback control commands, and obtaining coordinated control commands based on tension fluctuations, width deviations, and overall weights, the coordinated control commands are sent to the actuators of the corresponding process units to adjust process parameters.

2. The multi-process collaborative control method based on the flat-panel processing chain according to claim 1, characterized in that, Real-time acquisition of fabric surface condition parameters from each process unit, including warp tension, weft width, and running speed. Fabric anomaly parameters are then obtained based on these parameters, including: The fabric surface condition parameters from each process unit are acquired in real time. These parameters include the warp tension value, weft width value, and running speed value of the fabric. Obtain the baseline safety status parameters, which include the baseline safety warp tension value range, the baseline safety weft width value range, and the baseline safety operating speed value range of the fabric; Determine whether the fabric condition parameters exceed the baseline safety condition parameters; If all values ​​in the fabric condition parameters are within the corresponding value range in the baseline safety condition parameters, then each process control is considered normal. If all values ​​in the fabric condition parameters are outside the corresponding value range in the baseline safety condition parameters, the corresponding process control is determined to be abnormal. Values ​​outside the corresponding value range in the baseline safety condition parameters are marked as the corresponding abnormal values ​​and summarized as fabric abnormal parameters. Among them, fabric abnormal parameters include warp tension abnormal values, weft width abnormal values, and running speed abnormal values.

3. The multi-process collaborative control method based on the flat-panel processing chain according to claim 1, characterized in that, Based on fabric anomaly parameters, identify the source processes in the current process chain that will disrupt downstream processes, including: Obtain the reference fabric parameters, which include the reference warp tension value, the reference weft width value, and the reference running speed value; The corresponding process disturbance coefficient is obtained based on the abnormal fabric parameters and the reference fabric parameters. Obtain the fabric summary dataset corresponding to the abnormal fabric parameters in the current process chain. The fabric summary dataset includes summary warp tension data corresponding to abnormal warp tension values, summary weft width data corresponding to abnormal weft width values, and summary running speed data corresponding to abnormal running speed values. The summary warp tension data includes multiple summary warp tension values, the summary weft width data includes multiple summary weft width values, and the summary running speed data includes multiple summary running speed values. Obtain the corresponding state change coefficients based on the fabric summary dataset; The comprehensive disturbance value is obtained based on the corresponding process disturbance coefficient and the corresponding state change coefficient; Identify the source process in the current process chain that will cause disturbances to downstream processes based on the comprehensive disturbance value.

4. The multi-process collaborative control method based on the flat-panel processing chain according to claim 3, characterized in that, Based on the comprehensive disturbance value, the source processes in the current process chain that will cause disturbances to downstream processes are identified, including: Obtain the source table set, which includes the meridional source table, the latitudinal source table, and the velocity source table. Each source table includes multiple disturbance comprehensive value intervals, as well as the disturbance process and the corresponding disturbance level for each disturbance comprehensive value interval. Based on the disturbance comprehensive value range corresponding to the disturbance comprehensive value, obtain the corresponding disturbance process and the corresponding disturbance level from the source table set; The disturbance levels corresponding to the disturbance processes are sorted in descending order to obtain a disturbance sorting table; Select the first disturbing process from the disturbance ranking list; this is the source process in the current process chain that will cause disturbances to downstream processes.

5. The multi-process collaborative control method based on the flat-panel processing chain according to claim 1, characterized in that, Obtain the predicted disturbance values ​​of the source process to the downstream process, and based on the predicted disturbance values, generate feedforward control instructions for the control of the downstream process, including: Obtain the current fabric state parameters, historical fabric state parameters, and source weights of the source process. The current fabric state parameters include the current warp tension value, the current weft width value, and the current running speed value. The historical fabric state parameters include multiple historical warp tension values, multiple historical weft width values, and multiple historical running speed values. The source weights include warp weights, weft weights, and speed weights. The disturbance prediction value is obtained based on the current fabric state parameters, historical fabric state parameters and source weights; Obtain the feedforward control table, which includes multiple disturbance prediction value intervals and the corresponding feedforward control command for each disturbance prediction value interval; Based on the disturbance prediction value range corresponding to the disturbance prediction value, the feedforward control instructions for downstream process control are obtained from the feedforward control table.

6. The multi-process collaborative control method based on the flat-panel processing chain according to claim 1, characterized in that, Based on the deviation between the downstream fabric state parameters of the downstream process and the set target obtained from the feedforward control command, feedback control commands are generated, including: Control commands for downstream processes are adjusted based on feedforward control commands; After obtaining the control command for adjusting the downstream process, the fabric surface state parameters are obtained and marked as downstream fabric surface state parameters. The downstream fabric surface state parameters include the downstream warp tension value, the downstream weft width value, and the downstream running speed value. Obtain the fabric surface condition parameters of the target in the downstream process, wherein the fabric surface condition parameters of the target include the target warp tension value, the target weft width value, and the target running speed value. The deviation of the fabric condition is obtained based on the downstream fabric condition parameters and the target fabric condition parameters; Obtain the feedback control table, which includes multiple deviation ranges and the corresponding feedback control instructions for each deviation range; The feedback control command is obtained from the feedback control table based on the deviation range corresponding to the deviation.

7. The multi-process collaborative control method based on the flat-panel processing chain according to claim 1, characterized in that, Obtain fabric condition information from the source process, including moisture content and temperature. Based on this information, derive a comprehensive weight, which includes tension weight and width weight. Obtain fabric condition information from the source process, including moisture content and temperature; The humidity-temperature ratio is obtained based on moisture content and temperature. Obtain the fabric summary table, which includes multiple moisture temperature ratio ranges and the corresponding comprehensive weight for each moisture temperature ratio range. The comprehensive weight includes tension weight and width weight. The corresponding comprehensive weight is obtained from the fabric comprehensive table based on the humidity temperature ratio range corresponding to the humidity temperature ratio.

8. The multi-process collaborative control method based on the flat-panel processing chain according to claim 1, characterized in that, By integrating feedforward and feedback control commands, and obtaining coordinated control commands based on overall tension fluctuations, width deviations, and comprehensive weights, these commands are sent to the corresponding process unit's actuators to adjust process parameters, including: By combining feedforward control commands and feedback control commands, the overall tension fluctuation and width deviation of the fabric can be obtained. The fabric synergy value is obtained based on overall tension fluctuation, width deviation, and comprehensive weighting. Obtain the fabric coordination table, which includes multiple fabric coordination value ranges and the corresponding coordination control instructions for each fabric coordination value range; The corresponding coordination control command is obtained from the fabric coordination table based on the fabric coordination value range corresponding to the fabric coordination value. The collaborative control command is sent to the actuator of the corresponding process unit to adjust the process parameters.

9. A multi-process collaborative control system based on a flat-panel processing chain, applied to the multi-process collaborative control method based on a flat-panel processing chain as described in any one of claims 1 to 8, characterized in that, include: The fabric status module is used to acquire fabric status parameters from each process unit in real time. The fabric status parameters include the warp tension value, weft width value and running speed value of the fabric. Abnormal fabric parameters are obtained based on the fabric status parameters. The source process module is used to identify the source processes in the current process chain that will cause disturbances to downstream processes based on abnormal fabric parameters. The feedforward instruction module is used to obtain the disturbance prediction value of the source process to the downstream process, and generate feedforward control instructions for the control of the downstream process based on the disturbance prediction value. The feedback instruction module obtains the deviation between the downstream fabric state parameters of the downstream process and the set target based on the feedforward control instruction, and generates feedback control instructions. The comprehensive weighting module is used to obtain fabric state information from the source process, including moisture content and temperature. Based on the fabric state information, a comprehensive weight is obtained, which includes tension weight and width weight. The process adjustment module integrates feedforward control commands and feedback control commands, and obtains collaborative control commands based on tension fluctuations, width deviations, and overall weights. These collaborative control commands are then sent to the actuators of the corresponding process units to adjust the process parameters.

10. A multi-process collaborative control terminal based on a flat-panel processing chain, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When one or more programs are executed by one or more processors, the one or more processors implement the multi-process collaborative control method based on the flat-panel processing chain as described in any one of claims 1 to 8.

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