Multi-configuration product management method based on PLM system

Abstract

The invention discloses a multi-configuration product management method based on a PLM system, and relates to the technical field of digital management, the management steps are as follows: S1, curing material type selection and BOM generation, and coding related rule configuration and curing; s2, building a drawing frame host-station part multi-stage modular system, disassembling a product into a host module and a plurality of station part modules, and setting unique basic information and a material set for each module; a flexible parameter interval of an adaptive interface is additionally arranged at a connecting node of a host module and a station part module, a cross-model compatible adaptive list is configured for each module, and parameters on different models of host modules are subjected to fine adjustment and adaptation. Through the design of the flexible parameter interval and the cross-model compatible list, the adaptation problem of different models of modules is solved, the BOM is generated by calling the automatic matching module through the rule, the problems of mistakes, omission and repetition of manual material selection are thoroughly eradicated, and absolute accuracy of a data source is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of digital management technology, specifically to a multi-configuration product management method based on a PLM system. Background Technology

[0002] As a core piece of equipment in the textile production process, the performance and configuration of the drawing frame directly determine the quality and production efficiency of subsequent spun yarns. With the rapid development of the textile industry, market demand exhibits significant personalization and customization characteristics. Different customers have raised differentiated configuration requirements for drawing frames, including capacity, compatible raw material types, process precision, and energy consumption levels. This necessitates a multi-model, multi-configuration product system for drawing frames to respond to market demands. Currently, the industry's management of multiple configurations of drawing frames generally relies on traditional manual intervention and decentralized system collaboration models. Human error is frequent, and the reliability of data sources is poor. Currently, core processes such as BOM compilation and material selection are entirely manual. Due to the complex structure of drawing frames and the variety of materials, manual operation is prone to quality problems such as incorrect material selection, omissions, and duplicate selections, leading to distorted BOM data. This distorted data is directly transmitted to the manufacturing process, easily causing a chain reaction of problems such as production rework, material waste, and substandard product quality, failing to guarantee the accuracy of the production data source. To address this, we propose a multi-configuration product management method based on a PLM system. Summary of the Invention

[0003] To address the aforementioned technical problems, a multi-configuration product management method based on a PLM system is provided. This technical solution resolves the problems described above.

[0004] To achieve the above objectives, the technical solution adopted by this invention is: a multi-configuration product management method based on a PLM system, the management steps of which are as follows: S1. Solidify material selection and BOM generation, configure coding rules and solidify them; S2. Build a multi-level modular system for the main body and station components of the drawing frame. Decompose the product into a main body module and several station component modules, and set a unique basic information and material set for each module. At the connection node between the main body module and the station component module, add a flexible parameter range for the adaptation interface, configure a cross-model compatible adaptation list for each module, and fine-tune the parameters on different models of main body modules. S3. Based on the specific configuration requirements of market orders, the system calls the fixed configuration rules and the completed multi-level modular structure to automatically match the corresponding host module and workstation component module to generate the BOM for the slitting machine. When there are ambiguous expressions, the system analyzes the core requirements and corrects the deviation parameters based on historical order data and adaptation rules. For the matched BOM, the system pushes alternative module combination schemes for decision-making reference. S4. Automatically name and encode the drawing machine configuration corresponding to the generated BOM according to the preset coding rules; S5. Synchronize BOM data and coding information to the ERP system, and continuously update, maintain, and reuse configuration rules and knowledge.

[0005] Preferably, in step S1, material selection is based on technical requirements and compliance conditions, and is screened from the preferred library. After technical matching, supply chain, cost, compliance and sample verification, it is solidified. The BOM generation adopts a multi-level tree structure, outputting multiple versions of BOM for design, process and production. After departmental review, version management is implemented and solidified through the system. The coding rule configuration adopts segmented combination coding to meet the requirements of uniqueness, readability, extensibility and compatibility. It is solidified through documentation, system embedding and verification mechanisms.

[0006] Preferably, in step S2, the multi-level modular system is established by: reviewing the product's entire lifecycle technical documents; forming a cross-domain technical team; establishing modular design principles and core technical indicators; based on these technical indicators, decoupling the product into a main unit core module and workstation functional modules; refining the sub-units of each module; developing and verifying standardized interface protocols; constructing a segmented unique coding system; assigning unique identifiers to each module; and building a module attribute database containing comprehensive information on technical parameters and interface attributes; compiling a modular bill of materials (BOM); and conducting material generalization design and compatibility verification; iteratively optimizing the modular system through prototype manufacturing, assembly debugging, and functional performance testing; forming a standardized technical manual and officially issuing it; and solidifying module information and BOM data into the enterprise PLM / ERP system, thus establishing a multi-level modular system.

[0007] Preferably, the flexible parameter range in step S2 includes flexible parameters at the physical interface level, flexible parameters at the electrical performance level, and flexible parameters at the signal transmission level. The flexible parameter range at the physical interface level includes: insertion / extraction force range, positioning pin / guide groove tolerance range, and interface insertion / extraction stroke margin range. The connection node has built-in pressure and displacement sensors to detect interface wear; the system automatically fine-tunes the range thresholds when the insertion / extraction force decreases or increases. The flexible parameter range at the electrical performance level includes: operating voltage compatibility range, rated current carrying capacity range, and grounding resistance threshold range. The flexible parameters at the signal transmission level, for the stability of data interaction, set anti-interference parameter ranges, including: signal transmission rate adaptation range, signal attenuation compensation range, and communication delay tolerance range. The adaptation list adopts a hierarchical classification and dynamic updating architecture.

[0008] Preferably, in step S2, the parameter fine-tuning and adaptation of different host module models is performed through a manifest matching step as follows: Select the target workstation component model through the host module's control interface, and automatically retrieve the cross-model compatibility and adaptation list to match the corresponding flexible parameter range and fine-tuning benchmark value. The host module reads the inherent parameters of the workstation components based on the adaptation algorithm, and calculates the specific step size for parameter fine-tuning by combining the current environmental data. Fine-tuning is performed step by step, following the order of physical parameters first, then electrical parameters, and finally signal parameters. The positioning pin gap and insertion / extraction force threshold are adjusted. After successful mechanical adaptation, the voltage / current output is fine-tuned to optimize the signal transmission rate and attenuation compensation value. Instantaneous detection is performed after each fine-tuning step. After fine-tuning, the system automatically performs verification tests, including: connection stability test, electrical performance test, signal transmission test and high and low temperature environment test; After successful verification, the fine-tuned parameters are fixed in the host module's adaptation database, and an adaptation report is generated.

[0009] Preferably, the configuration parameters in step S3 include: customer-specified drawing frame capacity, compatible raw material type, process customization requirements, machine model compatibility requirements, and compliance standards; the fixed configuration is called in layers, and the system calls the basic matching rules, constraint adaptation rules, and priority rules in order of priority; the BOM after matching includes engineering BOM, production BOM, and procurement BOM, and the generated BOM is checked for rules, compatibility, and cost, and the total cost of the BOM is calculated to see if it is within the order budget.

[0010] Preferably, in step S3, when dealing with non-quantitative descriptions of ambiguous expressions in an order, the ambiguous types of process, parameter, and model adaptation are first classified and identified. The similar parameter mapping library, deviation case library, and fixed adaptation rules of historical orders are retrieved, and they are converted into quantitative indicators through semantic analysis and parameter fitting. After comparing with the initial BOM parameters, the excess items are finely adjusted according to the flexible parameter range, the adaptation conflicts are corrected by matching adapters, and the cost deviations are corrected by replacing low-cost modules. After double verification by rules and historical cases, a correction report is generated. Based on the baseline BOM, the system generates three alternative solutions according to the priority principles of cost, performance, and delivery time. The system compares the module composition, technical indicators, cost, and delivery time of each solution using a structured table, and includes applicable scenarios and risk warnings. The system pushes solutions in order of order priority and provides a virtual operation simulation tool to assist in decision-making.

[0011] Preferably, in step S4, the encoding adopts a 24-bit 8-segment hierarchical structure, with each segment corresponding to the basic, core configuration, and workstation adaptation key information of the model, associated with data at each level of the BOM, and generating a check code through the CRC16 algorithm; naming is divided into two categories: basic and extended, and BOM key parameters are concatenated according to modular rules, the generation process is seamlessly connected to the BOM verification stage, automatically extracts data, concatenates codes and combines names, and binds and archives them after uniqueness verification; an exception handling mechanism is established to trigger prompts and manual review for parameter missing or exceeding limits.

[0012] Preferably, in step S5, ERP synchronization and BOM archiving are automatically triggered, and three types of data packages—basic, adaptation, and version—are structured and encapsulated to perform multi-module data mapping for ERP material management and production planning.

[0013] Preferably, in step S5, the rule management configuration establishes a scenario-driven dynamic update mechanism, implements full-cycle maintenance based on the version repository and regular reviews, and monitors abnormal rules in real time; and builds a multi-dimensional rule knowledge base to associate data.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention avoids rule confusion and execution deviations by solidifying material selection, BOM generation, and coding rules, providing a unified and implementable core basis for subsequent automated processes and reducing the uncertainty of subjective human intervention. The multi-level modular system enables precise disassembly and flexible combination of product structures to adapt to diverse customization needs. The design of flexible parameter ranges and cross-model compatibility lists solves the adaptation problem of different model modules, reduces the risk of interface incompatibility, and improves cross-model reusability. It achieves accurate and efficient BOM generation by automatically matching modules through rule calls, completely eliminating errors, omissions, and duplications in manual material selection and ensuring absolute accuracy of the data source. Attached Figure Description

[0015] Figure 1 This is a flowchart of the management steps of the present invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] Reference Figure 1 As shown, a multi-configuration product management method based on a PLM system includes the following management steps: S1. Solidify material selection and BOM generation, configure coding rules and solidify them; S2. Build a multi-level modular system for the main body and station components of the drawing frame. Decompose the product into a main body module and several station component modules, and set a unique basic information and material set for each module. At the connection node between the main body module and the station component module, add a flexible parameter range for the adaptation interface, configure a cross-model compatible adaptation list for each module, and fine-tune the parameters on different models of main body modules. S3. Based on the specific configuration requirements of market orders, the system calls the fixed configuration rules and the completed multi-level modular structure to automatically match the corresponding host module and workstation component module to generate the BOM for the slitting machine. When there are ambiguous expressions, the system analyzes the core requirements and corrects the deviation parameters based on historical order data and adaptation rules. For the matched BOM, the system pushes alternative module combination schemes for decision-making reference. S4. Automatically name and encode the drawing machine configuration corresponding to the generated BOM according to the preset coding rules; S5. Synchronize BOM data and coding information to the ERP system, and continuously update, maintain, and reuse configuration rules and knowledge.

[0018] By replacing manual labor with rules, BOM generation has been 100% automated, eliminating quality issues such as errors, omissions, and duplicates caused by manual material selection, and providing an absolutely accurate data source for manufacturing. The process of BOM compilation and verification, which originally took hours or even days, has been shortened to minutes, enabling rapid response to market orders. Through multi-level modularization of "mainframe-workstation components," precise management of complex product structures has been achieved, allowing for flexible combinations to meet diverse sales needs while ensuring consistency between design and production data. Innovative automatic naming rules ensure that each configuration corresponds to a unique code, meeting the data specifications of PLI / ERP systems and unlocking the full potential of enterprise digitalization. The selection experience of senior engineers has been solidified into the system's configuration rules, forming the enterprise's core digital assets, reducing reliance on individual experience, and facilitating knowledge transfer and reuse.

[0019] In step S1, material selection is based on technical requirements and compliance conditions, and is screened from the preferred library. After technical matching, supply chain, cost, compliance and sample verification, it is solidified. The BOM generation adopts a multi-level tree structure, outputting multiple versions of BOM for design, process and production. After departmental review, version management is implemented and solidified through the system. The coding rule configuration adopts segmented combination coding to meet the requirements of uniqueness, readability, extensibility and compatibility. It is solidified through documentation, system embedding and verification mechanisms.

[0020] This application establishes a robust foundation for multi-configuration management of drawing frames through triple standardization and solidification of material selection, BOM generation, and coding rules, offering significant core benefits. Material selection involves screening from an optimal library and undergoing multi-dimensional verification based on technology, supply chain, cost, compliance, and sample validation before being solidified. This ensures materials meet technical and compliance requirements, locks in high-quality resources, and proactively mitigates compatibility and supply chain risks. The BOM adopts a multi-level tree structure, outputting multiple versions of design, process, and production to adapt to full-process requirements. After review, version management is solidified to ensure data consistency and full lifecycle traceability. Segmented coding rules meet uniqueness and readability requirements, and are solidified through documentation, system embedding, and verification mechanisms, resolving coding chaos and supporting multi-system digital collaboration. Overall, this approach completely eliminates the arbitrariness of traditional manual operations, providing a unified standard for subsequent automated processes, significantly reducing the risk of human error, and laying a solid foundation for full-process automated management.

[0021] In step S2, the multi-level modular system is established by reviewing the product's entire lifecycle technical documents, forming a cross-domain technical team, and establishing modular design principles and core technical indicators. Based on these technical indicators, the product is decoupled into a main unit core module and workstation functional modules, with each module's sub-units refined, and standardized interface protocols developed and verified. A segmented unique coding system is constructed, assigning unique identifiers to each module, and building a module attribute database containing comprehensive information on technical parameters and interface attributes. A modular bill of materials (BOM) is compiled, and material generalization design and compatibility verification are conducted. Through prototype manufacturing, assembly debugging, and functional performance testing, the modular system is iteratively optimized, resulting in a standardized technical manual that is officially promulgated. Module information and BOM data are then integrated into the enterprise's PLM / ERP system, establishing a multi-level modular system.

[0022] This application establishes a multi-level modular system for parallel processing machines through a systematic process. Its core value lies in building a standardized, highly adaptable, and reusable product structure foundation, while simultaneously solidifying the foundation for end-to-end digital collaboration. The benefits are primarily reflected in the following aspects: First, ensuring a scientifically sound and standardized system. Based on the review of full-lifecycle technical documents and cross-domain team collaboration, scientific design principles and core indicators are established, and a standardized technical manual is formed and promulgated, ensuring the system meets end-to-end requirements and is consistently implemented. Second, improving configuration flexibility and reusability. The product is decoupled into main unit and workstation modules, with standardized interfaces enabling flexible combination and interchange, quickly responding to customized needs; generalized material design and compatibility verification reduce development costs and shorten cycles. Third, achieving precise module control. Through segmented unique coding and a full-dimensional attribute database, precise traceability of the entire module lifecycle is achieved, and modular bills of materials optimize material management efficiency. Fourth, ensuring a reliable and stable system. Through multiple rounds of trial production, debugging, and iterative testing and optimization, adaptation and performance issues are resolved in advance, reducing the risk of operational failures. Fifth, supporting digital collaboration. Module information and BOM data are solidified into PLM / ERP systems, opening up cross-departmental data links and providing core data support for subsequent automated processes.

[0023] The flexible parameter ranges in step S2 include physical interface flexibility parameters, electrical performance flexibility parameters, and signal transmission flexibility parameters. The physical interface flexibility parameter ranges include: insertion / extraction force range, positioning pin / guide groove tolerance range, and interface insertion / extraction stroke margin range. Connection nodes have built-in pressure and displacement sensors to detect interface wear; the system automatically fine-tunes the range thresholds when the insertion / extraction force decreases or increases. The electrical performance flexibility parameter ranges include: operating voltage compatibility range, rated current carrying capacity range, and grounding resistance threshold range. The signal transmission flexibility parameters, designed for data interaction stability, set anti-interference parameter ranges, including: signal transmission rate adaptation range, signal attenuation compensation range, and communication delay tolerance range. The adaptation list adopts a hierarchical classification and dynamic updating architecture.

[0024] The core benefits of this application's flexible parameter range and hierarchical dynamic adaptation list design are: significantly improved compatibility and connection stability between the host and workstation component modules, while reducing adaptation costs and maintenance risks, laying the foundation for cross-model reuse; multiple flexible parameter ranges in the physical interface dimension can adapt to differences in interface materials and processing errors of different models, and built-in sensors enable wear-adaptive fine-tuning to avoid hard contact losses and extend interface life; flexible ranges in the electrical performance dimension are compatible with different host power supply characteristics, ensuring power supply and grounding safety and improving electromagnetic compatibility performance; and anti-interference parameter ranges in the signal transmission dimension adapt to different communication protocols and environmental interference, ensuring stable and reliable data interaction.

[0025] In step S2, the parameters on different host modules are fine-tuned and adapted through a manifest matching step: Select the target workstation component model through the host module's control interface, and automatically retrieve the cross-model compatibility and adaptation list to match the corresponding flexible parameter range and fine-tuning benchmark value. The host module reads the inherent parameters of the workstation components based on the adaptation algorithm, and calculates the specific step size for parameter fine-tuning by combining the current environmental data. Fine-tuning is performed step by step, following the order of physical parameters first, then electrical parameters, and finally signal parameters. The positioning pin gap and insertion / extraction force threshold are adjusted. After successful mechanical adaptation, the voltage / current output is fine-tuned to optimize the signal transmission rate and attenuation compensation value. Instantaneous detection is performed after each fine-tuning step. After fine-tuning, the system automatically performs verification tests, including: connection stability test, electrical performance test, signal transmission test and high and low temperature environment test; After successful verification, the fine-tuned parameters are fixed in the host module's adaptation database, and an adaptation report is generated.

[0026] This application uses a list matching method to lock in the flexible parameter range and benchmark value, and combines the inherent parameters of the components and environmental data to calculate the fine-tuning step size, avoiding adaptation deviations caused by blind adjustments. The process is scientific and orderly, with step-by-step fine-tuning in the order of "physical-electrical-signal", coupled with instantaneous detection at each step to prevent parameter sudden changes from damaging the module and ensure the safety of the adaptation process. The adaptation reliability is high, with multi-dimensional verification testing to comprehensively examine the adaptation effect. High and low temperature environment testing further ensures the adaptation stability under complex working conditions. The data is traceable and reusable, with fine-tuning parameters fixed and archived and adaptation reports generated to provide reference for subsequent similar adaptation needs, while also helping to iterate and optimize the adaptation algorithm. The adaptation efficiency is improved, with full-process automated execution, reducing manual intervention, quickly completing cross-model adaptation, and supporting efficient response to multiple configuration requirements.

[0027] The configuration parameters in step S3 include: customer-specified drawing frame capacity, compatible raw material types, process customization requirements, machine compatibility requirements, and compliance standards; the fixed configuration is called in layers, and the system calls the basic matching rules, constraint adaptation rules, and priority rules in order of priority; the BOM after matching includes engineering BOM, production BOM, and procurement BOM, and the generated BOM is checked for rules, compatibility, and cost, and the total cost of the BOM is calculated to see if it is within the order budget.

[0028] This application demonstrates precise demand conversion, clearly defining core order configuration parameters to ensure that BOM generation closely aligns with customer capacity, process, and compliance customization needs, avoiding demand deviations. The matching logic is scientific, employing layered calls to basic matching, constraint adaptation, and priority rules, balancing core parameter matching, cross-model compatibility, and order priority to improve the rationality of module combination. BOM adaptation covers the entire process, with multiple versions of BOMs from engineering, production, and procurement matching R&D, production, and procurement needs respectively, achieving business collaboration. Triple verification covers rule compliance, module compatibility, and cost budget, proactively avoiding adaptation failures and cost overruns, ensuring accurate and usable BOM data, and laying a solid foundation for subsequent production and delivery.

[0029] In step S3, when dealing with non-quantitative descriptions in orders that are ambiguous, the process, parameters, and model adaptation ambiguity types are first categorized and identified. The similar parameter mapping library, deviation case library, and fixed adaptation rules of historical orders are retrieved, and they are converted into quantitative indicators through semantic analysis and parameter fitting. After comparing with the initial BOM parameters, the excess items are fine-tuned according to the flexible parameter range, the adaptation conflicts are corrected by matching adapters, and the cost deviations are corrected by replacing low-cost modules. After double verification by rules and historical cases, a correction report is generated. Based on the baseline BOM, the system generates three alternative solutions according to the priority principles of cost, performance, and delivery time. The system compares the module composition, technical indicators, cost, and delivery time of each solution using a structured table, and includes applicable scenarios and risk warnings. The system pushes solutions in order of order priority and provides a virtual operation simulation tool to assist in decision-making.

[0030] This application addresses the challenge of quantifying fuzzy requirements by classifying and identifying processes and fuzzy parameter types, retrieving historical order data and adaptation rules, and combining semantic analysis and parameter fitting to transform qualitative descriptions into quantitative indicators. Simultaneously, it corrects over-limit items, resolves adaptation conflicts, and balances cost deviations based on flexible parameter ranges. This dual verification ensures parameter accuracy and avoids BOM errors caused by misunderstandings of requirements. Based on a benchmark BOM, it generates three priority alternatives categorized by cost, performance, and delivery time. A structured table provides a clear comparison of the module composition, indicators, costs, and delivery times of each option, along with scenario suggestions and risk warnings. Coupled with a virtual simulation tool, it helps technical and business personnel quickly select the optimal solution, improving order response efficiency. The fully automated process reduces manual intervention, satisfying diverse customer customization needs while quickly matching actual enterprise production and procurement conditions, thus enhancing market competitiveness.

[0031] In step S4, the coding adopts a 24-bit, 8-segment hierarchical structure. Each segment corresponds to the basic, core configuration, and workstation adaptation key information of the model, and is associated with data at each level of the BOM. A check code is generated using the CRC16 algorithm. Naming is divided into two categories: basic and extended. Key BOM parameters are concatenated according to modular rules. The generation process is seamlessly connected to the BOM verification stage, automatically extracting data, concatenating codes and combining names, and binding and archiving them after uniqueness verification. An exception handling mechanism is established to trigger prompts and manual review for missing parameters and exceeding limits.

[0032] This application employs a 24-bit, 8-segment hierarchical coding system, accurately linking basic machine information, core configuration details, and workstation compatibility information. This gives the coding its own "identity attribute," enabling rapid identification of configuration details. The CRC16 algorithm-generated checksum technically eliminates coding duplication and errors, completely resolving the management chaos of "one item, multiple codes" and "one code, multiple items," and meeting the data specification requirements of PLM / ERP systems. The dual design of basic and extended names balances the needs for rapid daily identification with in-depth technical traceability. Modular splicing rules ensure unified naming standards, facilitating collaborative use across departments.

[0033] In the S5 step, ERP synchronization and BOM archiving are automatically triggered, and three types of data packages—basic, adaptation, and version—are structured and encapsulated to perform data mapping for multiple modules of ERP material management and production planning.

[0034] This application's synchronization process is strongly bound to BOM archiving and automatically triggered, eliminating the need for manual secondary entry and completely avoiding errors and omissions caused by manual transcription. The structured encapsulation of three types of data packages—basic, adaptation, and version—ensures that the data transmitted to the ERP is complete and formatted uniformly, providing a high-quality data source for subsequent business operations.

[0035] In step S5, rule management is configured to establish a scenario-driven dynamic update mechanism, implement full-cycle maintenance based on version repository and regular reviews, and monitor abnormal rules in real time; a multi-dimensional rule knowledge base is built to associate data.

[0036] The scenario-driven dynamic update mechanism of this application can accurately respond to the import of new models, the addition of customized requirements, and the feedback of failure cases in adapting to actual business scenarios, ensuring that the rules always conform to the latest technical standards and market demands; version library management and regular review can control the rules throughout their entire lifecycle, avoid rules from becoming outdated or invalid, and clearly record the rule iteration trajectory for easy traceability and rollback.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A multi-configuration product management method based on a PLM system, characterized in that, The management steps are as follows: S1. Solidify material selection and BOM generation, configure coding rules and solidify them; S2. Build a multi-level modular system for the main body and station components of the drawing frame. Decompose the product into a main body module and several station component modules, and set a unique basic information and material set for each module. At the connection node between the main body module and the station component module, add a flexible parameter range for the adaptation interface, configure a cross-model compatible adaptation list for each module, and fine-tune the parameters on different models of main body modules. S3. Based on the specific configuration requirements of market orders, the system calls the fixed configuration rules and the completed multi-level modular structure to automatically match the corresponding host module and workstation component module to generate the BOM for the slitting machine. When there are ambiguous expressions, the system analyzes the core requirements and corrects the deviation parameters based on historical order data and adaptation rules. For the matched BOM, the system pushes alternative module combination schemes for decision-making reference. S4. Automatically name and encode the drawing machine configuration corresponding to the generated BOM according to the preset coding rules; S5. Synchronize BOM data and coding information to the ERP system, and continuously update, maintain, and reuse configuration rules and knowledge.

2. The multi-configuration product management method based on a PLM system according to claim 1, characterized in that: In step S1, material selection is based on technical requirements and compliance conditions, and is screened from the preferred library. After technical matching, supply chain, cost, compliance and sample verification, the material selection is finalized. The BOM generation adopts a multi-level tree structure, outputting multiple versions of BOMs for design, process, and production. After departmental review, version management is implemented and the BOMs are solidified through the system. The coding rule configuration adopts a segmented combination coding to meet the requirements of uniqueness, readability, extensibility, and compatibility. The BOMs are solidified through documentation, system embedding, and verification mechanisms.

3. The multi-configuration product management method based on a PLM system according to claim 1, characterized in that, In step S2, the multi-level modular system is established by reviewing the product's entire lifecycle technical documents, forming a cross-domain technical team, and establishing modular design principles and core technical indicators. Based on these technical indicators, the product is decoupled into a main unit core module and workstation functional modules, with each module's sub-units refined, and standardized interface protocols developed and verified. A segmented unique coding system is constructed, assigning unique identifiers to each module, and building a module attribute database containing comprehensive information on technical parameters and interface attributes. A modular bill of materials (BOM) is compiled, and material generalization design and compatibility verification are conducted. Through prototype manufacturing, assembly debugging, and functional performance testing, the modular system is iteratively optimized, resulting in a standardized technical manual that is officially promulgated. Module information and BOM data are then integrated into the enterprise's PLM / ERP system, establishing a multi-level modular system.

4. The multi-configuration product management method based on a PLM system according to claim 1, characterized in that: The flexible parameter ranges in step S2 include physical interface flexibility parameters, electrical performance flexibility parameters, and signal transmission flexibility parameters. The physical interface flexibility parameter ranges include: insertion / extraction force range, positioning pin / guide groove tolerance range, and interface insertion / extraction stroke margin range. Connection nodes have built-in pressure and displacement sensors to detect interface wear; the system automatically fine-tunes the range thresholds when the insertion / extraction force decreases or increases. The electrical performance flexibility parameter ranges include: operating voltage compatibility range, rated current carrying capacity range, and grounding resistance threshold range. The signal transmission flexibility parameters, designed for data interaction stability, set anti-interference parameter ranges, including: signal transmission rate adaptation range, signal attenuation compensation range, and communication delay tolerance range. The adaptation list adopts a hierarchical classification and dynamic updating architecture.

5. A multi-configuration product management method based on a PLM system according to claim 1, characterized in that, In step S2, the parameters on different host modules are fine-tuned and adapted through a manifest matching step: Select the target workstation component model through the host module's control interface, and automatically retrieve the cross-model compatibility and adaptation list to match the corresponding flexible parameter range and fine-tuning benchmark value. The host module reads the inherent parameters of the workstation components based on the adaptation algorithm, and calculates the specific step size for parameter fine-tuning by combining the current environmental data. Fine-tuning is performed step by step, following the order of physical parameters first, then electrical parameters, and finally signal parameters. The positioning pin gap and insertion / extraction force threshold are adjusted. After successful mechanical adaptation, the voltage / current output is fine-tuned to optimize the signal transmission rate and attenuation compensation value. An instantaneous detection is performed after each fine-tuning step; After fine-tuning, the system automatically performs verification tests, including: connection stability test, electrical performance test, signal transmission test and high and low temperature environment test; After successful verification, the fine-tuned parameters are fixed in the host module's adaptation database, and an adaptation report is generated.

6. The multi-configuration product management method based on a PLM system according to claim 1, characterized in that, The configuration parameters in step S3 include: customer-specified drawing frame capacity, compatible raw material types, process customization requirements, machine compatibility requirements, and compliance standards; the fixed configuration is called in layers, and the system calls the basic matching rules, constraint adaptation rules, and priority rules in order of priority; the BOM after matching includes engineering BOM, production BOM, and procurement BOM, and the generated BOM is checked for rules, compatibility, and cost, and the total cost of the BOM is calculated to see if it is within the order budget.

7. A multi-configuration product management method based on a PLM system according to claim 1, characterized in that: In step S3, when dealing with non-quantitative descriptions in orders that are ambiguous, the process, parameters, and model adaptation ambiguity types are first categorized and identified. The similar parameter mapping library, deviation case library, and fixed adaptation rules of historical orders are retrieved, and they are converted into quantitative indicators through semantic analysis and parameter fitting. After comparing with the initial BOM parameters, the excess items are fine-tuned according to the flexible parameter range, the adaptation conflicts are corrected by matching adapters, and the cost deviations are corrected by replacing low-cost modules. After double verification by rules and historical cases, a correction report is generated. Based on the baseline BOM, the system generates three alternative solutions according to the priority principles of cost, performance, and delivery time. The system compares the module composition, technical indicators, cost, and delivery time of each solution using a structured table, and includes applicable scenarios and risk warnings. The system pushes solutions in order of order priority and provides a virtual operation simulation tool to assist in decision-making.

8. A multi-configuration product management method based on a PLM system according to claim 1, characterized in that: In step S4, the coding adopts a 24-bit 8-segment hierarchical structure. Each segment corresponds to the basic, core configuration and workstation adaptation key information of the model, and is associated with data at each level of BOM. The check code is generated through the CRC16 algorithm. The naming is divided into two categories: basic and extended. The key parameters of BOM are spliced ​​according to modular rules. The generation process is seamlessly connected to the BOM verification stage. Data is automatically extracted, coding is spliced ​​and names are combined, and after uniqueness verification, they are bound and archived. Establish an anomaly handling mechanism to trigger prompts and manual review for missing parameters and exceeding limits.

9. A multi-configuration product management method based on a PLM system according to claim 1, characterized in that: In the S5 step, ERP synchronization and BOM archiving are automatically triggered, and three types of data packages—basic, adaptation, and version—are structured and encapsulated to perform data mapping for multiple modules of ERP material management and production planning.

10. A multi-configuration product management method based on a PLM system according to claim 1, characterized in that: In step S5, rule management is configured to establish a scenario-driven dynamic update mechanism, implement full-cycle maintenance based on version repository and regular reviews, and monitor abnormal rules in real time; a multi-dimensional rule knowledge base is built to associate data.

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