A modular design method and system for clothing technology based on adjustable parameters

Abstract

The present invention relates to the technical field of clothing technology, and particularly to a modular design method and system for clothing technology based on adjustable parameters. The method includes the following steps: obtaining a set of basic process parameters; defining process association relationships according to the set of basic process parameters to obtain a process association diagram; constructing a meta-model according to the process association diagram to obtain a process meta-model; obtaining a set of target style parameters; mapping the parameters in the set of target style parameters into the process meta-model to adjust the process parameters and obtain an adjusted set of process parameters; optimizing the process flow according to the adjusted set of process parameters to obtain a parameterized process model; analyzing the process flow of the parameterized process model to obtain a process flow diagram; and dividing the module boundaries of the process flow diagram to obtain module boundaries. By solving the deficiencies of the existing methods in terms of efficiency and flexibility, the present invention realizes a more intelligent, efficient and flexible process design system.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing technology, and particularly to a modular design method and system for clothing technology based on adjustable parameters. Background Art

[0002] The clothing manufacturing industry is undergoing a transformation from mass production to personalized customization. Consumers' demand for diverse and personalized clothing styles is increasing day by day, requiring enterprises to be able to respond quickly to market changes and shorten the product development cycle. Traditional clothing technology design methods mainly rely on manual experience and have fixed processes, making it difficult to meet such rapidly changing demands. To improve efficiency and flexibility, modular design methods for clothing technology have emerged. The core idea is to decompose complex clothing technology processes into several independent and reusable technology modules, and then recombine these modules according to different styles and design requirements to form new technology processes. Some early modular design methods were mainly based on simple splitting and recombination of technology processes, lacking parametric control and intelligent support. Later, parametric modeling and CAD / CAM technologies began to be applied to clothing technology design. Some rule-based expert systems were also used to guide the formulation of technology processes.

[0003] Although modular design methods for clothing technology have made certain progress, there are still the following drawbacks:

[0004] 1. Low efficiency in technology process design; many existing methods still require a large amount of manual intervention, such as the division, sorting, and connection of modules. Especially for complex styles, the design efficiency is still low.

[0005] 2. Insufficient flexibility in process adjustment; the modularity of some methods is not high enough, and the dependency relationships between modules are complex, making it difficult to adjust flexibly according to new requirements. For example, modifying a parameter requires redesigning the entire technology process. Summary of the Invention

[0006] Based on this, it is necessary to provide a modular design method and system for clothing technology based on adjustable parameters to solve at least one of the above technical problems.

[0007] To achieve the above object, a modular design method for clothing technology based on adjustable parameters includes the following steps:

[0008] Step S1: Obtain a basic process parameter set; define process association relationships according to the basic process parameter set to obtain a process association diagram; construct a meta-model according to the process association diagram to obtain a process meta-model;

[0009] Step S2: Obtain the target style parameter set; map the parameters in the target style parameter set into the process meta-model, perform process parameter adjustment to obtain the adjusted process parameter set; optimize the process flow according to the adjusted process parameter set to obtain a parameterized process model;

[0010] Step S3: Analyze the process flow of the parameterized process model to obtain a process flow chart; divide the module boundaries of the process flow chart to obtain a module boundary set; extract module information from the parameterized process model according to the module boundary set and construct a process module library to obtain a process module library;

[0011] Step S4: Obtain a new design requirement parameter set; perform module matching on the process module library according to the new design requirement parameter set to obtain a candidate module set; perform process reconfiguration optimization according to the process meta-model and the candidate module set to obtain a reconfigured process flow;

[0012] Step S5: Perform model process simulation operation on the reconfigured process flow according to the process meta-model to obtain original simulation data; perform process verification on the original simulation data to obtain a simulation result, so as to achieve the modular design task of clothing technology.

[0013] The present invention lays the foundation for the modular design of the entire garment process by constructing a process meta-model that contains all process information, association relationships, and parametric expressions. It not only realizes the structuring and standardization of process knowledge but also supports flexible configuration based on parameters, providing a solid foundation for subsequent process customization and optimization. Automatic customization of process parameters and processes based on style design. By mapping style parameters to the process meta-model and performing parameter adjustment and process optimization, the system can automatically generate a process flow that matches a specific style design, significantly improving the efficiency and accuracy of process design. The modular decomposition of the process flow and the construction of a process module library are realized. By decomposing the process flow into independent and reusable modules and storing them in the module library, the system can support the rapid retrieval, recombination, and reuse of modules, improving the flexibility and efficiency of process design and promoting the accumulation and precipitation of process knowledge. Rapid reconstruction and optimization of the process flow based on the module library. By intelligently matching, sorting, and connecting process modules, the system can quickly generate a new process flow according to new design requirements and optimize it to meet different design and production needs, greatly shortening the process design cycle. The virtual simulation verification and optimization of the process flow are realized. By simulating the actual production process in a virtual environment and analyzing and evaluating the simulation results, the system can verify and optimize the process flow, improving the reliability and efficiency of process design and reducing risks and costs in actual production. Therefore, the present invention provides a modular design method for garment processes based on adjustable parameters. Through innovations in key links such as module matching, parametric modeling, module division, parameter mapping, and process verification, it effectively overcomes the deficiencies of existing modular design methods for garment processes based on adjustable parameters in terms of efficiency and flexibility, and realizes a more intelligent, efficient, and flexible process design system for garment enterprises.

[0014] Preferably, step S1 includes the following steps:

[0015] Step S11: Collect basic process parameters according to common garment processes to obtain a set of basic process parameters;

[0016] Step S12: Define process association relationships based on the set of basic process parameters to obtain a process association diagram;

[0017] Step S13: Abstract process modules from the process association diagram to obtain an abstract process module library;

[0018] Step S14: Perform parametric expression on the abstract process module library to obtain a parametric process module library;

[0019] Step S15: Construct a meta-model for the parametric process module library, the process association diagram, and the set of basic process parameters to obtain a process meta-model.

[0020] The present invention systematically collects the basic parameters of common clothing processes, establishes a comprehensive parameter database, provides a data basis for subsequent definition of process association relationships, construction of process modules, and parametric expression, ensures the integrity and accuracy of process information, and avoids process design problems caused by missing or incorrect information. By defining the association relationships between processes and presenting them in the form of a graph, the execution sequence, dependency relationships, and conditional parameters between different processes are clearly expressed, providing a logical framework for subsequent process module abstraction and process optimization, making the process flow clearer, easier to understand and manage, and supporting more complex process logics, such as conditional branching and parallel execution. Abstracting the process nodes in the process association graph into process modules realizes the modularization of the process flow, decomposes the complex process flow into independent and reusable units, provides a basis for subsequent module recombination and process customization, improves the flexibility and efficiency of process design, and facilitates the management and reuse of modules. By expressing the process modules in a parametric form, flexible configuration and adjustment of process parameters are realized, enabling the process modules to adapt to different styles, fabrics, and design requirements, enhancing the adaptability and customizability of process design, and providing a basis for subsequent parameter optimization and automated adjustment. Combining the parametric process module library, process association graph, and basic process parameters into a unified process meta-model constructs a complete and parametrically configurable process knowledge base, provides a unified data platform for subsequent process flow generation, module recombination, and simulation verification, realizes the centralized management and efficient utilization of process knowledge, and supports parametric-based process flow customization and optimization.

[0021] Preferably, step S12 includes the following steps:

[0022] Step S121: Construct a knowledge graph based on the basic process parameter set to obtain a clothing process knowledge graph;

[0023] Step S122: Create process nodes for the basic process parameter set to obtain a process node set;

[0024] Step S123: Perform relationship reasoning on the process node set according to the clothing process knowledge graph to obtain an inference relationship set;

[0025] Step S124: Construct an association relationship for the process node set according to the inference relationship set to obtain an initial process association graph;

[0026] Step S125: Conduct manual review and correction on the initial process association graph to obtain the process association graph.

[0027] The present invention constructs a clothing process knowledge graph, which associates process parameters, material properties, and process knowledge in a structured form, providing a rich knowledge basis for subsequent relationship reasoning and process flow optimization. This enables the system to more intelligently understand the mutual influence and constraint relationships between process parameters and supports more complex process logic reasoning. Creating each process as an independent node lays the foundation for subsequent construction of a process association graph and relationship reasoning, enabling the system to more clearly express and process the relationships between different processes and supporting more refined process flow control and management. Using the knowledge graph for relationship reasoning can discover implicit association relationships between process nodes. For example, inferring the required process steps based on fabric properties expands the scope of process associations, enabling the system to automatically infer complex relationships between processes, not limited to predefined rules, thereby improving the automation and intelligence levels of the process flow. Constructing an initial process association graph based on the inference relationship set visualizes the inferred process relationships, facilitating subsequent manual review and correction. This enables experts to more intuitively understand and verify the results of the system's automatic reasoning and make necessary corrections and supplements, thereby ensuring the accuracy and integrity of the process association graph. Through expert review and correction of the initial process association graph, expert experience and domain knowledge are combined, making up for the deficiencies of automated reasoning and ensuring the accuracy and practicality of the process association graph, providing a reliable basis for subsequent modular decomposition and recombination.

[0028] Preferably, step S2 includes the following steps:

[0029] Step S21: Obtain design parameters for the target style to obtain a target style parameter set;

[0030] Step S22: Map the parameters in the target style parameter set into the process meta-model to obtain a mapped parameter set;

[0031] Step S23: Adjust the process parameters of the process meta-model according to the mapped parameter set to obtain an adjusted process parameter set;

[0032] Step S24: Select process modules for the process meta-model according to the adjusted process parameter set and generate a process flow to obtain an initial process flow;

[0033] Step S25: Optimize the process flow of the initial process flow to obtain a parameterized process model.

[0034] The present invention obtains the design parameters of the target style through multiple channels, including obtaining size data by three-dimensional human body scanning, selecting style parameters from the style template library, and querying fabric characteristics from the material database, ensuring the comprehensiveness and accuracy of the target style information and providing a reliable data basis for subsequent parameter mapping and process parameter adjustment. Mapping the target style parameters to the relevant parameters in the process meta-model establishes a bridge between style design and process parameters, realizes the customization of the process flow driven by parameters, enables the system to automatically adjust process parameters according to different style designs, and improves the efficiency and automation degree of process design. Adjusting the parameters in the process meta-model according to the mapped parameter set realizes the customization of process parameters based on style design, makes the process parameters accurately match the style design, and thus ensures that the quality and style of the final product meet the design requirements. Selecting appropriate process modules from the process meta-model according to the adjusted process parameter set and generating the initial process flow realizes the automatic generation of the process flow, greatly improves the efficiency of process design, and ensures that the generated process flow matches the target style parameters. By optimizing the initial process flow, such as parallelizing parallel steps and removing redundant steps, the production efficiency is further improved, the production cost is reduced, and the process flow becomes more concise and efficient, and finally a parameterized process model optimized for a specific style is obtained.

[0035] Preferably, step S3 includes the following steps:

[0036] Step S31: Analyze the process flow of the parameterized process model to obtain the process flow chart;

[0037] Step S32: Identify the key nodes of the process flow chart to obtain the key node set;

[0038] Step S33: Divide the module boundaries of the process flow chart according to the key node set to obtain the module boundary set;

[0039] Step S34: Extract the module information of the parameterized process model according to the module boundary set to obtain the module information set;

[0040] Step S35: Construct a process module library based on the module information set to obtain the process module library.

[0041] By converting the parametric process model into a visual process flow chart, the present invention makes the process flow more intuitive, easier to understand and analyze, provides a visual basis for subsequent key node identification and module boundary division, and facilitates manual intervention and adjustment. By identifying the key nodes in the process flow chart, such as branch, merge, and loop nodes, it provides a basis for module boundary division, ensures the rationality and effectiveness of module division, and makes the generated modules more independent and reusable. According to the key node set, the module boundaries are divided, and the process flow is decomposed into a series of independent modules, realizing the modularization of the process flow, laying a foundation for subsequent module recombination and reuse, and improving the flexibility and efficiency of process design. The detailed information of each module is extracted, including process steps, input and output parameters, execution time, resource requirements, as well as the dependency relationships and constraint conditions between modules, providing complete data for constructing a process module library and ensuring the integrity and accuracy of module information. The extracted module information is constructed into a structured process module library, realizing the storage, management, and retrieval of process modules, providing rich module resources for subsequent module matching and recombination, facilitating the reuse and sharing of process modules, and supporting the accumulation and precipitation of process knowledge.

[0042] Preferably, step S33 includes the following steps:

[0043] Step S331: Perform an initial module segmentation on the process flow chart according to the key node set to obtain an initial module segment set;

[0044] Step S332: Evaluate the module complexity of the initial module segment set to obtain a module complexity index set;

[0045] Step S333: Use a preset complexity threshold to identify high-complexity module segments from the module complexity index set to obtain a high-complexity module segment set;

[0046] Step S334: Identify split points for the high-complexity module segment set to obtain split point data;

[0047] Step S335: Redraw the boundaries of the high-complexity module segment set according to the split point data to obtain boundary redrawing data;

[0048] Step S336: Adjust the module boundaries of the high-complexity module segment set according to the boundary redrawing data to obtain an adjusted module segment set;

[0049] Step S337: Determine the module boundaries according to the adjusted module segment set and the initial module segment set to obtain a module boundary set.

[0050] The present invention preliminarily divides the process flow chart through key nodes, forming an initial module division scheme, which provides a basis for subsequent module adjustment and optimization based on complexity, and avoids the subjectivity and inefficiency of completely manual module division. The complexity of each initial module segment is evaluated, and the complexity of the module is quantified from multiple dimensions such as the number of steps, the number of parameters, the number of parameter types, and the density of dependency relationships, providing an objective basis for subsequent identification of high-complexity modules and avoiding the limitations of judgment based solely on experience. Through a preset complexity threshold, modules with excessively high complexity are quickly identified, providing a clear goal for subsequent module splitting and optimization, avoiding unnecessary splitting of all modules, and improving the efficiency of module division. The splitting points of high-complexity modules are identified, and suitable splitting positions within the modules are found, providing guidance for subsequent boundary re-drawing, ensuring that the complexity of the split modules is reduced and the logical coherence within the modules is maintained. According to the splitting point data, the boundaries of high-complexity modules are re-drawn, generating a new module division scheme, reducing the complexity of the modules, and improving the reusability and maintainability of the modules. According to the boundary re-drawing data, the module boundaries are adjusted, and the newly split modules are integrated into the module set, forming a more optimal module division scheme, improving the granularity and reusability of the modules. The adjusted module segments and the initial module segments that have not been split are integrated, and finally the module boundaries are determined, forming a complete and optimized module division scheme, providing accurate module boundary information for subsequent module information extraction and process module library construction.

[0051] Preferably, step S4 includes the following steps:

[0052] Step S41: Obtain a new design requirement parameter set; perform module matching on the process module library according to the new design requirement parameter set to obtain a candidate module set;

[0053] Step S42: Sort the candidate module set according to the process meta-model to obtain a module sequence;

[0054] Step S43: Connect the modules in the module sequence according to the process meta-model to obtain a module network;

[0055] Step S44: Verify the process of the module network and perform process reorganization and optimization to obtain a reorganized process flow.

[0056] The present invention screens candidate modules that meet the new design requirements from a process module library by obtaining a new design requirement parameter set and performing module matching, providing a basis for subsequent module sorting and connection, realizing rapid reconstruction of the process flow based on the module library, and reducing the workload of designing the process flow from scratch. Sorting the candidate modules according to the module dependency relationships and constraint conditions defined in the process meta-model ensures that the order of module connection conforms to the process logic, avoids unreasonable process flows caused by incorrect module connections, and guarantees the correctness and feasibility of the generated process flow. Connecting the sorted modules according to the connection methods defined in the process meta-model to form a module network constructs a complete process flow structure, supports various connection methods such as parallel, branch, and loop, realizes flexible customization of the process flow, and provides a basis for subsequent process verification and optimization. By performing process verification and optimization on the module network, such as checking the correctness of module connections, eliminating circular dependencies, and optimizing process parameters, the quality and efficiency of the reorganized process flow are further improved, ensuring that the generated process flow meets the design requirements and process constraints and achieving optimal performance.

[0057] Preferably, step S41 includes the following steps:

[0058] Step S411: Perform text parsing and keyword extraction on the new design requirement parameter set to obtain requirement key parameters;

[0059] Step S412: Perform semantic role annotation on the requirement key parameters to obtain semantic role annotation data;

[0060] Step S413: Query the pre-constructed ontology knowledge base of the clothing process field according to the requirement key parameters and the semantic role annotation data to obtain ontology knowledge data;

[0061] Step S414: Structure the requirement intention of the ontology knowledge data to obtain a structured requirement intention;

[0062] Step S415: Construct module feature vectors according to the process module library to obtain a set of module feature vectors;

[0063] Step S416: Calculate the semantic similarity between the requirement and the module according to the structured requirement intention and the set of module feature vectors to obtain a list of module semantic similarities;

[0064] Step S417: Perform module scoring based on machine learning according to the structured requirement intention and the set of module feature vectors to obtain a list of module matching scores;

[0065] Step S418: Screen and sort the candidate modules from the list of module semantic similarities and the list of module matching scores to obtain a set of candidate modules.

[0066] Through text parsing and keyword extraction of the new design requirement parameter set, the present invention converts unstructured text information into structured key parameters, providing a basis for subsequent semantic analysis and module matching, enabling the system to understand the core content of the design requirements. By performing semantic role annotation on the key requirement parameters, the semantic roles of the keywords in the requirement description are identified, such as actions, locations, objects, etc., and the semantic information of the design requirements is more deeply understood, providing more accurate semantic information for subsequent ontology knowledge base query and requirement intention structuring. By querying the ontology knowledge base in the field of clothing technology, the key requirement parameters are associated with domain knowledge to obtain richer semantic information, such as hierarchical relationships and attributes of concepts, expanding the semantic scope of the requirement information and providing more comprehensive knowledge support for more accurate module matching. Combining the ontology knowledge data with the semantic role annotation data to structure the requirement intention, forming a clear and definite requirement expression, providing structured data input for subsequent module matching, and improving the accuracy and efficiency of module matching. By constructing a module feature vector, information such as the text description and input / output parameters of the module is converted into a numerical vector representation, providing a data basis for subsequent semantic similarity calculation and machine learning scoring, enabling the system to quantitatively compare and analyze the modules. By calculating the semantic similarity between the requirement and the module, the correlation between the requirement and the module is quantified, providing an important basis for module screening, enabling the system to preferentially select modules that are closer to the requirement semantics. Using a machine learning model to score the modules can learn the complex associations between requirements and modules in historical data, thereby more accurately predicting the matching degree between the module and the current requirement, and improving the intelligent level and accuracy of module matching. By comprehensively considering multiple aspects of information such as semantic similarity, machine learning scoring, and parameter constraints, the candidate modules are screened and sorted, ensuring that the finally selected modules not only meet the semantic requirements but also conform to the new requirements in terms of parameters, improving the accuracy and reliability of module matching.

[0067] Preferably, step S5 includes the following steps:

[0068] Step S51: Build a simulation environment according to the process meta-model to obtain a virtual simulation environment;

[0069] Step S52: Import the reorganized process flow into the virtual simulation environment and perform relevant process associations to obtain the imported process flow;

[0070] Step S53: Perform simulation operation on the imported process flow to obtain the original simulation data;

[0071] Step S54: Analyze and evaluate the results of the original simulation data to obtain a simulation evaluation report;

[0072] Step S55: Optimize and adjust the parameters of the reorganized process flow according to the simulation evaluation report to obtain the simulation results.

[0073] In the present invention, a virtual simulation environment is built based on the process meta-model, and the real clothing production process and equipment are simulated in the virtual environment, providing a realistic simulation platform for subsequent process flow verification and optimization, and avoiding the costs and risks of testing in actual production. Importing the reorganized process flow into the virtual simulation environment and performing relevant process associations enables the virtual environment to execute the simulation according to the reorganized process flow, transforming the abstract process flow into specific simulation operations, providing a basis for subsequent simulation operation and data collection. By running the imported process flow in the virtual simulation environment, the actual clothing production process is simulated, and various key data such as time, material consumption, and quality parameters are collected, providing data support for subsequent analysis and evaluation of the simulation results. By analyzing and evaluating the original simulation data, such as calculating the total production time, total material consumption, and various product quality indicators, and comparing them with the preset standards, the performance of the reorganized process flow can be comprehensively evaluated, providing a direction and basis for subsequent optimization of the process flow. According to the simulation evaluation report, the parameters of the reorganized process flow are optimized and adjusted, and the optimization effect is verified through repeated simulations. Finally, a verified and optimized process flow and the best combination of process parameters are obtained, thereby maximizing production efficiency, reducing costs, and ensuring product quality.

[0074] Preferably, the present invention also provides a clothing process modular design system with adjustable parameters for implementing the clothing process modular design method with adjustable parameters as described above. The clothing process modular design system with adjustable parameters includes:

[0075] A meta-model construction unit for obtaining a set of basic process parameters; defining process association relationships according to the set of basic process parameters to obtain a process association diagram; constructing a meta-model according to the process association diagram to obtain a process meta-model;

[0076] A parameterized design unit for obtaining a set of target style parameters; mapping the parameters in the set of target style parameters into the process meta-model to adjust the process parameters and obtain an adjusted set of process parameters; optimizing the process flow according to the adjusted set of process parameters to obtain a parameterized process model;

[0077] A modular decomposition unit for analyzing the process flow of the parameterized process model to obtain a process flow diagram; dividing the module boundaries of the process flow diagram to obtain a set of module boundaries; extracting module information from the parameterized process model according to the set of module boundaries and constructing a process module library to obtain a process module library;

[0078] A module recombination unit, configured to obtain a new set of design requirement parameters; perform module matching on a process module library according to the new set of design requirement parameters to obtain a candidate module set; perform process recombination optimization according to a process meta-model and the candidate module set to obtain a recombined process flow.

[0079] A process verification unit, configured to perform model process simulation operation on the recombined process flow according to the process meta-model to obtain original simulation data; perform process verification on the original simulation data to obtain a simulation result, so as to implement the modular design task of clothing technology. Description of the Drawings

[0080] Figure 1 It is a schematic diagram of the step flow of a clothing process modular design method based on adjustable parameters.

[0081] Figure 2 It is a detailed implementation step flow diagram of step S3 in the present invention.

[0082] Figure 3 It is a detailed implementation step flow diagram of step S4 in the present invention.

[0083] The realization of the purpose, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0084] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0085] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0087] To achieve the above object, please refer to Figures 1 to 3 , a modular design method for clothing technology based on adjustable parameters, comprising the following steps:

[0088] Step S1: Obtain a set of basic process parameters; define process association relationships according to the set of basic process parameters to obtain a process association diagram; construct a meta-model according to the process association diagram to obtain a process meta-model;

[0089] Step S2: Obtain a set of target style parameters; map the parameters in the set of target style parameters into the process meta-model, adjust the process parameters to obtain an adjusted set of process parameters; optimize the process flow according to the adjusted set of process parameters to obtain a parameterized process model;

[0090] Step S3: Analyze the process flow of the parameterized process model to obtain a process flow diagram; divide the module boundaries of the process flow diagram to obtain a set of module boundaries; extract module information from the parameterized process model according to the set of module boundaries and construct a process module library to obtain a process module library;

[0091] Step S4: Obtain a set of new design requirement parameters; perform module matching on the process module library according to the set of new design requirement parameters to obtain a set of candidate modules; perform process reconfiguration and optimization according to the process meta-model and the set of candidate modules to obtain a reconfigured process flow;

[0092] Step S5: Perform model process simulation operation on the reconfigured process flow according to the process meta-model to obtain original simulation data; perform process verification on the original simulation data to obtain a simulation result, so as to achieve the clothing process modular design task.

[0093] In the embodiments of the present invention, referring to Figure 1 as shown, it is a schematic diagram of the step flow of the modular design method for clothing technology based on adjustable parameters of the present invention. In this example, the modular design method for clothing technology based on adjustable parameters includes the following steps:

[0094] Step S1: Obtain a set of basic process parameters; define process association relationships according to the set of basic process parameters to obtain a process association diagram; construct a meta-model according to the process association diagram to obtain a process meta-model;

[0095] In the embodiment of the present invention, first, the basic parameters of common clothing processes are collected, such as the thread type, stitch density, speed of sewing, cutting method, accuracy, ironing temperature, pressure, etc., to form a basic process parameter set. Then, using the graph database Neo4j, according to the logical relationship and conditional parameters between processes, a process association graph is constructed, where nodes represent processes and edges represent the sequential, parallel or conditional relationships between processes. Finally, the process association graph, the basic process parameter set, and the abstracted process modules and their parametric expressions are integrated into Neo4j to form a final process meta-model, which contains the standardized descriptions of all process modules and the possible connection and combination methods between them.

[0096] Step S2: Obtain the target style parameter set; map the parameters in the target style parameter set to the process meta-model to adjust the process parameters and obtain an adjusted process parameter set; optimize the process flow according to the adjusted process parameter set to obtain a parametric process model;

[0097] In the embodiment of the present invention, the size, style and fabric parameters of the target style are obtained through three-dimensional human body scanning, a style template library and a material database to form a target style parameter set. Then, these parameters are mapped to the corresponding process parameters in the process meta-model. For example, the chest circumference is mapped to the body width, and the parameter values are adjusted according to the mapping relationship. Finally, according to the adjusted parameter set, appropriate process modules are selected from the process meta-model, and optimization algorithms such as genetic algorithms are used to optimize the generated initial process flow, such as parallelizing parallel steps, removing redundant steps, etc., and finally a parametric process model for a specific style is obtained.

[0098] Step S3: Analyze the process flow of the parametric process model to obtain a process flow chart; divide the module boundaries of the process flow chart to obtain a module boundary set; extract module information from the parametric process model according to the module boundary set and construct a process module library to obtain a process module library;

[0099] In the embodiment of the present invention, the Graphviz tool is used to visualize the process flow of the parametric process model as a process flow chart, where nodes represent process steps and edges represent the dependency relationships between steps. Then, the key nodes (branch, merge, loop nodes) in the flow chart are identified through the depth-first search algorithm, and the module boundaries are divided with the key nodes as the boundaries to form a module boundary set. Finally, module information including process steps, parameters, dependency relationships, etc. is extracted from the parametric process model according to the module boundary set, and this information is stored in the MongoDB database to construct a process module library.

[0100] Step S4: Obtain a new set of design requirement parameters; perform module matching on the process module library according to the new set of design requirement parameters to obtain a candidate module set; perform process reorganization and optimization according to the process meta-model and the candidate module set to obtain a reorganized process flow.

[0101] In the embodiment of the present invention, a new set of design requirement parameters input by the user is obtained, such as size modification, style change, etc. Then, using NLP techniques (word segmentation, semantic role labeling), an ontology knowledge base, and a machine learning model, the requirements are analyzed and understood, and the most suitable candidate module set is matched from the process module library. Finally, according to the module dependency relationships and constraints defined in the process meta-model, the candidate modules are sorted using a topological sorting algorithm and connected to form a module network. Then, the module network is verified and optimized, such as removing redundant modules, adjusting the module order, etc., and finally a reorganized process flow that meets the new design requirements is obtained.

[0102] Step S5: Perform model process simulation and operation on the reorganized process flow according to the process meta-model to obtain original simulation data; perform process verification on the original simulation data to obtain simulation results, so as to implement the modular design task of clothing processes.

[0103] In the embodiment of the present invention, based on the parameters in the process meta-model, a virtual simulation environment is built in the Clo3D software, including a virtual human body model, clothing styles, fabrics, and equipment. Then, the reorganized process flow is converted into an instruction sequence recognizable by Clo3D and run in the simulation environment to collect original simulation data, such as time, material consumption, quality parameters, etc. Next, the simulation data is analyzed to evaluate the efficiency, cost, and quality of the process flow and generate a simulation evaluation report. Finally, according to the evaluation report, optimization algorithms such as genetic algorithms are used to adjust and optimize the process flow parameters, such as modifying the sewing speed, adjusting the process step order, etc., and finally an optimized simulation result is obtained, including the final process parameters and performance indicators.

[0104] Preferably, step S1 includes the following steps:

[0105] Step S11: Collect basic process parameters according to common clothing processes to obtain a set of basic process parameters;

[0106] Step S12: Define process association relationships according to the set of basic process parameters to obtain a process association diagram;

[0107] Step S13: Perform process module abstraction on the process association diagram to obtain an abstract process module library;

[0108] Step S14: Perform parameterized expression on the abstract process module library to obtain a parameterized process module library;

[0109] Step S15: Construct a meta-model for the parametric process module library, process association graph, and basic process parameter set to obtain a process meta-model.

[0110] In the embodiment of the present invention, first, a database containing all common clothing processes is established, such as sewing, cutting, ironing, bonding, embroidery, etc. Then, for each process, its related basic parameters are recorded in detail. Taking the sewing process as an example, the recorded parameters include thread type (e.g., polyester thread, cotton thread), stitch density (e.g., 2mm, 3mm), sewing speed (e.g., 800 stitches per minute, 1000 stitches per minute), sewing stitch type (e.g., straight stitch, overlock), sewing force magnitude (e.g., 0.5N, 1N), etc. For the cutting process, the recorded parameters include cutting method (e.g., manual cutting, laser cutting), cutting accuracy (e.g., ±1mm, ±0.5mm), cut piece size (e.g., length, width), etc. Similarly, parameters such as temperature, pressure, and time of the ironing process, adhesive type, glue application amount, curing time of the bonding process, and embroidery thread type, stitch density, embroidery pattern of the embroidery process are recorded. All the collected parameter information is stored in the database to form a structured basic process parameter set, and a value range and measurement unit are set for each parameter.

[0111] Use the graph database Neo4j to create a process association graph. Each process collected in step S11 is created as a node in the graph database, and the node attributes include the process name and description. Then, according to the logical relationship between processes, directed edges are created between the nodes to represent the execution order and dependency relationship of the processes. For example, the "Cut garment pieces" node points to the "Sew garment pieces" node, indicating that sewing can only be performed after cutting. The edge attributes include relationship type (e.g., sequential relationship, parallel relationship, conditional relationship) and conditional parameters. For example, if the fabric thickness is greater than 3mm, then pre-shrinking treatment is required, so a conditional relationship edge is created between the "Fabric detection" node and the "Pre-shrinking treatment" node, and the conditional parameter is "Fabric thickness > 3mm". For a parallel relationship, such as "Sew sleeves" and "Sew body" can be performed simultaneously, then a bidirectional edge is created between these two nodes. The finally generated process association graph clearly shows the dependency relationship and execution order between different processes, as well as the conditional parameters that trigger different process paths.

[0112] Analyze the process association diagram generated in step S12, and aggregate process nodes with similar functions or operation steps into abstract process modules. For example, aggregate nodes such as "cut the front piece", "cut the back piece", and "cut the sleeve piece" into a "cutting module". Each module represents an independent process unit and defines its inputs and outputs. For example, the input of the "cutting module" is "fabric", and the output is "cut garment pieces". The input of the "sew the sleeve" module is "cut sleeve pieces" and "cut body", and the output is "sewn sleeve". Store all the abstracted process modules and their input / output information in a MongoDB database to form an abstract process module library.

[0113] From the basic process parameter set obtained in step S11, extract the parameters related to each abstract process module and associate them with the module. For example, associate parameters such as "thread type", "stitch density", and "sewing speed" with the "sewing module". In addition, define the parameters that control the combination relationship between modules, such as the module connection method (e.g., flat seam, overlock seam), module order, conditional parameters, etc. For example, the parameters of the "sew the sleeve" module, in addition to the sewing parameters, also include the connection method parameter (e.g., flat seam, overlock seam), and the connection order parameter with the "sew the body" module. Add this parameter information to the MongoDB database to form a parameterized process module library, where each module contains the required process parameters and the parameters for controlling the combination of modules.

[0114] Import the parameterized process module library generated in step S14 into a Neo4j graph database, and create each module as a node. The node attributes include the module name, description, input / output interfaces, and related process parameters. Then, according to the process association diagram generated in step S12, establish connections between the module nodes to represent the dependency relationships and execution orders between the modules. The edge attributes include the relationship type and conditional parameters. At the same time, import the basic process parameter set in step S11 into Neo4j and associate it with the corresponding module nodes. Finally, the information in the Neo4j database constitutes a complete process meta-model, which includes the standardized descriptions of all process modules, the possible connection and combination methods between them, and the related process parameters, all of which are expressed in a parameterized form.

[0115] Preferably, step S12 includes the following steps:

[0116] Step S121: Construct a knowledge graph based on the basic process parameter set to obtain a clothing process knowledge graph;

[0117] Step S122: Create process nodes for the basic process parameter set to obtain a process node set;

[0118] Step S123: Perform relationship reasoning on the process node set according to the clothing process knowledge graph to obtain an inference relationship set;

[0119] Step S124: Construct an association relationship for the process node set according to the inference relationship set to obtain an initial process association graph;

[0120] Step S125: Conduct manual review and correction on the initial process association graph to obtain the process association graph.

[0121] In the embodiment of the present invention, the clothing process knowledge graph is constructed by using the graph database Neo4j. Each process parameter in the basic process parameter set obtained in step S11 is used as a node in the knowledge graph, such as "thread type", "stitch density", "fabric thickness", etc. At the same time, related processes and materials are also created as nodes, such as "sewing", "cutting", "cotton cloth", "wool", etc. Then, according to process specifications, material characteristics, and expert experience, relationships are established between the nodes. For example, create relationships (cotton cloth, shrinkage rate, high), (wool, elasticity, medium), (sewing, thread type, polyester thread), (stitch density, affects, sewing strength), etc. The relationship types include "material characteristics", "process requirements", "parameter influence", etc. In this way, a clothing process knowledge graph containing the relationships between fabrics, processes, and parameters is constructed and stored in the Neo4j database.

[0122] From the basic process parameter set obtained in step S11, extract each process, such as sewing, cutting, ironing, etc., and create corresponding process nodes in the Neo4j graph database. The attributes of each node include the name, description, and related parameter information of the process. For example, create a node named "sewing", whose description is "the process of connecting two or more pieces of fabric with sewing thread", and the related parameters include "thread type", "stitch density", "sewing speed", etc., and these parameters are connected to the corresponding parameter nodes through relationships. All the created process nodes form a process node set.

[0123] Using the clothing process knowledge graph constructed in step S121 and the graph algorithms provided by the APOC library, perform relationship reasoning on the process node set created in step S122. For example, it is known that there are relationships (wool, shrinkage rate, high) and (high shrinkage rate, pre-shrinking treatment, necessary) in the knowledge graph, as well as the process nodes "cutting" and "pre-shrinking treatment". Through the path finding algorithm `apoc.path.expandConfig` in the APOC library, it can be inferred that if the fabric is wool, then the "pre-shrinking treatment" node needs to be executed after the "cutting" node, and this inference relationship (cutting, conditional relationship; fabric = wool, pre-shrinking treatment) is added to the inference relationship set. The inference relationship set stores all the relationships between process nodes obtained through knowledge graph reasoning.

[0124] In the Neo4j graph database, based on the set of inference relationships obtained in step S123, connections are established between the process nodes created in step S122 to form an initial process association graph. Different types of relationships are represented using different types of relationships. For example, solid arrows are used to represent sequential relationships, dashed arrows are used to represent conditional relationships, and bidirectional arrows are used to represent parallel relationships. For conditional relationships, the parameters that trigger the condition and the corresponding parameter values are stored as attributes of the relationship. For example, for the relationship (cutting, conditional relationship; fabric = wool, pre-shrinking treatment), "fabric = wool" is stored as an attribute of the relationship.

[0125] Domain experts review the initial process association graph generated in step S124. For example, they check whether the connections between process nodes are correct, whether the conditional parameters are reasonable, and whether there are any missing or redundant relationships. Experts can directly modify the process association graph through the interface of the Neo4j graph database, such as adding, deleting, or modifying nodes and edges, and adjusting conditional parameters. The reviewed and corrected process association graph serves as the final process association graph, providing a basis for subsequent modular decomposition and recombination.

[0126] Preferably, step S2 includes the following steps:

[0127] Step S21: Obtain design parameters for the target style to obtain a target style parameter set;

[0128] Step S22: Map the parameters in the target style parameter set into the process meta-model to obtain a mapped parameter set;

[0129] Step S23: Adjust the process parameters of the process meta-model according to the mapped parameter set to obtain an adjusted process parameter set;

[0130] Step S24: Select process modules for the process meta-model according to the adjusted process parameter set and generate a process flow to obtain an initial process flow;

[0131] Step S25: Optimize the process flow of the initial process flow to obtain a parameterized process model.

[0132] In the embodiments of the present invention, dimensional parameters of the target style are obtained through a three-dimensional body scanner, such as chest circumference, waist circumference, hip circumference, garment length, sleeve length, etc. At the same time, the style of the target style is selected through a predefined style template library, such as fitted, loose, A-line, etc. In addition, characteristic parameters of the fabric used for the target style are queried through a material database, such as thickness, elasticity, shrinkage rate, etc. The obtained dimensional parameters, style parameters, and material parameters are stored in a JSON format file to form a target style parameter set.

[0133] Read the target style parameter set in JSON format generated in step S21, and map the parameters to the process meta-model generated in step S15. For example, map the "chest circumference" parameter in the target style parameter set to the "body width" parameter of the "cutting module" in the process meta-model, and map the "body length" parameter to the "body length" parameter of the "cutting module". For style parameters, such as the "slim fit" style, map it to the "stitch density" parameter of the "sewing module" and set it to a smaller stitch density value. Store all mapping relationships and corresponding parameter values in another JSON file to form a mapped parameter set. The mapping relationships are based on a predefined rule library and a machine learning model.

[0134] Read the mapped parameter set generated in step S22, and adjust the parameters in the process meta-model generated in step S15 according to the mapping relationships and parameter values therein. For example, calculate the value of the "body width" parameter of the "cutting module" according to the rule of "body width = chest circumference / 2 + 5 cm" in the mapped parameter set. Set the value of the "stitch density" parameter of the "sewing module" to 2 mm according to the rule of "stitch density = 2 mm" corresponding to the "slim fit" style. Store all adjusted process parameter values in a new JSON file to form an adjusted process parameter set.

[0135] Read the adjusted process parameter set generated in step S23, and select appropriate process modules from the process meta-model generated in step S15 according to the parameter values therein. For example, select the "cutting module" according to the values of the "body width" and "body length" parameters; select the "sewing module" according to the parameter values such as "thread type", "stitch density", and "sewing speed". Then, connect the selected modules according to the dependency relationships and execution order defined between the modules in the process meta-model to form an initial process flow. Store the generated initial process flow in the Neo4j database in the form of a directed acyclic graph, where the nodes in the graph represent process modules and the edges represent the execution order between the modules.

[0136] Optimize the initial process flow generated in step S24. For example, if there are two or more modules that can be executed in parallel, adjust the process to execute these modules in parallel to shorten the production time. If there are redundant process steps, delete them to improve production efficiency. The optimization process is based on predefined optimization rules and genetic algorithms. The optimized process flow together with the adjusted process parameter set generated in step S23 constitutes a parameterized process model, and store the parameterized process model in the Neo4j database, which contains the process parameters for a specific style and the optimized process flow.

[0137] Preferably, step S3 includes the following steps:

[0138] Step S31: Analyze the parametric process model for the process flow to obtain a process flow chart.

[0139] Step S32: Identify key nodes in the process flow chart to obtain a key node set.

[0140] Step S33: Divide the module boundaries of the process flow chart according to the key node set to obtain a module boundary set.

[0141] Step S34: Extract module information from the parametric process model according to the module boundary set to obtain a module information set.

[0142] Step S35: Construct a process module library from the module information set to obtain a process module library.

[0143] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0144] Step S31: Analyze the parametric process model for the process flow to obtain a process flow chart.

[0145] In the embodiment of the present invention, the parametric process model generated in step S25 is read from the Neo4j database, including the process flow and process parameters. The Graphviz tool is used to convert the process flow in the parametric process model into a visual process flow chart. Each node in the figure represents a process step (for example: cutting the front piece, sewing the shoulder, ironing the collar), and the node attributes include information such as the step name, execution time, required resources, etc. The directed edges between the nodes represent the sequence and dependency relationships between the steps, and the dependency relationship types are marked on the edges (for example: sequential, parallel, conditional).

[0146] Step S32: Identify key nodes in the process flow chart to obtain a key node set.

[0147] In the embodiment of the present invention, the process flow chart generated in step S31 is analyzed, and the depth-first search algorithm is used to traverse the flow chart to identify key nodes. Key nodes include branch nodes (nodes with multiple subsequent steps), merge nodes (nodes with multiple predecessor steps), and loop nodes (nodes that form a loop). The identified key nodes are stored in a list to form a key node set, and the list elements include the node ID and node type. For example, if the step of "sewing the cuffs" can be followed by "sewing the side seams" or "sewing the collar", then "sewing the cuffs" is identified as a branch node and added to the key node set.

[0148] Step S33: Divide the module boundaries of the process flow chart according to the key node set to obtain a module boundary set.

[0149] In the embodiments of the present invention, according to the key node set in step S32, the process flow chart is divided into several modules. The module boundaries are defined by the key nodes. The process step sequence between two adjacent key nodes is taken as a module, and the step sequences between the starting node and the first key node, and between the last key node and the ending node are also taken as a module respectively. Record the starting node and ending node IDs of each module, and store this information in a list to form a module boundary set. For example, if "cutting garment pieces" is the starting node and "sewing shoulders" is the key node, then all the steps between "cutting garment pieces" and "sewing shoulders" form a module, and the node IDs of "cutting garment pieces" and "sewing shoulders" are recorded in the module boundary set.

[0150] Step S34: Extract module information from the parametric process model according to the module boundary set to obtain a module information set;

[0151] In the embodiments of the present invention, according to the module boundary set generated in step S33, extract the information of each module from the parametric process model generated in step S25. For example, extract the process steps included in the module, the input and output parameters of each step, the execution time, resource requirements, etc. In addition, extract the dependency relationships and constraint conditions between modules. For example, module A must be executed before module B, and module C needs to meet specific conditions to be executed, etc. Store the information of each module as a JSON object, including module ID, module name, process step list, input and output parameter list, dependency relationship list, etc. Store the JSON objects of all modules in a list to form a module information set.

[0152] Step S35: Construct a process module library from the module information set to obtain a process module library;

[0153] In the embodiments of the present invention, import the module information set generated in step S34 into the MongoDB database to construct a process module library. Each module is stored as a document in the database, and the document fields include module ID, module name, process step list, input and output parameter list, dependency relationship list, etc., which are consistent with the JSON object structure in step S34. Through the indexing function of the MongoDB database, module information can be quickly retrieved according to the module ID, name, etc.

[0154] Preferably, step S33 includes the following steps:

[0155] Step S331: Perform an initial module segmentation on the process flow chart according to the key node set to obtain an initial module segment set;

[0156] Step S332: Evaluate the module complexity of the initial module segment set to obtain a module complexity index set;

[0157] Step S333: Identify high-complexity module segments from the module complexity metric set using a preset complexity threshold, obtaining a high-complexity module segment set;

[0158] Step S334: Identify split points from the high-complexity module segment set, obtaining split point data;

[0159] Step S335: Redraw the boundaries of the high-complexity module segment set based on the split point data, obtaining boundary redrawing data;

[0160] Step S336: Adjust the module boundaries of the high-complexity module segment set according to the boundary redrawing data, obtaining an adjusted module segment set;

[0161] Step S337: Determine the module boundaries based on the adjusted module segment set and the initial module segment set, obtaining a module boundary set.

[0162] In the embodiment of the present invention, using the key node set obtained in step S32, the process flow chart generated in step S31 is initially segmented. The step sequence between the start node and the first key node, the step sequence between adjacent key nodes, and the step sequence between the last key node and the end node are respectively defined as an initial module segment. A unique ID is assigned to each initial module segment, and the process step sequence, start key node ID, and end key node ID it contains are recorded. The information of all initial module segments is stored in a list to form an initial module segment set.

[0163] Traverse each module segment in the initial module segment set generated in step S331. For each module segment, count the number of process steps it contains, the number of process parameters involved, and the number of parameter types. In addition, analyze the dependency relationship between the process steps inside the module segment and calculate the dependency density, for example, dividing the number of dependencies between steps by the square of the number of steps. The number of steps, parameter number, parameter type number, and dependency density of each module segment are used as module complexity metrics and stored in a JSON object. The complexity metric JSON objects of all module segments are stored in a list to form a module complexity metric set.

[0164] Preset a complexity threshold, for example, the step number threshold is 10, the parameter number threshold is 20, and the dependency density threshold is 0.5. Traverse the module complexity metric set generated in step S332. If any one of the complexity metrics of a certain module segment exceeds the preset threshold, mark this module segment as a high-complexity module segment and add its information to the high-complexity module segment set.

[0165] Traverse each module segment in the set of high-complexity module segments identified in step S333. Analyze the process steps and dependencies within the module segment to find suitable splitting points. Preferentially select the step with the least information transfer or parameter dependency with other steps as the splitting point to reduce the coupling degree between modules. Record the ID of the splitting point step for each high-complexity module segment to form splitting point data.

[0166] According to the splitting point data identified in step S334, redefine the boundaries of the high-complexity module segments. Split the original high-complexity module segments into two or more new module segments at the splitting points. Assign a unique ID to each newly generated module segment, and record the sequence of process steps, the starting key node ID, and the ending key node ID it contains (the splitting point serves as a new key node). Store the information of all new module segments, as well as the ID of the original module segment and the splitting point information, in a list to form boundary redefinition data.

[0167] According to the boundary redefinition data generated in step S335, update the initial module segment set. Replace the high-complexity module segments with the newly generated module segments after splitting to form an adjusted set of module segments.

[0168] Traverse the adjusted set of module segments generated in step S336, extract the starting node ID and ending node ID of each module segment, and store this information in a list to form the final module boundary set. The information of the initial module segments that have not been split is also included in the final module boundary set.

[0169] Preferably, step S4 includes the following steps:

[0170] Step S41: Obtain a new set of design requirement parameters; perform module matching on the process module library according to the new set of design requirement parameters to obtain a candidate module set;

[0171] Step S42: Sort the candidate module set according to the process meta-model to obtain a module sequence;

[0172] Step S43: Connect the modules in the module sequence according to the process meta-model to obtain a module network;

[0173] Step S44: Verify the process of the module network and perform process reorganization and optimization to obtain a reorganized process flow.

[0174] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes:

[0175] Step S41: Obtain a new set of design requirement parameters; perform module matching on the process module library according to the new set of design requirement parameters to obtain a candidate module set;

[0176] In the embodiments of the present invention, new design requirement parameters are input through a user interface, such as modifying clothing sizes (e.g., increasing the length of the clothes by 2 cm), changing the style (e.g., changing from a fitted style to a loose style), or adding new design elements (e.g., adding a zip pocket). These new design requirement parameters are stored as a new set of design requirement parameters in JSON format. Then, using the module matching method described in S418, according to the new set of design requirement parameters, matching modules are retrieved from the process module library constructed in step S35, and the information of the matching modules is stored in a list to form a candidate module set.

[0177] Step S42: Sort the candidate module set according to the process meta-model to obtain a module sequence;

[0178] In the embodiments of the present invention, the process meta-model constructed in step S15 is read from the Neo4j database to obtain the dependency relationships and constraint conditions between modules. Then, a topological sorting algorithm is used to sort the candidate module set obtained in step S41 to generate a module sequence that satisfies the dependency relationships and constraint conditions defined in the process meta-model. For example, if the process meta-model stipulates that the "cutting module" must be executed before the "sewing module", then in the sorted module sequence, the "cutting module" is located before the "sewing module". The sorted module sequence is stored in a list, and the list elements are module IDs.

[0179] Step S43: Connect the modules in the module sequence according to the process meta-model to obtain a module network;

[0180] In the embodiments of the present invention, according to the module connection method defined in the process meta-model constructed in step S15, the modules in the module sequence generated in step S42 are connected to form a module network. The module network is a directed graph, where the nodes in the graph represent modules and the edges represent the connection relationships between modules. The attributes of the edges include connection types (e.g., sequential connection, parallel connection, conditional connection) and conditional parameters. For example, if the "sew cuffs" module and the "sew body" module can be executed in parallel, a parallel connection relationship is established between these two modules. The generated module network is stored in the Neo4j database.

[0181] Step S44: Verify the process of the module network and perform process reorganization and optimization to obtain a reorganized process flow;

[0182] In the embodiments of the present invention, a rule-based inference engine is used to verify the module network generated in step S43, for example, to check whether the connections between modules conform to the rules and constraints defined in the process meta-model, whether there are circular dependencies, etc. If problems are found, the module network is adjusted and optimized according to predefined optimization rules and heuristic algorithms, such as modifying module parameters, adjusting module order, adding or deleting modules, etc. The optimization objectives include minimizing production time, minimizing material consumption, maximizing product quality, etc. The optimized module network is the reorganized process flow, which is stored in the Neo4j database.

[0183] Preferably, step S41 includes the following steps:

[0184] Step S411: Perform text parsing and keyword extraction on the new design requirement parameter set to obtain requirement key parameters;

[0185] Step S412: Perform semantic role annotation on the requirement key parameters to obtain semantic role annotation data;

[0186] Step S413: Query the pre-constructed ontology knowledge base in the clothing process field according to the requirement key parameters and the semantic role annotation data to obtain ontology knowledge data;

[0187] Step S414: Structure the requirement intention of the ontology knowledge data to obtain a structured requirement intention;

[0188] Step S415: Construct module feature vectors according to the process module library to obtain a set of module feature vectors;

[0189] Step S416: Calculate the semantic similarity between the requirement and the module according to the structured requirement intention and the set of module feature vectors to obtain a list of module semantic similarities;

[0190] Step S417: Perform module scoring based on machine learning according to the structured requirement intention and the set of module feature vectors to obtain a list of module matching scores;

[0191] Step S418: Screen and sort the candidate modules from the list of module semantic similarities and the list of module matching scores to obtain a set of candidate modules.

[0192] In the embodiment of the present invention, Jieba word segmentation tool is used to perform word segmentation and part-of-speech tagging on the text description in the new design requirement parameter set in JSON format obtained in step S41. For example, for the requirement description "add a zipper pocket on the left chest", the word segmentation result is "add / v a / m zipper / n pocket / n on / p left chest / n". Then, nouns and verbs are extracted as the key parameters of the requirements, such as "left chest", "add", "zipper", and "pocket". The extracted key parameters of the requirements are stored in a list.

[0193] Use the LTP semantic role labeling tool to perform semantic role labeling on the key parameters of the requirements extracted in step S411. For example, for "add a zipper pocket on the left chest", the labeling results are: A0 (add) = "on / p left chest / n", A1 (add) = "one / m zipper / n pocket / n". Among them, A0 represents the place where the action occurs, and A1 represents the recipient of the action. The semantic role labeling results are stored in a JSON object, for example: {"action"; "add", "location"; "left chest", "target"; "zipper pocket"}.

[0194] Use the SPARQL query language to query the pre-built clothing craft domain ontology knowledge base. For example, based on the key parameter "pocket" and the "target" in the semantic role annotation data; "zipper pocket", query the concepts related to "pocket" in the ontology knowledge base, such as "patch pocket", "dig pocket", "zipper pocket", etc., as well as the hierarchical relationships and attributes between them. Store the query results in a list to form ontology knowledge data.

[0195] According to the ontology knowledge data obtained in step S413, the semantic role annotation data obtained in step S412 is structured to form a structured demand intent. For example, "zipper pocket" is mapped to the corresponding concept in the ontology knowledge base, and its attributes, such as "pocket type", "zipper length", etc., are extracted. Finally, the structured demand intent is represented as a JSON object, for example: {"action"; "increase", "location"; "left chest", "target"; "pocket", "pocket_type"; "zipper pocket", "zipper_length"; "10cm"}.

[0196] Read the information of each module from the MongoDB process module library constructed in step S35, including module name, function description, input and output parameters, etc. Use the TF-IDF algorithm to extract the feature vectors of the module description text. At the same time, perform one-hot encoding on the input and output parameters of the module to form parameter feature vectors. Concatenate the text feature vectors and parameter feature vectors to form the comprehensive feature vector of the module. Store the feature vectors of all modules in a list to form a module feature vector set.

[0197] Convert the structured requirement intention obtained in step S414 into a feature vector. For example, use Word2Vec to convert the requirement keywords into word vectors, and then average the word vectors to obtain the requirement feature vector. Calculate the cosine similarity between the requirement feature vector and each module feature vector in the module feature vector set obtained in step S415 to obtain a list of module semantic similarities, where the list elements contain the module ID and the corresponding similarity score.

[0198] Use a pre-trained machine learning model (e.g., gradient boosting decision tree model) to score the modules. The input of the model is the feature vector of the structured requirement intention obtained in step S414 and the module feature vector obtained in step S415, and the output is the matching score of the module. Store the matching scores of all modules in a list to form a list of module matching scores, where the list elements contain the module ID and the corresponding matching score.

[0199] Sort the list of module semantic similarities obtained in step S416 and the list of module matching scores obtained in step S417 from high to low according to the similarity or score. Set a threshold, e.g., 0.8, and filter out the modules whose semantic similarity and module matching score are both higher than the threshold. If the new design requirement parameter set obtained in step S41 contains specific parameter constraints, e.g., "pocket type: zipper pocket", then further filter out the modules that meet the parameter constraints. Sort the filtered modules according to the comprehensive score (e.g., the weighted average of semantic similarity and module matching score) to obtain the final candidate module set, and store it in a list, where the list elements are module IDs.

[0200] Preferably, step S5 includes the following steps:

[0201] Step S51: Build a simulation environment according to the process meta-model to obtain a virtual simulation environment;

[0202] Step S52: Import the reorganized process flow into the virtual simulation environment and perform relevant process associations to obtain the imported process flow;

[0203] Step S53: Simulate and run the imported process flow to obtain the original simulation data;

[0204] Step S54: Analyze and evaluate the results of the original simulation data to obtain a simulation evaluation report;

[0205] Step S55: Optimize and adjust the parameters of the reorganized process flow according to the simulation evaluation report to obtain the simulation results.

[0206] In the embodiment of the present invention, the Clo3D software is used to build a virtual simulation environment. Read the process meta-model constructed in step S15 from the Neo4j database to obtain process parameters, module information, and the connection relationships between modules. Create a virtual human model and a clothing style in Clo3D according to the clothing style information in the process meta-model. Import the corresponding virtual fabric in Clo3D according to the fabric parameters in the process meta-model. Configure the parameters of virtual devices such as virtual sewing machines and virtual cutting tables in Clo3D according to the process parameters defined in the process meta-model, such as sewing speed, stitch length, cutting accuracy, etc.

[0207] Convert the reorganized process flow (a directed graph stored in the Neo4j database) generated in step S44 into an instruction sequence recognizable by Clo3D. For example, convert the "cutting module" into a cutting operation instruction in Clo3D, specifying the virtual fabric to be cut, the cutting path, and the cutting parameters. Convert the "sewing module" into a sewing operation instruction in Clo3D, specifying parameters such as the virtual garment pieces to be sewn, the type of sewing thread, the stitch length, and the sewing speed. Add all the instructions to the simulation process of Clo3D in sequence to form an imported process flow.

[0208] Run the imported process flow in the Clo3D virtual simulation environment. During the simulation run, record the execution time of each process step, the material consumption (e.g., fabric usage, sewing thread usage), and the product quality parameters (e.g., stitching strength, dimensional deviation). Store all the recorded data in a CSV file to form the original simulation data.

[0209] Read the original simulation data (CSV file) generated in step S53. Calculate the total production time, the total material consumption, and the average value and standard deviation of each product quality index. Compare the calculation results with the predefined quality standards and cost targets to evaluate the feasibility, efficiency, and economy of the process flow. Organize the evaluation results into a simulation evaluation report, and the report content includes the numerical values of each index, charts, and the analysis and summary of the simulation results.

[0210] According to the simulation evaluation report generated in step S54, if it is found that the simulation results do not meet the preset quality standards or cost targets, it is necessary to optimize and adjust the parameters of the reorganized process flow. For example, if it is found that the production time is too long, one can try to increase the sewing speed of the virtual sewing machine or adjust the process flow so that some steps are executed in parallel. Parameter optimization can use optimization algorithms such as genetic algorithms to minimize production time and material consumption and maximize product quality. Apply the optimized parameters to the Clo3D simulation environment, re-run the simulation, and store the optimized simulation results in the database to finally obtain the simulation results, including the optimized process parameters and the corresponding simulation data.

[0211] Preferably, the present invention also provides a modular design system for clothing processes with adjustable parameters, which is used to execute the modular design method for clothing processes with adjustable parameters as described above. The modular design system for clothing processes with adjustable parameters includes:

[0212] A meta-model construction unit, which is used to obtain a set of basic process parameters; define process association relationships according to the set of basic process parameters to obtain a process association diagram; construct a meta-model according to the process association diagram to obtain a process meta-model;

[0213] A parameterized design unit, which is used to obtain a set of target style parameters; map the parameters in the set of target style parameters to the process meta-model, adjust the process parameters, and obtain an adjusted set of process parameters; optimize the process flow according to the adjusted set of process parameters to obtain a parameterized process model;

[0214] A modular decomposition unit, which is used to analyze the process flow of the parameterized process model to obtain a process flow diagram; divide the module boundaries of the process flow diagram to obtain a set of module boundaries; extract module information from the parameterized process model according to the set of module boundaries and construct a process module library to obtain a process module library;

[0215] A module recombination unit, which is used to obtain a set of new design requirement parameters; match the modules in the process module library according to the set of new design requirement parameters to obtain a candidate module set; perform process recombination optimization according to the process meta-model and the candidate module set to obtain a reorganized process flow;

[0216] A process verification unit, which is used to perform model process simulation operation on the reorganized process flow according to the process meta-model to obtain original simulation data; verify the process of the original simulation data to obtain simulation results, so as to complete the modular design task of clothing processes.

[0217] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0218] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A modular design method for garment technology based on adjustable parameters, characterized in that: The following steps are involved: Step S1: Obtain a basic process parameter set; Define the process association relationship according to the basic process parameter set to obtain a process association diagram; The metamodel is constructed according to the process association diagram to obtain the process metamodel; Step S2: Obtain a target style parameter set; Mapping the parameters in the target style parameter set to the process meta-model, adjusting the process parameters, and obtaining the adjusted process parameter set; Optimize the process flow according to the adjusted process parameter set to obtain a parameterized process model; Step S3: performing process flow analysis on the parameterized process model to obtain a process flow chart; The process flow chart is divided into module boundaries to obtain a module boundary set; module information of the parameterized process model is extracted according to the module boundary set, and a process module library is constructed to obtain a process module library, wherein step S3 is specifically as follows: Step S31: performing process flow analysis on the parameterized process model to obtain a process flow chart; Step S32: identifying key nodes of the process flow chart to obtain a key node set; Step S33: Divide the process flow chart into module boundaries according to the key node set to obtain a module boundary set, wherein step S33 is specifically as follows: Step S331: performing initial module segmentation on the process flow chart according to the key node set to obtain an initial module segment set; Step S332: performing module complexity evaluation on the initial module segment set to obtain a module complexity index set; Step S333: using a preset complexity threshold to identify high-complexity modules in the module complexity index set, and obtaining a high-complexity module segment set; Step S334: identifying split points of the high-complexity module segment set to obtain split point data; Step S335: redrawing the boundary of the high-complexity module segment set according to the split point data to obtain boundary redrawing data; Step S336: adjusting the module boundaries of the high-complexity module segment set according to the boundary redrawing data to obtain an adjusted module segment set; Step S337: Determine the module boundary according to the adjusted module segment set and the initial module segment set to obtain a module boundary set; Step S34: extracting module information from the parameterized process model according to the module boundary set to obtain a module information set; Step S35: constructing a process module library for the module information set to obtain a process module library; Step S4: obtaining a new design requirement parameter set; performing module matching on the process module library according to the new design requirement parameter set to obtain a candidate module set; performing process reorganization optimization according to the process meta-model and the candidate module set to obtain a reorganized process flow; Step S5: performing model process simulation on the reorganized process flow according to the process element model to obtain original simulation data; The original simulation data is processed and the simulation results are obtained to realize the modular design task of clothing process.

2. The parameter-adjustable clothing process modular design method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting basic process parameters according to common clothing processes to obtain a basic process parameter set; Step S12: defining the process association relationship according to the basic process parameter set to obtain a process association diagram; Step S13: abstracting the process module of the process association diagram to obtain an abstract process module library; Step S14: parametrically expressing the abstract process module library to obtain a parameterized process module library; Step S15: constructing a meta-model for the parameterized process module library, the process association diagram, and the basic process parameter set to obtain a process meta-model.

3. The parameter-adjustable clothing process modular design method according to claim 2 is characterized in that: Step S12 includes the following steps: Step S121: construct a knowledge graph according to the basic process parameter set to obtain a clothing process knowledge graph; Step S122: creating a process node for the basic process parameter set to obtain a process node set; Step S123: performing relationship reasoning on the process node set according to the clothing process knowledge graph to obtain a reasoning relationship set; Step S124: constructing association relationships for the process node set according to the inference relationship set to obtain an initial process association graph; Step S125: Manually review and modify the initial process association diagram to obtain a process association diagram.

4. The parameter-adjustable clothing process modular design method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: acquiring design parameters for a target style to obtain a target style parameter set; Step S22: Mapping the parameters in the target style parameter set to the process meta-model to obtain a mapping parameter set; Step S23: adjusting the process parameters of the process element model according to the mapping parameter set to obtain an adjusted process parameter set; Step S24: selecting a process module for the process element model according to the adjusted process parameter set, and generating a process flow to obtain an initial process flow; Step S25: Optimize the initial process flow to obtain a parameterized process model.

5. The parameter-adjustable clothing process modular design method according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: obtaining a new design requirement parameter set; performing module matching on the process module library according to the new design requirement parameter set to obtain a candidate module set; Step S42: Sort the candidate module set according to the process meta-model to obtain a module sequence; Step S43: Connect the module sequence according to the process element model to obtain a module network; Step S44: verify the module network process, and perform process reorganization and optimization to obtain a reorganized process flow.

6. The parameter-adjustable clothing process modular design method according to claim 5 is characterized in that: Step S41 includes the following steps: Step S411: performing text parsing and keyword extraction on the new design requirement parameter set to obtain the key requirement parameters; Step S412: performing semantic role labeling on the key parameters of the requirements to obtain semantic role labeling data; Step S413: querying the pre-built clothing craft domain ontology knowledge base according to the required key parameters and the semantic role annotation data to obtain ontology knowledge data; Step S414: Structuring the demand intent of the ontology knowledge data to obtain structured demand intent; Step S415: constructing module feature vectors according to the process module library to obtain a module feature vector set; Step S416: Calculate the semantic similarity between the requirements and the modules according to the structured requirements intention and the module feature vector set to obtain a module semantic similarity list; Step S417: Perform module scoring based on machine learning according to the structured demand intention and the module feature vector set to obtain a module matching score list; Step S418: Screening and sorting candidate modules in the module semantic similarity list and the module matching score list to obtain a candidate module set.

7. The parameter-adjustable clothing process modular design method according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: constructing a simulation environment according to the process element model to obtain a virtual simulation environment; Step S52: importing the reorganized process flow into the virtual simulation environment, performing correlation on the relevant processes, and obtaining the imported process flow; Step S53: simulate and run the imported process flow to obtain original simulation data; Step S54: Analyze and evaluate the original simulation data to obtain a simulation evaluation report; Step S55: Optimize and adjust parameters of the reorganization process flow according to the simulation evaluation report to obtain simulation results.

8. A parameter-adjustable clothing process modular design system, characterized in that: Used to execute the parameter-adjustable clothing process modular design method according to claim 1, the parameter-adjustable clothing process modular design system comprises: A metamodel construction unit is used to obtain a basic process parameter set; define a process association relationship according to the basic process parameter set to obtain a process association diagram; and construct a metamodel according to the process association diagram to obtain a process metamodel; The parametric design unit is used to obtain a target style parameter set; map the parameters in the target style parameter set to the process meta-model, adjust the process parameters, and obtain an adjusted process parameter set; optimize the process flow according to the adjusted process parameter set to obtain a parametric process model; The modular decomposition unit is used to perform process analysis on the parameterized process model to obtain a process flow chart; divide the process flow chart into module boundaries to obtain a module boundary set; extract module information from the parameterized process model according to the module boundary set, and construct a process module library to obtain a process module library; The module reorganization unit is used to obtain a new design requirement parameter set; perform module matching on the process module library according to the new design requirement parameter set to obtain a candidate module set; perform process reorganization optimization according to the process meta-model and the candidate module set to obtain a reorganized process flow; The process verification unit is used to perform model process simulation on the reorganized process flow according to the process meta-model to obtain original simulation data; perform process verification on the original simulation data to obtain simulation results to realize the modular design task of clothing process.

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