Embroidery pattern intelligent optimization processing system and method

Abstract

The invention discloses an intelligent optimization processing system and method for an embroidery pattern, and relates to the technical field of pattern processing. When the system runs, embroidery pattern data are collected from multiple sources, key feature points and line information of the pattern are recognized and extracted, feature analysis is performed on the embroidery pattern, and according to different embroidery requirements, the embroidery pattern is subjected to intelligent optimization processing; the method comprises the following steps: segmenting a pattern into a plurality of regions, obtaining pattern feature points, calculating an embroidery path length L by adopting an optimization algorithm based on the segmented pattern regions, ensuring the minimization of an embroidery stitch and the smoothness and coherence of the whole pattern, and automatically adjusting parameters of an embroidery machine according to the complexity and density of the pattern and the attribute of a selected material. The method comprises the following steps: performing analogue simulation on an optimized embroidery path and parameters, pre-estimating an effect after embroidery is completed, calculating to obtain an embroidery pattern quality prediction index Cyzl, performing threshold comparison according to a calculation result to obtain quality prediction grade evaluation, proposing an optimization suggestion, and generating a final embroidery pattern file.

Description

Technical Field

[0001] The present invention relates to the technical field of pattern processing, and in particular to an embroidery pattern intelligent optimization processing system and method. Background Art

[0002] As a traditional decorative art form, the quality and effect of embroidery patterns largely depend on pattern design, needle selection, and the precision and parameter settings of the embroidery machine. With the development of modern technology, embroidery craftsmanship has gradually shifted from manual operation to digital control, using computer-aided design and CNC embroidery machines for large-scale production. However, although the hardware capabilities of embroidery machines have been greatly improved, how to improve the quality and production efficiency of embroidery patterns while meeting diverse design requirements remains a challenge. In the current embroidery production process, embroidery parameters and paths are adjusted based on manual experience and simple automation tools, which often leads to problems such as unstable embroidery quality and low production efficiency. For example, different pattern complexities and material properties require different stitch density and speed settings, but existing embroidery equipment is usually difficult to achieve adaptive adjustment according to actual needs. In addition, the optimization of embroidery paths is mostly based on manual settings or basic algorithms, without considering the overall coherence of the pattern and the adaptability of the material. In this case, problems such as frequent needle changes, long embroidery time, and uneven embroidery density often occur during the embroidery process, resulting in the pattern quality being difficult to meet high-standard market demands.

[0003] The shortcomings of the existing embroidery production system mainly come from the following aspects: first, the existing system has limited ability to analyze and identify the characteristics of embroidery patterns, and cannot perform detailed segmentation and optimization based on the characteristics of different patterns; second, the traditional path planning algorithm cannot fully consider the diversity of material properties and pattern complexity in the embroidery process, resulting in discontinuous embroidery paths, long embroidery time, and low efficiency; finally, the lack of effective quality prediction and optimization feedback mechanism makes it difficult to stably control the quality of the embroidery patterns produced. These shortcomings will bring a series of abnormal effects, such as distortion of pattern details caused by uneven stitch density, increased material waste, and excessive embroidery time and cost, which seriously affect the overall operating efficiency of the production line and the market competitiveness of the product. Therefore, it is necessary to develop an embroidery pattern intelligent optimization processing system with intelligent pattern optimization, path optimization, parameter adaptive adjustment and quality prediction functions to comprehensively improve the automation level of embroidery technology and product quality. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an embroidery pattern intelligent optimization processing system and method, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an embroidery pattern intelligent optimization processing system, including a pattern data acquisition module, a pattern feature analysis module, an embroidery path optimization module, an embroidery parameter adaptive adjustment module, an embroidery simulation quality prediction module and an optimization result output module; The pattern data acquisition module is used to collect embroidery pattern data from multiple sources, and identify and extract key feature points and line information of the pattern; The pattern feature analysis module is used to perform feature analysis on the embroidery pattern using image processing and pattern recognition algorithms, identify the features of edges, textures and color blocks in the pattern, divide the pattern into multiple areas according to different embroidery requirements, and obtain pattern feature points; The embroidery path optimization module is used to calculate the embroidery path length L based on the segmented pattern area using an optimization algorithm to ensure the minimization of embroidery stitches and the smoothness and continuity of the overall pattern; The embroidery parameter adaptive adjustment module is used to automatically adjust the parameters of the embroidery machine, including stitch density, speed and needle type, according to the complexity and density of the pattern and the properties of the selected material; The embroidery simulation quality prediction module is used to simulate the optimized embroidery path and parameters, estimate the effect after the embroidery is completed, calculate the embroidery pattern quality prediction index Cyzl, perform threshold comparison according to the calculation result to obtain the quality prediction level evaluation, and put forward optimization suggestions; The optimization result output module is used to generate the final embroidery pattern file, support multiple file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization result in actual operation, and generate a report of the optimization process.

[0006] Preferably, the pattern data acquisition module includes a pattern input unit, a pattern preprocessing unit and a feature point extraction unit; The pattern input unit is used to obtain digital data of embroidery patterns through a scanner, camera or design software, receive and input embroidery pattern data, and supports scanned images, vector files and digital design file input formats; The pattern preprocessing unit is used to preprocess the input pattern data, including image denoising, resolution adjustment and color balancing operations; The feature point extraction unit is used to identify and extract key feature points and line information of the pattern, and generate digital feature data of the pattern.

[0007] Preferably, the pattern feature analysis module includes an edge detection unit, a pattern recognition unit and a region segmentation unit; The edge detection unit uses an image processing algorithm to perform edge detection on the pattern, extract edge features of the pattern, and generate edge data for subsequent segmentation and path optimization; The pattern recognition unit uses a pattern recognition algorithm to analyze the texture features and color block distribution in the pattern, and calculates the pattern complexity coefficient C according to the number of feature points, edge length, number and distribution of color blocks in the pattern. f ; The pattern complexity coefficient C f Calculated by the following formula: ; Where N points Indicates the number of feature points in the pattern, L edges Represents the total length of the pattern edge, N colors Indicates the number of different color blocks, Respectively represent weight coefficients; The region segmentation unit is used to segment the pattern into multiple regions according to embroidery requirements, each region contains its feature point information, obtains the embroidery speed v and the embroidery head replacement time C, and generates a data structure for embroidery path optimization.

[0008] Preferably, the embroidery path optimization module includes a path generation unit and a path continuity correction unit; The path generation unit is used to generate an initial embroidery path based on the segmented pattern area by using a path optimization algorithm, calculate the optimal embroidery path from one feature point to another feature point, reduce the total stitch moving distance, obtain the embroidery path length L, and smooth the initially generated embroidery path to reduce the vibration of the embroidery machine; The embroidery path length L is calculated by the following formula: ; Where P i and P i +1 are two consecutive points on the path, d(P i , P i +1) is the distance between two consecutive points; The path continuity correction unit is used to perform continuity correction on the embroidery path to ensure the overall consistency of the pattern, avoid needle changes and empty runs, and obtain the embroidery time T after optimization: ; Among them, L represents the embroidery path length, v represents the embroidery speed, and C represents the constant of the embroidery head replacement time.

[0009] Preferably, the embroidery parameter adaptive adjustment module includes an embroidery density adjustment unit and an embroidery speed adjustment unit; The embroidery density adjustment unit is used to adjust the embroidery density according to the pattern complexity Cf and material properties M, automatically adjust stitch density , ensuring the consistency and quality of embroidery in different areas; The embroidery speed adjustment unit is used to automatically select a suitable needle type based on the physical properties of the material and the specific requirements of the pattern, and dynamically adjust the embroidery speed v according to the pattern characteristics and material properties to adapt to different embroidery requirements; Adjust embroidery density and stitch density according to the complexity of the pattern and the characteristics of the material Calculated by the following formula: ; in, is the weight coefficient; The embroidery speed v is automatically adjusted according to the pattern complexity and material characteristics. The embroidery speed v is calculated by the following formula: ; in, is the empirical coefficient, C f The higher it is, the lower the embroidery speed v will be.

[0010] Preferably, the material property M is calculated by the following formula: ; Among them, m i represents the material properties, λ i Represents the material property m i The weight coefficient of .

[0011] Preferably, the embroidery simulation quality prediction module includes an embroidery path simulation unit and a parameter effect calculation unit; The embroidery path simulation unit is used to simulate the optimized embroidery path and estimate the effect after the embroidery is completed; The parameter effect calculation unit is used to use the embroidery pattern quality prediction index Cyzl formula to perform quality prediction and evaluate the final quality of the embroidery pattern; The embroidery pattern quality prediction index Cyzl is calculated by the following formula: ; Where Cyzl represents the embroidery pattern quality prediction index, C f Indicates the complexity of the pattern, L indicates the length of the embroidery path, and T indicates the embroidery time. represents the embroidery density, v represents the embroidery speed, M represents the material properties, w1, w2, w3, w4, w5 and w6 represent the pattern complexity C respectively. f , embroidery path length L, embroidery time T, embroidery density , embroidery speed v and weight coefficient of material property M.

[0012] Preferably, the embroidery simulation quality prediction module further includes a quality evaluation unit; The quality evaluation unit is used to divide the embroidery quality into three grades according to the comparison between the embroidery pattern quality prediction index Cyzl and the preset quality qualified threshold Z and the preset substandard threshold X: If the embroidery pattern quality prediction index Cyzl > Z, the pattern embroidery quality is of excellent grade, indicating that the stitches of the embroidery pattern are fine, the pattern is clear, the color is accurate, and the material is well matched, without further optimization; If the substandard threshold X < Cyzl ≤ Z, the pattern embroidery quality is of qualified grade, indicating that the quality of the embroidery pattern meets the requirements, but there are areas for optimization. Call the path coherence correction unit to re-evaluate and optimize the current embroidery path, reduce the redundant stitch movement and the embroidery path length L to improve the embroidery efficiency and overall aesthetics; If the embroidery pattern quality prediction index Cyzl ≤ X, the pattern embroidery quality is of substandard grade, indicating that the quality of the embroidery pattern does not meet the requirements, there are large quality defects or mismatches between the design and the material. Call the parameter effect evaluation unit to comprehensively analyze all influencing factors, including the pattern complexity C f , the embroidery path length L, the embroidery density and the material property M to identify the causes of quality problems.

[0013] Preferably, the optimization result output module includes a file format conversion unit and an optimization report generation unit; The file format conversion unit is used to convert the final embroidery pattern, optimization path and parameter configuration into file formats suitable for different embroidery machines to ensure the compatibility and executability of the output results; The optimization report generation unit is used to generate a detailed report on the optimization process, including parameter adjustment, path optimization and quality prediction steps.

[0014] An intelligent optimization processing method for embroidery patterns includes the following steps: Step 1: Collect embroidery pattern data from multiple sources, identify and extract the key feature points and line information of the pattern; Step 2: Use image processing and pattern recognition algorithms to perform feature analysis on the embroidery pattern, identify the features of the edges, textures and color blocks in the pattern, and divide the pattern into multiple regions according to different embroidery requirements to obtain the pattern feature points; Step 3: Based on the divided pattern regions, use an optimization algorithm to calculate the embroidery path length L to ensure the minimization of embroidery stitches and the smoothness and coherence of the overall pattern; Step 4: Automatically adjust the embroidery machine parameters, including stitch density, speed and needle type, according to the complexity and density of the pattern and the properties of the selected material; Step 5: By simulating the optimized embroidery path and parameters, the effect after embroidery is estimated, and the embroidery pattern quality prediction index Cyzl is calculated. According to the calculation results, the threshold value is compared to obtain the quality prediction level evaluation, and optimization suggestions are put forward; Step 6: Generate the final embroidery pattern file, support various file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization results in actual operation, and generate a report of the optimization process.

[0015] The present invention provides an embroidery pattern intelligent optimization processing system and method, which has the following beneficial effects: (1) When the system is running, it collects embroidery pattern data from multiple sources, identifies and extracts the key feature points and line information of the pattern, performs feature analysis on the embroidery pattern, divides the pattern into multiple areas according to different embroidery requirements, obtains the pattern feature points, and uses an optimization algorithm to calculate the embroidery path length L based on the segmented pattern areas to ensure the minimization of embroidery stitches and the smoothness and continuity of the overall pattern. It automatically adjusts the parameters of the embroidery machine according to the complexity, density and properties of the selected material of the pattern. It simulates the optimized embroidery path and parameters to estimate the effect after embroidery is completed, and calculates the embroidery pattern quality prediction index Cyzl. It performs threshold comparison based on the calculation results to obtain the quality prediction level evaluation, and puts forward optimization suggestions to generate the final embroidery pattern file.

[0016] (2) This embroidery pattern intelligent optimization processing system integrates six modules: pattern data acquisition, pattern feature analysis, embroidery path optimization, embroidery parameter adaptive adjustment, embroidery simulation quality prediction and optimization result output. From the input of the embroidery pattern to the optimized output, a complete set of automated processes has been formed. The system first accurately obtains the digital information of the pattern through the pattern data acquisition module, and then identifies and extracts the key feature points through the pattern feature analysis module to ensure that the embroidery pattern has sufficient data support at the design end. On this basis, the embroidery path optimization module and the embroidery parameter adaptive adjustment module optimize the embroidery path and process parameters through intelligent algorithms to achieve the continuity, smoothness and efficiency of the embroidery process. Finally, the embroidery simulation quality prediction module and the optimization result output module can predict the embroidery quality in advance, make timely adjustments and optimizations, and output optimization solutions that are compatible with different devices, thereby effectively improving production efficiency and embroidery quality.

[0017] (3) The various modules of the system work together to realize the automation of the entire process of embroidery pattern collection, analysis, optimization and output. Through the pattern feature analysis module and the embroidery path optimization module, the system can automatically segment the pattern and calculate the optimal embroidery path, reduce the stitch movement distance, and shorten the embroidery time; while the embroidery parameter adaptive adjustment module automatically adjusts the embroidery density, speed and needle type according to the pattern complexity and material characteristics to ensure the accuracy and effect of the embroidery process. These intelligent processes greatly reduce the need for manual intervention and solve the problems of high pattern complexity and large material diversity in traditional embroidery production, successfully achieving the goal of optimizing the embroidery process, improving embroidery efficiency and quality stability.

[0018] (4) Compared with the current embroidery production system that relies on manual experience adjustment and simple automated algorithms, this system achieves full process optimization and intelligence from design to production through intelligent algorithms and adaptive adjustment technology. Compared with traditional methods, the system not only greatly reduces the redundancy of embroidery paths and embroidery time, and improves production efficiency, but also reduces quality defects and rework rates caused by material characteristics or pattern design problems through intelligent parameter adjustment and simulation quality prediction. Especially in the application scenarios of complex patterns and diversified materials, this system can significantly improve the consistency and fineness of embroidery quality and reduce production costs and energy consumption. In summary, this system has brought about a comprehensive improvement in the embroidery industry from process flow to final product quality, and provided technical support and development direction for future intelligent embroidery production. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a block diagram of an embroidery pattern intelligent optimization processing system of the present invention; Figure 2 The present invention is a schematic diagram of the steps of an embroidery pattern intelligent optimization processing method. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 The present invention provides an embroidery pattern intelligent optimization processing system, please refer to Figure 1 , including a pattern data acquisition module, a pattern feature analysis module, an embroidery path optimization module, an embroidery parameter adaptive adjustment module, an embroidery simulation quality prediction module and an optimization result output module; The pattern data acquisition module is used to collect embroidery pattern data from multiple sources, and identify and extract key feature points and line information of the pattern; The pattern feature analysis module is used to perform feature analysis on the embroidery pattern using image processing and pattern recognition algorithms, identify the features of edges, textures and color blocks in the pattern, divide the pattern into multiple areas according to different embroidery requirements, and obtain pattern feature points; The embroidery path optimization module is used to calculate the embroidery path length L based on the segmented pattern area using an optimization algorithm to ensure the minimization of embroidery stitches and the smoothness and continuity of the overall pattern; The embroidery parameter adaptive adjustment module is used to automatically adjust the parameters of the embroidery machine, including stitch density, speed and needle type, according to the complexity and density of the pattern and the properties of the selected material; The embroidery simulation quality prediction module is used to simulate the optimized embroidery path and parameters, estimate the effect after the embroidery is completed, calculate the embroidery pattern quality prediction index Cyzl, perform threshold comparison according to the calculation result to obtain the quality prediction level evaluation, and put forward optimization suggestions; The optimization result output module is used to generate the final embroidery pattern file, support multiple file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization result in actual operation, and generate a report of the optimization process.

[0022] In this embodiment, embroidery pattern data is collected from multiple sources, key feature points and line information of the pattern are identified and extracted, image processing and pattern recognition algorithms are used to perform feature analysis on the embroidery pattern, and features of edges, textures and color blocks in the pattern are identified. According to different embroidery requirements, the pattern is divided into multiple areas to obtain pattern feature points. Based on the divided pattern areas, an optimization algorithm is used to calculate the embroidery path length L to ensure the minimization of embroidery stitches and the smoothness and continuity of the overall pattern. According to the complexity and density of the pattern and the properties of the selected material, the parameters of the embroidery machine, including stitch density, speed and needle type, are automatically adjusted. By simulating the optimized embroidery path and parameters, the effect after embroidery is completed is estimated, and the embroidery pattern quality prediction index Cyzl is calculated. According to the calculation results, a threshold comparison is performed to obtain a quality prediction grade evaluation, and optimization suggestions are put forward to generate a final embroidery pattern file, support multiple file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization results in actual operation, and generate a report on the optimization process.

[0023] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the pattern data acquisition module includes a pattern input unit, a pattern preprocessing unit and a feature point extraction unit; The pattern input unit is used to obtain digital data of embroidery patterns through a scanner, camera or design software, receive and input embroidery pattern data, and supports scanned images, vector files and digital design file input formats; The pattern preprocessing unit is used to preprocess the input pattern data, including image denoising, resolution adjustment and color balancing operations; The feature point extraction unit is used to identify and extract key feature points and line information of the pattern, and generate digital feature data of the pattern.

[0024] The pattern feature analysis module includes an edge detection unit, a pattern recognition unit and a region segmentation unit; The edge detection unit uses an image processing algorithm to perform edge detection on the pattern, extract edge features of the pattern, and generate edge data for subsequent segmentation and path optimization; The pattern recognition unit uses a pattern recognition algorithm to analyze the texture features and color block distribution in the pattern, and calculates the pattern complexity coefficient C according to the number of feature points, edge length, number and distribution of color blocks in the pattern. f ; The pattern complexity coefficient C f Calculated by the following formula: ; Where N points Indicates the number of feature points in the pattern, L edges Represents the total length of the pattern edge, N colors Indicates the number of different color blocks, Respectively represent weight coefficients; The region segmentation unit is used to segment the pattern into multiple regions according to embroidery requirements, each region contains its feature point information, obtains the embroidery speed v and the embroidery head replacement time C, and generates a data structure for embroidery path optimization.

[0025] The embroidery path optimization module includes a path generation unit and a path consistency correction unit; The path generation unit is used to generate an initial embroidery path based on the segmented pattern area by using a path optimization algorithm, calculate the optimal embroidery path from one feature point to another feature point, reduce the total stitch moving distance, obtain the embroidery path length L, and smooth the initially generated embroidery path to reduce the vibration of the embroidery machine; The embroidery path length L is calculated by the following formula: ; Where P i and P i +1 are two consecutive points on the path, d(Pi , P i +1) is the distance between two consecutive points; The path continuity correction unit is used to perform continuity correction on the embroidery path to ensure the overall consistency of the pattern, avoid needle changes and empty runs, and obtain the embroidery time T after optimization: ; Among them, L represents the embroidery path length, v represents the embroidery speed, and C represents the constant of the embroidery head replacement time.

[0026] In this embodiment, the system significantly improves the processing efficiency and quality of embroidery patterns by integrating multiple modules and units. First, the pattern data acquisition module realizes the automation of the entire process from embroidery pattern input to feature extraction. The pattern input unit supports data input in multiple formats, which increases the compatibility of the system; the pattern preprocessing unit improves the quality of pattern data through denoising, resolution adjustment and color balancing operations; the feature point extraction unit can automatically identify and extract key feature points and line information, providing high-precision data support for subsequent analysis. Compared with traditional manual preprocessing and feature extraction methods, this module reduces the need for manual intervention and improves the efficiency and accuracy of data processing.

[0027] Example 3 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the embroidery parameter adaptive adjustment module includes an embroidery density adjustment unit and an embroidery speed adjustment unit; The embroidery density adjustment unit is used to adjust the embroidery density according to the pattern complexity C f and material properties M, automatically adjust stitch density , ensuring the consistency and quality of embroidery in different areas; The embroidery speed adjustment unit is used to automatically select a suitable needle type based on the physical properties of the material and the specific requirements of the pattern, and dynamically adjust the embroidery speed v according to the pattern characteristics and material properties to adapt to different embroidery requirements; Adjust embroidery density and stitch density according to the complexity of the pattern and the characteristics of the material Calculated by the following formula: ; in, is the weight coefficient; The embroidery speed v is automatically adjusted according to the pattern complexity and material characteristics. The embroidery speed v is calculated by the following formula: ; in, is the empirical coefficient, C f The higher it is, the lower the embroidery speed v will be.

[0028] The material property M is calculated by the following formula: ; Among them, m i represents the material properties, λ i Represents the material property m i The weight coefficient of .

[0029] The embroidery simulation quality prediction module includes an embroidery path simulation unit and a parameter effect calculation unit; The embroidery path simulation unit is used to simulate the optimized embroidery path and estimate the effect after the embroidery is completed; The parameter effect calculation unit is used to use the embroidery pattern quality prediction index Cyzl formula to perform quality prediction and evaluate the final quality of the embroidery pattern; The embroidery pattern quality prediction index Cyzl is calculated by the following formula: ; Where Cyzl represents the embroidery pattern quality prediction index, C f Indicates the complexity of the pattern, L indicates the length of the embroidery path, and T indicates the embroidery time. represents the embroidery density, v represents the embroidery speed, M represents the material properties, w1, w2, w3, w4, w5 and w6 represent the pattern complexity C respectively. f , embroidery path length L, embroidery time T, embroidery density , embroidery speed v and weight coefficient of material property M.

[0030] The embroidery simulation quality prediction module also includes a quality assessment unit; The quality evaluation unit is used to classify the embroidery quality into three levels according to the comparison between the embroidery pattern quality prediction index Cyzl and the preset qualified quality threshold Z and the preset substandard threshold X: If the embroidery pattern quality prediction index Cyzl>Z, the pattern embroidery quality is of high quality, indicating that the embroidery pattern has fine stitches, clear patterns, accurate colors, and good material adaptation, and no further optimization is required. During the production process, the system will monitor the working status of the embroidery machine and embroidery parameters in real time, such as density, speed v, and needle type to see if they are within the optimal setting range. If the parameter fluctuation is detected to exceed the allowable range, it will be adjusted to the optimal setting immediately. After each batch of embroidery is completed, the system automatically calls the parameter calibration unit to recalibrate key embroidery parameters (such as stitch density, speed, and needle type) to ensure that the machine continues to maintain a high-quality level in the next batch of production; If the off-grade threshold X < Cyzl ≤ Z, the quality of the pattern embroidery is at the qualified level, indicating that the quality of the embroidered pattern meets the requirements, but there is room for optimization. Call the path coherence correction unit to re-evaluate and optimize the current embroidery path, reduce unnecessary stitch movements and the length L of the embroidery path, so as to improve the embroidery efficiency and overall aesthetics, and analyze the matching degree between the material property M and the current pattern feature complexity C f If it is found that the material does not fully match the pattern complexity, the system will recommend replacing the material or readjusting the embroidery density and speed to improve the fitness; If the embroidery pattern quality prediction index Cyzl ≤ X, the quality of the pattern embroidery is at the off-grade level, indicating that the quality of the embroidered pattern does not meet the requirements, there are large quality defects or mismatches between the design and the material. Call the parameter effect evaluation unit to comprehensively analyze all influencing factors, including the pattern complexity C f , the length L of the embroidery path, the embroidery density and the material property M, identify the causes of quality problems, and recommend a global reset of the embroidery parameters, including redesigning the embroidery path, optimizing the embroidery density and speed settings, and selecting a more suitable needle type and material, so as to fundamentally improve the embroidery quality.

[0031] In this embodiment, the embroidery parameter adaptive adjustment module and the simulation quality prediction module have significant beneficial effects in improving the quality and production efficiency of the embroidered pattern. First of all, the embroidery parameter adaptive adjustment module can, through the embroidery density adjustment unit and the embroidery speed adjustment unit, dynamically and adaptively adjust the stitch density and the embroidery speed v according to the pattern complexity C f and the material property M. In this way, the system not only ensures the consistency and high-quality output of embroidery in different regions, but also reduces the errors and inconsistencies that may be caused by manual parameter adjustment. Compared with the traditional fixed parameter setting method, this module realizes higher embroidery fineness and production flexibility through comprehensive analysis of the pattern and material properties.

[0032] Embodiment 4 This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1 , specifically: the optimization result output module includes a file format conversion unit and an optimization report generation unit; The file format conversion unit is used to convert the final embroidery pattern, optimization path and parameter configuration into a file format suitable for different embroidery machines, ensuring the compatibility and executability of the output results; The optimization report generation unit is used to generate a detailed report on the optimization process, including parameter adjustment, path optimization and quality prediction steps.

[0033] In this embodiment, the optimization result output module provides efficient support and guarantee for the final result output of the embroidery production process through the file format conversion unit and the optimization report generation unit. First, the file format conversion unit can convert the final optimized embroidery pattern, path and parameter configuration into a file format suitable for different embroidery machines, ensuring the compatibility and executability of the output results. This function effectively solves the problems of work interruption and file conversion difficulties caused by format incompatibility between different embroidery machines, and greatly improves the continuity and seamless connection of the embroidery production process.

[0034] Example 5 A method for intelligent optimization of embroidery patterns, please refer to Figure 2 , specifically: including the following steps: Step 1: Collect embroidery pattern data from multiple sources, identify and extract key feature points and line information of the pattern; Step 2: Use image processing and pattern recognition algorithms to perform feature analysis on the embroidery pattern, identify the features of the edges, textures, and color blocks in the pattern, divide the pattern into multiple areas according to different embroidery requirements, and obtain the pattern feature points; Step 3: Based on the segmented pattern area, an optimization algorithm is used to calculate the embroidery path length L to ensure the minimization of embroidery stitches and the smoothness and continuity of the overall pattern; Step 4: Automatically adjust the embroidery machine parameters, including stitch density, speed and needle type, according to the complexity and density of the pattern and the properties of the selected material; Step 5: By simulating the optimized embroidery path and parameters, the effect after embroidery is estimated, and the embroidery pattern quality prediction index Cyzl is calculated. According to the calculation results, the threshold value is compared to obtain the quality prediction level evaluation, and optimization suggestions are put forward; Step 6: Generate the final embroidery pattern file, support various file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization results in actual operation, and generate a report of the optimization process.

[0035] In this embodiment, the present invention provides an embroidery process automation control system based on pattern data analysis and optimization, which realizes intelligent optimization and efficient execution of embroidery patterns through six steps, bringing multiple beneficial effects. First, through the pattern data collection, feature analysis and path optimization processing of steps one to three, the accuracy and efficiency of the embroidery path are effectively improved. This data-driven path optimization strategy significantly reduces the idle running time and needle change frequency of the embroidery machine, and improves production efficiency and embroidery accuracy. In step four, the system automatically adjusts the operating parameters of the embroidery machine, such as stitch density, embroidery speed and needle type, according to the pattern complexity, density and material properties, to ensure that the embroidery process can best match the specific material and pattern requirements. This automated parameter adjustment mechanism not only improves the embroidery consistency and quality between different patterns and materials, but also reduces the errors and complexity in the manual adjustment process, realizes more efficient and more accurate embroidery control, and greatly improves the stability and quality of the production process. The simulation and optimization result output modules of steps five and six make the quality control and optimization process of the entire embroidery process more visual and manageable. By using simulation to predict the quality of the optimized embroidery path and parameters, the system can predict the effect after the embroidery is completed in advance, and perform graded evaluation based on the comparison between the quality prediction index Cyzl and the preset threshold to ensure that the embroidery quality meets the expected standards. Finally, the system can output optimized file formats and optimization reports suitable for a variety of embroidery machines to ensure the smooth implementation of the optimization results in actual operations. Compared with traditional embroidery technology, the breakthrough improvements in embroidery path optimization, parameter adaptive adjustment and quality prediction of this system have greatly improved the intelligence level of embroidery technology and the quality of finished products, providing important support for the automation and intelligent development of the embroidery industry.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An embroidery pattern intelligent optimization processing system, characterized in that: It includes a pattern data acquisition module, a pattern feature analysis module, an embroidery path optimization module, an embroidery parameter adaptive adjustment module, an embroidery simulation quality prediction module and an optimization result output module; The pattern data acquisition module is used to collect embroidery pattern data from multiple sources, identify and extract key feature points and line information of the pattern; The pattern feature analysis module is used to perform feature analysis on the embroidery pattern using image processing and pattern recognition algorithms, identify the features of edges, textures and color blocks in the pattern, divide the pattern into multiple areas according to different embroidery requirements, and obtain pattern feature points; The embroidery path optimization module is used to calculate the embroidery path length L based on the segmented pattern area using an optimization algorithm to ensure the minimization of embroidery stitches and the smoothness and continuity of the overall pattern; The embroidery parameter adaptive adjustment module is used to automatically adjust the parameters of the embroidery machine, including stitch density, speed and needle type, according to the complexity and density of the pattern and the properties of the selected material; The embroidery simulation quality prediction module is used to simulate the optimized embroidery path and parameters, estimate the effect after the embroidery is completed, calculate the embroidery pattern quality prediction index Cyzl, perform threshold comparison according to the calculation result to obtain the quality prediction level evaluation, and put forward optimization suggestions; The optimization result output module is used to generate the final embroidery pattern file, support multiple file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization result in actual operation, and generate a report of the optimization process.

2. The embroidery pattern intelligent optimization processing system according to claim 1, characterized in that: The pattern data acquisition module includes a pattern input unit, a pattern preprocessing unit and a feature point extraction unit; The pattern input unit is used to obtain digital data of embroidery patterns through a scanner, camera or design software, receive and input embroidery pattern data, and supports scanned images, vector files and digital design file input formats; The pattern preprocessing unit is used to preprocess the input pattern data, including image denoising, resolution adjustment and color balancing operations; The feature point extraction unit is used to identify and extract key feature points and line information of the pattern, and generate digital feature data of the pattern.

3. The embroidery pattern intelligent optimization processing system according to claim 1, characterized in that: The pattern feature analysis module includes an edge detection unit, a pattern recognition unit and a region segmentation unit; The edge detection unit uses an image processing algorithm to perform edge detection on the pattern, extract edge features of the pattern, and generate edge data for subsequent segmentation and path optimization; The pattern recognition unit uses a pattern recognition algorithm to analyze the texture features and color block distribution in the pattern, and calculates the pattern complexity coefficient C according to the number of feature points, edge length, number and distribution of color blocks in the pattern. f ; The pattern complexity coefficient C f Calculated by the following formula: ； Where N points Indicates the number of feature points in the pattern, L edges Represents the total length of the pattern edge, N colors Indicates the number of different color blocks, Respectively represent weight coefficients; The region segmentation unit is used to segment the pattern into multiple regions according to embroidery requirements, each region contains its feature point information, obtains the embroidery speed v and the embroidery head replacement time C, and generates a data structure for embroidery path optimization.

4. The embroidery pattern intelligent optimization processing system according to claim 1, characterized in that: The embroidery path optimization module includes a path generation unit and a path consistency correction unit; The path generation unit is used to generate an initial embroidery path based on the segmented pattern area by using a path optimization algorithm, calculate the optimal embroidery path from one feature point to another feature point, reduce the total stitch moving distance, obtain the embroidery path length L, and smooth the initially generated embroidery path to reduce the vibration of the embroidery machine; The length L of the embroidery path is calculated by the following formula: ； Where P i and P i +1 are two consecutive points on the path, d(P i , P i +1) is the distance between two consecutive points; The path continuity correction unit is used to perform continuity correction on the embroidery path to ensure the overall consistency of the pattern, avoid needle changes and empty runs, and obtain the embroidery time T after optimization: ; Where L represents the length of the embroidery path, v represents the embroidery speed, and C represents a constant for the embroidery head replacement time.

5. The embroidery pattern intelligent optimization processing system according to claim 1, characterized in that: The embroidery parameter adaptive adjustment module includes an embroidery density adjustment unit and an embroidery speed adjustment unit; The embroidery density adjustment unit is used to adjust the embroidery density according to the pattern complexity C f and material properties M, automatically adjust stitch density , ensuring the consistency and quality of embroidery in different areas; The embroidery speed adjustment unit is used to automatically select a suitable needle type based on the physical properties of the material and the specific requirements of the pattern, and dynamically adjust the embroidery speed v according to the pattern characteristics and material properties to meet different embroidery needs; Adjust embroidery density and stitch density according to the complexity of the pattern and the characteristics of the material Calculated by the following formula: ； in, is the weight coefficient; Automatically adjust the embroidery speed v according to the pattern complexity and material characteristics. The embroidery speed v is calculated by the following formula: ； in, is the empirical coefficient, C f The higher it is, the lower the embroidery speed v will be.

6. The embroidery pattern intelligent optimization processing system according to claim 5, characterized in that: The material characteristic M is calculated by the following formula: ； Among them, m i represents the material properties, λ i Represents the material property m i The weight coefficient of .

7. The embroidery pattern intelligent optimization processing system according to claim 4, characterized in that: The embroidery simulation quality prediction module includes an embroidery path simulation unit and a parameter effect calculation unit; The embroidery path simulation unit is used to simulate the optimized embroidery path and estimate the effect after embroidery completion; The parameter effect calculation unit is used to perform quality prediction using the embroidery pattern quality prediction index Cyzl formula and evaluate the final quality of the embroidery pattern; The embroidery pattern quality prediction index Cyzl is calculated by the following formula: ； Where Cyzl represents the embroidery pattern quality prediction index, C f Indicates the complexity of the pattern, L indicates the length of the embroidery path, and T indicates the embroidery time. represents the embroidery density, v represents the embroidery speed, M represents the material properties, w1, w2, w3, w4, w5 and w6 represent the pattern complexity C respectively. f , embroidery path length L, embroidery time T, embroidery density , embroidery speed v and weight coefficient of material property M.

8. The embroidery pattern intelligent optimization processing system according to claim 1, characterized in that: The embroidery simulation quality prediction module further includes a quality evaluation unit; The quality evaluation unit is used to divide the embroidery quality into three grades according to the comparison between the embroidery pattern quality prediction index Cyzl and the preset quality qualification threshold Z and the preset substandard threshold X: If the embroidery pattern quality prediction index Cyzl > Z, the pattern embroidery quality is of high quality grade, indicating that the stitches of the embroidery pattern are fine, the pattern is clear, the color is accurate, and the material is well matched, and no further optimization is required; If the substandard threshold X < Cyzl ≤ Z, the pattern embroidery quality is of qualified grade, indicating that the quality of the embroidery pattern meets the requirements, but there are areas for optimization. Call the path coherence correction unit to re-evaluate and optimize the current embroidery path, reduce unnecessary stitch movements and the length L of the embroidery path, so as to improve the embroidery efficiency and overall aesthetics; If the embroidery pattern quality prediction index Cyzl≤X, the pattern embroidery quality is of substandard grade, indicating that the embroidery pattern quality does not meet the requirements, there are major quality defects or mismatch between the design and the material, and the parameter effect evaluation unit is called to evaluate all influencing factors, including the pattern complexity C f , embroidery path length L, embroidery density Comprehensive analysis of material properties M can be performed to identify the causes of quality problems.

9. The embroidery pattern intelligent optimization processing system according to claim 1, characterized in that: The optimization result output module includes a file format conversion unit and an optimization report generation unit; The file format conversion unit is used to convert the final embroidery pattern, optimization path and parameter configuration into a file format suitable for different embroidery machines to ensure the compatibility and executability of the output results; The optimization report generation unit is used to generate a detailed report of the optimization process, including parameter adjustment, path optimization and quality prediction steps.

10. An embroidery pattern intelligent optimization processing method, applied to an embroidery pattern intelligent optimization processing system according to any one of claims 1 to 9, characterized in that: It includes the following steps: Step 1: Collect embroidery pattern data from multiple sources, and identify and extract the key feature points and line information of the pattern; Step 2: Use image processing and pattern recognition algorithms to perform feature analysis on the embroidery pattern, identify the features of edges, textures and color blocks in the pattern, and divide the pattern into multiple regions according to different embroidery needs to obtain pattern feature points; Step 3: Based on the segmented pattern regions, use an optimization algorithm to calculate the length L of the embroidery path to ensure the minimization of embroidery stitches and the smoothness and coherence of the overall pattern; Step 4: Automatically adjust the parameters of the embroidery machine according to the complexity, density of the pattern and the properties of the selected material, including stitch density, speed and needle type; Step 5: By simulating the optimized embroidery path and parameters, the effect after embroidery is estimated, and the embroidery pattern quality prediction index Cyzl is calculated. According to the calculation results, the threshold value is compared to obtain the quality prediction level evaluation, and optimization suggestions are put forward; Step 6: Generate the final embroidery pattern file, support various file formats of the embroidery machine, output the optimized embroidery path and parameter configuration, ensure the smooth execution of the optimization results in actual operation, and generate a report of the optimization process.

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