Intelligent speed regulation training method and system for high-speed jet color weaving machine

Abstract

The invention discloses an intelligent speed regulation training method and system for a high-speed jet color loom, and belongs to the field of textile industry automation. The technical problem that a traditional high-speed jet color loom is low in production efficiency is solved. According to the technical scheme, accurate process control and automatic adjustment are achieved at different weaving and printing speeds through a multi-parameter real-time monitoring system and an intelligent speed regulation control module of the high-speed jet color weaving machine on the basis of a self-adaptive weaving and printing parameter regulation and intelligent feedback mechanism in combination with a comprehensive strategy of multi-parameter simulation, quality evaluation and defect detection; comprising the following steps: inputting an order and a pattern, determining a strategy according to analysis characteristics, calculating multivariable weights to adjust the overall speed of the fabric, automatically prompting speed regulation parameters to carry out threshold alarm, adaptively adjusting the speed to match the real-time change of multiple parameters, and identifying the defects of the fabric based on a color CLE Lab model. Dynamically adjusting the weaving and printing speed and generating a report according to a defect detection result; the invention is applied to the high-speed jet color weaving machine.

Description

Technical Field

[0001] The invention provides an intelligent speed regulation training method and system for a high-speed air jet color loom, belonging to the field of textile industry automation. Background Art

[0002] With the development of science and technology, the automation and intelligence of textile machinery are constantly improving. As an important equipment in the textile industry, the production efficiency and product quality of high-speed jet color looms directly affect the economic benefits of the textile industry. However, during the operation of traditional high-speed jet color looms, due to the lack of an effective weaving and printing detection mechanism, operators face difficulties in identifying fabric defects, which often leads to a lack of cognition and understanding, making it difficult to accurately judge the weaving and printing quality, thus affecting production efficiency and product quality.

[0003] Traditional high-speed jet color looms usually rely on manual parameter adjustment and quality inspection during operation. This method is not only inefficient, but also lacks systematic training tools and guidance, and cannot help operators effectively cope with various challenges in production. Therefore, on high-speed and large-scale production lines, this traditional method obviously cannot meet the needs of modern production, and an intelligent speed regulation training method and system are needed to improve the accuracy and efficiency of weaving and printing inspection.

[0004] To solve the above problems, the present invention proposes an intelligent speed regulation training method and system for a high-speed jet color loom. The system enables the operator to clearly understand the operating status of the jet color loom through a multi-parameter simulation display at a real-time global weaving speed, so as to more effectively adjust the parameters to optimize the fabric speed. At the same time, the system has the function of automatically prompting the weaving quality evaluation under parameterized speed regulation, providing real-time and accurate quality feedback, helping the operator to adjust the parameters in time to ensure the weaving quality. In addition, the system can evaluate the color difference perceived by the human eye for weaving speed regulation matching, and more accurately evaluate the color difference by simulating the visual perception of the human eye, ensuring the uniform color of the product to meet consumer needs. At the same time, the system can also mark multi-parameter defects in real time and generate local case output simulations to help operators find and solve problems in time, thereby further improving production efficiency and product quality. Summary of the invention

[0005] In order to overcome the deficiencies in the prior art, the present invention aims to solve the technical problem of providing an intelligent speed regulation training method and system improvement for a high-speed jet color loom.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: an intelligent speed regulation training method and system for high-speed jet color looms, characterized in that: based on adaptive weaving and printing parameter control and intelligent feedback mechanism, combined with a comprehensive strategy of multi-parameter simulation, quality assessment and defect detection, through a multi-parameter real-time monitoring system and an intelligent speed regulation control module of a high-speed jet color loom, accurate process control and automatic adjustment are achieved at different weaving and printing speeds. The specific implementation method includes the following steps: S1: Input orders and patterns to determine strategies based on analysis characteristics. S2: Calculate the weights of multiple variables to adjust the overall speed of the fabric. S3: Automatically prompt the speed control parameters for threshold alarm. S4: Adaptive speed adjustment to match real-time changes in multiple parameters. S5: Identification of fabric defects based on the color CLE Lab model. S6: Dynamically adjust the weaving and printing speed and generate reports based on the defect detection results.

[0007] The step S1 specifically includes: Order information and patterns are input into the system, and the input data is analyzed through advanced feature extraction algorithms to determine the optimal speed layering strategy. The appropriate speed regulation mode is selected based on the fabric characteristics and pattern complexity to achieve dynamic optimization and performance improvement of the weaving and printing process.

[0008] The step S2 specifically includes: According to the results of layered analysis of the pattern, a dynamic control algorithm is used to generate precise speed adjustment signals and color control signals. Through the regional speed regulation mechanism, refined speed control is implemented in each weaving and printing area to ensure the consistency of fabric surface quality and the accuracy of color expression.

[0009] The step S3 specifically includes: Automatically calculate the dynamic speed requirements of each weaving and printing area, and use the adaptive control algorithm to generate the corresponding control signal to achieve consistency and coordination of regional speeds; if there is a difference between the actual speed and the predicted speed, the system will automatically prompt the user to ensure the overall quality and performance stability of the fabric during the production process.

[0010] The step S4 specifically includes: In the system, according to the customized dynamic speed matching strategy, the speed control strategy is determined in real time based on user-defined multiple parameters such as fabric texture, color, pattern complexity, etc. through advanced data analysis and machine learning algorithms. The program can dynamically adjust the running speed of the jet color loom according to the changes in these parameters to match the changes in the pattern. Especially during the weaving process, if the color or complexity of the pattern changes, the system will automatically adjust the speed to ensure the quality and efficiency of weaving. At the same time, the system will automatically learn and optimize the speed control strategy based on real-time operation data to further improve the operation efficiency and product quality of the jet color loom.

[0011] The step S5 specifically includes: Based on the color CIE Lab model, the color characteristics of the fabric surface are analyzed in real time, and the convolutional neural network (CNN) algorithm is used to identify and judge fabric defects that are difficult for the human eye to detect. Through the correlation analysis of defect feature data and speed control parameters, the linear regression model algorithm is used to dynamically optimize the speed control strategy, which specifically includes constructing a mathematical relationship between defect characteristics and speed control parameters, and optimizing the weaving and printing speed through weight calculation to improve production efficiency and ensure the consistency and stability of product quality. In addition, the system combines machine learning models for predictive analysis, identifies potential defect trends, and realizes active management of fabric quality through feedforward control to optimize quality monitoring during the production process.

[0012] The step S6 specifically includes: After the layering is completed, the weaving speed is dynamically adjusted to compensate for fabric defects based on the defect information obtained by the real-time defect detection module according to the multifunctional weaving and printing intelligent simulation model. The system generates a corresponding speed adjustment strategy by analyzing the type and severity of the defect. At the same time, all detected defect data is recorded and a detailed quality report is generated, including the nature, location and treatment suggestions of the defect, to ensure the traceability, quality monitoring and optimization of production efficiency of the entire weaving and printing process.

[0013] An intelligent speed regulation training method and system for a high-speed jet color loom, characterized by comprising a high-speed jet color loom, a multi-parameter real-time monitoring module integrated in a control unit of the high-speed jet color loom, an intelligent speed regulation control module and a defect detection and marking module, wherein the module functions as a running module of the intelligent speed regulation training program for the high-speed jet color loom; The intelligent speed regulation training program is mainly composed of a multi-parameter surface detection algorithm, a linear feature extraction algorithm, a color detection algorithm based on color gamut analysis, and a defect module; the defect data can be recorded and saved, and has the function of exporting and viewing to support subsequent quality analysis and management. The dynamic speed control algorithm automatically adjusts the weaving and printing speed according to the real-time monitored process parameters through an adaptive optimization algorithm to ensure stable output of fabrics under different production conditions. At the same time, the model also supports manual speed adjustment function, allowing operators to flexibly adjust the weaving and printing speed according to actual needs, and provide self-prompt optimization suggestions during the speed adjustment process to further improve production efficiency and fabric quality. In addition, the model can combine historical data for intelligent prediction and provide long-term speed adjustment strategy recommendations. The color difference detection algorithm adopts the CLE LAB color difference analysis model, combined with the program's built-in visual perception model, to automatically detect color changes during the speed adjustment process, and compare it with the target color to generate a real-time color difference evaluation report to ensure color consistency;

[0014] The multi-parameter simulation model under the actual global weaving speed refers to a comprehensive analysis of real-time speed data and multiple parameters based on the weaving process of a high-speed jet color loom. The model includes objective measurements and subjective evaluations of multiple key parameters, wherein objective measurements include four measurement parameters: fabric material, fabric length, color consistency, and pattern accuracy, and subjective evaluation covers user satisfaction and visual quality scores. The quantitative relationship model between objective measurement and subjective evaluation is to calculate the difference between the objective and subjective parameters of each sample, and use the random forest algorithm for nonlinear fitting to obtain the weight value, so as to achieve effective prediction and optimization of weaving quality. The weight calculation of objective measurement is to analyze the influence of the four parameters of fabric material, fabric length, color consistency, and pattern accuracy on product quality based on previous production data, collect relevant data of past production, including product quality indicators and production parameters, find out the relationship between each parameter and the quality of the final product through regression analysis, and give a score. The score is based on a 1-10 scoring standard (1 point is the worst and 10 is the best). After the four parameters are scored, normalization calculation is performed, and the calculation formula is as follows: Where W i is the weight of the i-th parameter, S i is the score of the ith parameter and n is the total number of parameters. Based on the calculated weight, the speed of the fabric can be determined. Generally speaking, the higher the weight, the faster the speed of the fabric can be set during production, because it means that this parameter has less impact on quality. The specific speed calculation method can be as follows: SA=k·(1+α·(W m +W l +W c +W p )) Where k is a reference speed (unit: m / min). m : The weight of the fabric material; W l: Weight of fabric length; W c : Weight of color consistency; W p : The weight of the pattern accuracy. α: The adjustment coefficient, which controls the amplitude of the speed adjustment. By adjusting the value of α, the sensitivity of the speed adjustment can be controlled. For example, if α = 0.5, the influence of each parameter will only affect half of the base speed. If the sum of all weights is 0, then the speed remains the same; if the sum of weights is positive, then the speed increases; if the sum of weights is negative, then the speed decreases.

[0015] The range of α is defined as: 0.5≤α<1.0: Suitable for smaller adjustment requirements and usually used in stable production processes. 1.0≤α<2.0: Applicable to production environments that require rapid response to changes, such as high-end customization or complex pattern printing. α≥2.0: Can be used in special circumstances, such as production scenarios that require great changes, but should be used with caution to avoid affecting fabric quality.

[0016] Case 1: Background: Producing home fabrics, focusing on fabric durability and pattern accuracy. Fabric material score: 8; Fabric length score: 6; Color consistency score: 7; Pattern accuracy score: 9 Weight: Fabric material: Fabric Length: Color consistency: Pattern accuracy: Speed ​​adjustment: Assuming the base speed k = 60 (unit: m / min), the adjustment coefficient α = 1.0, the speed adjustment calculation is: SA=k·(1+α·(W m +W l +W c +W p ))=120 m / min

[0017] Case 2: Background: High-end silk products, focusing on fabric material and color consistency. Fabric material score: 9; Fabric length score: 4; Color consistency score: 10; Pattern accuracy score: 8 Weight: Fabric Material: Fabric Length: Color consistency: Pattern accuracy: Speed ​​adjustment: Assuming the base speed k = 50 (unit: m / min), the adjustment coefficient α = 1.5, the speed adjustment calculation is: SA=k·(1+α·(W m +W l +W c +W p ))=125 m / min

[0018] Case 3: Background: Wear-resistant outdoor sportswear, with special attention paid to fabric material and color consistency. Fabric material score: 10; Fabric length score: 5; Color consistency score: 8; Pattern accuracy score: 7 Weight: Fabric Material: Fabric Length: Color consistency: Pattern accuracy: Speed ​​adjustment: Assuming the base speed k = 65 (unit: m / min), the adjustment coefficient α = 1.8, the speed adjustment calculation is: SA=k·(1+α·(W m +W l +W c +W p ))=182 m / min

[0019] The weaving quality assessment device under the self-prompting parametric speed regulation is composed of a speed adjustment module, a quality detection module, a feedback optimization module, a data recording device and a positioning sensor. It has the ability to monitor, automatically adjust and optimize the weaving process in real time, and significantly improve the weaving quality and production efficiency. Specifically, the speed adjustment module uses a rectangular metal plate with a circumferential cut array, combined with a micro-flow convergence structure, to accurately guide and adjust the weaving speed; the quality detection module monitors the weaving quality in real time through a high-frequency motor and an eccentric wheel; the feedback optimization module automatically adjusts the speed based on the quality data. In addition, the positioning sensor monitors the state of the fabric in real time to ensure precise control. Users can adjust the speed independently according to the monitoring results to meet personalized production needs, and the data recording device supports subsequent analysis and model optimization.

[0020] The human eye perceived color difference evaluation strategy for printing speed matching is based on the color components and geometric relationships of the patterns printed by the color high-speed jet color loom, and aims to regulate the color transition and pattern connection at the boundaries of adjacent weaving and printing areas. The strategy includes four control methods: overall speed regulation, local speed regulation, continuous speed regulation and discrete speed regulation. Combining the color component distribution, color characteristics and human eye perception threshold, the system will formulate an adaptive inkjet component ratio and stacking order to improve the quality and efficiency of weaving and printing. Specifically, the system will consider factors such as visual threshold, surface texture and slope of the weaving and printing area, and automatically optimize the speed regulation strategy to achieve more accurate color transition and pattern connection.

[0021] The multi-parameter defect real-time marking and local case output simulation model refers to a defect case library based on a high-speed jet color loom, which realizes real-time monitoring and marking analysis of fabric defects. The model builds a quantitative relationship between objective measurement and subjective evaluation by supporting convolutional neural networks (CNN). Among them, objective measurement covers surface defects, line defects and color defects; subjective evaluation includes user satisfaction with fabric quality and visual defect scores. The model calculates weight values ​​through difference calculation and nonlinear fitting, and effectively marks and classifies defects. The key functional points of this model include real-time monitoring, quantitative relationship modeling and effective defect classification.

[0022] In summary, the "intelligent speed regulation training method and system for high-speed jet color loom" of the present invention is efficient and accurate, and can monitor and adjust the operating status of the high-speed jet color loom in real time, and optimize the speed and quality of the fabric production process. The program effectively reduces production defects, improves the consistency and quality of fabrics, provides innovative solutions for the weaving industry, and promotes the further development of intelligent weaving equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings: Figure 1 Flowchart of the present invention DETAILED DESCRIPTION

[0024] In order to realize the intelligent speed regulation training method and system of the high-speed jet color loom, the present invention specifically adopts the following technical solutions: An intelligent speed regulation training method and system for high-speed jet color loom based on intelligent algorithm can optimize the speed and quality of fabric production process through real-time data analysis and user-defined parameters. The specific operation steps include: 1) Input order information and pattern: First, the user inputs the detailed information of the order and the fabric pattern, and the system analyzes its characteristics, determines the appropriate speed stratification strategy, and selects the speed regulation mode. 2) Generate speed and color control signals: Based on the analysis results, the system generates speed adjustment signals and color control signals to adjust the weaving and printing speeds in different areas to ensure that the color and quality of the fabric in different areas meet the standards. 3) Calculate speed adjustment requirements: The system calculates the speed adjustment required for each area and generates corresponding control signals to ensure the production consistency of fabrics in each area. 4) Real-time determination of speed control strategy: Based on user-defined multi-parameters, the system determines the speed control strategy in real time and dynamically adjusts the loom speed to match the changes in fabric pattern to ensure efficient production. 5) Real-time identification of fabric defects: Using the color CLE Lab model, the system identifies fabric defects in real time and dynamically adjusts the speed according to the defect type and location to optimize the weaving and printing quality and reduce the production of defective products. 6) Adjust the weaving speed and generate reports: After the layering is completed, the system adjusts the weaving speed according to the defect detection results, records the defect information, and generates a detailed report for subsequent analysis and improvement.

[0025] The multi-parameter simulation model under the actual global weaving speed refers to a comprehensive analysis of real-time speed data and multiple parameters based on the weaving process of a high-speed jet color loom. The model includes objective measurements and subjective evaluations of multiple key parameters, wherein objective measurements include four measurement parameters: fabric material, fabric length, color consistency, and pattern accuracy, and subjective evaluation covers user satisfaction and visual quality scores. The quantitative relationship model between objective measurement and subjective evaluation is to calculate the difference between the objective and subjective parameters of each sample, and use the random forest algorithm for nonlinear fitting to obtain the weight value, so as to achieve effective prediction and optimization of weaving quality. The weight calculation of objective measurement is to analyze the influence of the four parameters of fabric material, fabric length, color consistency, and pattern accuracy on product quality based on previous production data, collect relevant data of past production, including product quality indicators and production parameters, find out the relationship between each parameter and the quality of the final product through regression analysis, and give a score. The score is based on a 1-10 scoring standard (1 point is the worst and 10 is the best). After the four parameters are scored, normalization calculation is performed, and the calculation formula is as follows: Where W i is the weight of the i-th parameter, S i is the score of the ith parameter and n is the total number of parameters. According to the calculated weight, the speed of the fabric can be determined. Generally speaking, the higher the weight, the faster the speed of the fabric can be set during production, because it means that this parameter has less impact on the quality. The specific speed calculation method can be as follows, SA is the speed adjustment formula: SA=k·(1+α·(W m +W l +W c +W p )) Where k is a reference speed (unit: m / min). m : The weight of the fabric material; W l : Weight of fabric length; W c : Weight of color consistency; W p: The weight of the pattern accuracy. α: The adjustment coefficient, which controls the amplitude of the speed adjustment. By adjusting the value of α, the sensitivity of the speed adjustment can be controlled. For example, if α = 0.5, the influence of each parameter will only affect half of the base speed. If the sum of all weights is 0, then the speed remains the same; if the sum of weights is positive, then the speed increases; if the sum of weights is negative, then the speed decreases.

[0026] The range of α is defined as: 0.5≤α<1.0: Suitable for smaller adjustment requirements and usually used in stable production processes. 1.0≤α<2.0: Applicable to production environments that require rapid response to changes, such as high-end customization or complex pattern printing. α≥2.0: Can be used in special circumstances, such as production scenarios that require great changes, but should be used with caution to avoid affecting fabric quality.

[0027] Case 1: Background: Producing home fabrics, focusing on fabric durability and pattern accuracy. Fabric material score: 8; Fabric length score: 6; Color consistency score: 7; Pattern accuracy score: 9 Weight: Fabric Material: Fabric Length: Color consistency: Pattern accuracy: Speed ​​adjustment: Assuming the base speed k = 60 (unit: m / min), the adjustment coefficient α = 1.0, the speed adjustment calculation is: SA=k·(1+α·(W m +W l +W c +W p ))=120 m / min

[0028] Case 2: Background: High-end silk products, focusing on fabric material and color consistency. Fabric material score: 9; Fabric length score: 4; Color consistency score: 10; Pattern accuracy score: 8 Weight: Fabric Material: Fabric Length: Color consistency: Pattern accuracy: Speed ​​adjustment: Assuming the base speed k = 50 (unit: m / min), the adjustment coefficient α = 1.5, the speed adjustment calculation is: SA=k·(1+α·(W m +Wl +W c +W p ))=125 m / min

[0029] Case 3: Background: Wear-resistant outdoor sportswear, with special attention paid to fabric material and color consistency. Fabric material score: 10; Fabric length score: 5; Color consistency score: 8; Pattern accuracy score: 7 Weight: Fabric Material: Fabric Length: Color consistency: Pattern accuracy: Speed ​​adjustment: Assuming the base speed k = 65 (unit: m / min), the adjustment coefficient α = 1.8, the speed adjustment calculation is: SA=k·(1+α·(W m +W l +W c +W p ))=182 m / min

[0030] The weaving quality evaluation device under the self-prompting parameterized speed regulation is composed of a speed adjustment module, a quality detection module, a feedback optimization module, a data recording device and a positioning sensor, and has the functions of real-time monitoring, automatic adjustment and optimization of the weaving process, thereby significantly improving the weaving quality and production efficiency. Specifically, the speed adjustment module adopts a rectangular metal plate with a circumferential cut array, equipped with a micro-flow convergence structure to accurately guide the weaving speed and perform cutting adjustment; the quality detection module monitors the quality changes in the weaving process in real time through high-frequency motors, connecting rods, eccentric wheels and other components; the feedback optimization module automatically adjusts the weaving speed based on the quality detection data. In addition, the positioning sensor adopts a high-precision photoelectric sensor to monitor the running state and position changes of the fabric in real time to ensure accurate control of the weaving process. Users can also adjust the speed parameters autonomously according to the real-time monitoring results to achieve personalized production needs; the data recording device and the positioning sensor are installed in parallel at the four ends of the lower surface of the speed adjustment module to collect and record the various parameter data in the weaving process, providing support for subsequent data analysis and model optimization.

[0031] The human eye perception color difference evaluation strategy for printing speed matching refers to starting from the understanding of the color components and geometric relationships of the patterns printed by the color high-speed jet color loom, with the goal of regulating the color transition and pattern connection between the current adjacent weaving and printing area boundaries, forming four types of control methods, including overall speed regulation and local speed regulation in the perimeter direction of the weaving and printing area, and continuous speed regulation and discrete speed regulation in the weaving and printing construction direction. Combined with the distribution data and color characteristics of the color components of the weaving and printing area, the component proportion and stacking order of the inkjet in the color weaving and printing area are comprehensively planned according to the visual threshold, surface texture and slope of the weaving and printing area, and other planning ideas with adaptive control. Specifically, the system will formulate a set of speed regulation strategies for the weaving and printing area based on the color components and geometric relationships of the weaving and printing patterns, as well as the human eye's perception threshold of color differences. This set of strategies aims to achieve color transition and pattern connection between weaving and printing areas to improve weaving and printing quality and efficiency. In the process of achieving this goal, the system will take into account various factors, such as the distribution data of color components, color characteristics, visual threshold, surface texture and slope of the weaving and printing area. Based on these factors, the system adaptively adjusts the inkjet composition ratio and stacking order to optimize the weaving effect.

[0032] The multi-parameter defect real-time marking and local case output simulation model refers to real-time monitoring and marking analysis of fabric defects based on the defect case library collected during the production process of high-speed jet color looms. The model constructs a quantitative relationship model between objective measurement and subjective evaluation of defect recognition and classification by supporting convolutional neural network (CNN) training. Among them, the objective measurement includes three parameters: surface defects, line defects and color defects; the subjective evaluation covers the user's satisfaction with fabric quality and visual defect scores. The quantitative relationship model between objective measurement and subjective evaluation is achieved by calculating the difference between the objective measurement and subjective evaluation parameters of each sample, and using the support convolutional neural network for nonlinear fitting, so as to obtain the weight value, so as to achieve effective marking and classification of defects.

[0033] What needs to be explained about the specific structure of the present invention is that the connection relationship between the various component modules adopted in the present invention is definite and feasible. Except for the special instructions in the embodiments, the specific connection relationship can bring about the corresponding technical effects, and solve the technical problems raised by the present invention without relying on the execution of the corresponding software program. Except for the specific instructions, the models and connection methods of the components, modules and specific components appearing in the present invention belong to the existing technologies such as published patents, published journal articles, or common knowledge that can be obtained by technical personnel in this field before the application date, and there is no need to elaborate, so that the technical solution provided in this case is clear, complete and feasible, and the corresponding physical products can be reproduced or obtained according to the technical means.

[0034] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent speed regulation training method and system for a high-speed air jet color loom, characterized in that: Based on adaptive weaving and printing parameter control and intelligent feedback mechanism, combined with the comprehensive strategy of multi-parameter simulation, quality assessment and defect detection, through the multi-parameter real-time monitoring system and intelligent speed control module of the high-speed jet color loom, accurate process control and automatic adjustment are achieved at different weaving and printing speeds; the specific implementation method includes the following steps: S1: Enter orders and patterns to determine strategies based on analysis characteristics; S2: Calculate the weights of multiple variables to adjust the overall speed of the fabric; S3: Automatically prompt the speed control parameters for threshold alarm; S4: Adaptive speed adjustment to match real-time changes in multiple parameters; S5: Identification of fabric defects based on the color CLE Lab model; S6: Dynamically adjust the weaving and printing speed and generate reports based on the defect detection results.

2. The intelligent speed regulation training program for a high-speed air jet color loom according to claim 1 is characterized in that: The step S1 specifically includes: Order information and patterns are input into the system, and the input data is analyzed through advanced feature extraction algorithms to determine the optimal speed layering strategy. The appropriate speed regulation mode is selected based on the fabric characteristics and pattern complexity to achieve dynamic optimization and performance improvement of the weaving and printing process.

3. The intelligent speed regulation training program for a high-speed air jet color loom according to claim 2 is characterized in that: The step S2 specifically includes: According to the results of layered analysis of the pattern, a dynamic control algorithm is used to generate precise speed adjustment signals and color control signals. Through the regional speed regulation mechanism, refined speed control is implemented in each weaving and printing area to ensure the consistency of fabric surface quality and the accuracy of color expression.

4. The intelligent speed regulation training program for a high-speed air jet color loom according to claim 3 is characterized in that: The step S3 specifically includes: Automatically calculate the dynamic speed requirements of each weaving and printing area, and use the adaptive control algorithm to generate the corresponding control signal to achieve consistency and coordination of regional speeds; if there is a difference between the actual speed and the predicted speed, the system will automatically prompt the user to ensure the overall quality and performance stability of the fabric during the production process.

5. The intelligent speed regulation training program for a high-speed air jet color loom according to claim 4 is characterized in that: The step S4 specifically includes: In the system, according to the customized dynamic speed matching strategy, based on the user-defined multiple parameters such as fabric texture, color, pattern complexity, etc., the speed regulation strategy is determined in real time through advanced data analysis and machine learning algorithms; the program can dynamically adjust the operating speed of the jet color loom according to the changes in these parameters to match the changes in the pattern; especially during the weaving process, if the color or complexity of the pattern changes, the system will automatically adjust the speed to ensure the weaving quality and efficiency; at the same time, the system will automatically learn and optimize the speed regulation strategy based on real-time operating data to further improve the operating efficiency and product quality of the jet color loom.

6. The intelligent speed regulation training program for a high-speed air jet color loom according to claim 5 is characterized in that: The step S5 specifically includes: Based on the color CIE Lab model, the color characteristics of the fabric surface are analyzed in real time, and the convolutional neural network (CNN) algorithm is used to identify and judge fabric defects that are difficult for the human eye to detect. Through the correlation analysis of defect feature data and speed control parameters, the linear regression model algorithm is used to dynamically optimize the speed control strategy, which specifically includes constructing a mathematical relationship between defect characteristics and speed control parameters, and optimizing the weaving and printing speed through weight calculation to improve production efficiency and ensure the consistency and stability of product quality. In addition, the system combines machine learning models for predictive analysis, identifies potential defect trends, and realizes active management of fabric quality through feedforward control to optimize quality monitoring during the production process.

7. The intelligent speed regulation training program for a high-speed jet color loom according to claim 6 is characterized in that: The step S6 specifically includes: After the layering is completed, according to the multifunctional weaving and printing intelligent simulation model, the weaving and printing speed is dynamically adjusted to compensate for the fabric defects based on the defect information obtained by the real-time defect detection module; the system generates a corresponding speed adjustment strategy by analyzing the type and severity of the defects; at the same time, all detected defect data is recorded and a detailed quality report is generated, including the nature, location and treatment suggestions of the defects, to ensure the traceability of the entire weaving and printing process, quality monitoring and optimization of production efficiency.

8. An intelligent speed regulation training system for a high-speed jet color loom, characterized in that: It comprises a high-speed jet color loom, a multi-parameter real-time monitoring module integrated in a control unit of the high-speed jet color loom, an intelligent speed control module and a defect detection and marking module, wherein the module function is an operation module of an intelligent speed regulation training program for a high-speed jet color loom as claimed in any one of claims 1 to 5; The intelligent speed regulation training program is mainly composed of a multi-parameter surface detection algorithm, a linear feature extraction algorithm, a color detection algorithm based on color gamut analysis, and a defect module; defect data can be recorded and saved, and has the function of exporting and viewing to support subsequent quality analysis and management; The dynamic speed control model automatically adjusts the weaving speed according to the real-time monitored process parameters through an adaptive optimization algorithm to ensure stable output of fabrics under different production conditions. At the same time, the model also supports manual speed adjustment, allowing the operator to flexibly adjust the weaving speed according to actual needs, and provides self-prompt optimization suggestions during the speed adjustment process to further improve production efficiency and fabric quality. The color difference detection algorithm adopts the CLE LAB color difference analysis model, combined with the program's built-in visual perception model, to automatically detect color changes during the speed regulation process, and compare them with the target color to generate a real-time color difference evaluation report to ensure color consistency.

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