CN-121998748-A - BOM data management method and system based on PLM

CN121998748ACN 121998748 ACN121998748 ACN 121998748ACN-121998748-A

Abstract

The invention relates to the technical field of data processing, in particular to a BOM data management method and system based on PLM, which comprises the steps of obtaining basic data of a product BOM, complementing missing parameters required by MTBF calculation to obtain a calculation ready BOM, splitting functional fragments according to a hierarchical structure for the calculation ready BOM, deploying MTBF calculation nodes to call the functional fragment calculation in parallel to obtain a dynamic reliability calculation result, analyzing the dynamic reliability calculation result through a decision tree algorithm, identifying potential fault points of the BOM and quantifying risks, generating an optimization scheme by combining a PLM with a component reliability knowledge base to obtain an optimized reliability calculation result, integrating key value-added content through an intelligent enhancement system based on the optimized reliability calculation result, and generating a standardized reliability prediction report according to a standard requirement. The scheme builds a BOM whole-flow closed-loop mechanism with the depth of the PLM and the MTBF, and improves reliability and accuracy.

Inventors

ZHENG XINHUA
HUANG QIANG
CHEN XING

Assignees

浙江兴达讯软件股份有限公司

Dates

Publication Date: 20260508
Application Date: 20260120

Claims (10)

1. A method of PLM-based BOM data management, the method comprising: acquiring BOM basic data of a product, complementing the missing parameters required by MTBF calculation by combining a built-in component reliability knowledge base of PLM, and obtaining a calculated ready BOM through format regularity and integrity check; splitting a function fragment according to a hierarchical structure of the calculation ready type BOM, deploying MTBF computing nodes, performing fragment calculation according to a parallel calling function, synchronizing actual working condition data of a full life cycle of a product to the MTBF computing nodes by using PLM, dynamically adjusting reliability parameters of components, fusing a computing result through PLM cooperation with a central hub, and checking consistency to obtain a dynamic reliability computing result; Analyzing a dynamic reliability calculation result through a decision tree algorithm, identifying BOM potential fault points and quantifying risks, pushing fault and risk information to a PLM system, generating an optimization scheme by combining a component reliability knowledge base by PLM, updating a calculation ready type BOM and synchronizing to an MTBF calculation node for secondary calculation, and obtaining an optimized reliability calculation result; Based on the optimized reliability calculation result, integrating key value-added content through the intelligent enhancement system, and regulating a report format according to bidding requirements to generate a standardized reliability prediction report.
2. The PLM-based BOM data management method of claim 1, wherein the specific obtaining process of the computation-ready BOM is as follows: docking a product design module through a standardized interface of the PLM system, collecting BOM basic data, and forming an original BOM data set; invoking a built-in component reliability knowledge base of the PLM, carrying out parameter matching based on component identifiers in an original BOM data set, extracting core parameters required by MTBF calculation, completing complementation on unmatched missing parameters according to a similar component parameter complementation rule, and obtaining a complete BOM of parameters; According to the parameter format requirement preset by MTBF, carrying out field standardization and normalization on the BOM with complete parameters, and unifying parameter units and data types; and sequentially executing MTBF (methyl tert-butyl function) calculation core parameter missing verification, parameter format compliance verification and BOM hierarchical structure rationality verification, removing invalid data and abnormal levels, and generating a calculation ready type BOM after verification.
3. The PLM-based BOM data management method of claim 2, wherein, for the computing ready-type BOM, splitting the function slices according to the hierarchical structure, the specific process of deploying MTBF computing nodes to call the function slices for computing in parallel is as follows: Analyzing the hierarchical structure of the calculation ready type BOM, identifying the functional boundaries of each module and the independent calculation units, and determining the partition granularity; dividing the slicing priority according to the importance of the module function; Labeling a unique identifier and an upstream and downstream calculation dependency relationship identifier for each fragment, wherein a fragment preamble fragment calculation result with a dependency relationship is a subsequent fragment input; According to the number of the fragments and the computation complexity, dynamically deploying corresponding MTBF computing nodes, distributing the fragments to idle computing nodes through a load balancing algorithm, and calling the fragment data by the MTBF computing nodes according to the priority order to execute parallel computation.
4. The method for managing BOM data based on PLM according to claim 3, wherein the specific process of dynamically adjusting the reliability parameters of components from the PLM synchronization product full life cycle actual condition data to the MTBF computing node is as follows: The PLM system is used for docking research and development test equipment, a production monitoring system and a field operation acquisition terminal to acquire full life cycle actual working condition data; Performing time sequence regulation and abnormal value elimination on working condition data according to a preset time interval to generate a standard working condition data set, and synchronizing the working condition data set to each MTBF computing node in real time through a message queue mechanism; Extracting the component information corresponding to the fragments by utilizing each MTBF computing node, and inquiring and matching the corresponding industry standard correction factors based on the working condition data; and dynamically updating the calculation parameters according to the correction factors and the original reliability parameters of the components and the components by a preset parameter adjustment formula.
5. The PLM-based BOM data management method of claim 4, wherein the specific obtaining process of the dynamic reliability calculation result is as follows: After the calculation of the fragments by each MTBF calculation node is finished, uploading the calculation result, the fragment identification and the calculation time stamp to the PLM cooperation center; Data alignment is carried out on the calculation result according to the slicing hierarchical relation through the PLM collaboration center, fusion weight is distributed based on module function importance, and the initial reliability result of the whole computer is calculated through a weighted summation algorithm; Setting a consistency check threshold, comparing the deviation of each fragment calculation result with the historical similar fragment calculation data and the homologous deviation of the calculation results of different nodes, and identifying an overrun abnormal result; and triggering recalculation of the fragments corresponding to the abnormal results, fusing the corrected fragments again until all the results meet the consistency requirement, and outputting a dynamic reliability calculation result.
6. The PLM-based BOM data management method of claim 5, wherein the specific process of identifying the BOM potential failure point and quantifying risk is as follows: Loading a dynamic reliability calculation result, and constructing a fault feature vector library by combining PLM historical fault data and an industry fault library; Adopting a decision tree algorithm to perform feature matching on the parameter abnormal value and the reliability substandard item in the calculation result, and identifying potential fault points; constructing a risk quantification model from two dimensions of fault occurrence probability and fault influence degree, calculating risk values of all fault points and dividing risk grades; And generating a fault risk analysis report containing the fault point positions, the feature descriptions and the risk grades.
7. The PLM-based BOM data management method of claim 6, wherein the specific obtaining process of the optimized reliability calculation result is as follows: pushing a fault risk analysis report to a PLM system, and utilizing a calculation ready type BOM node and component reliability knowledge base corresponding to PLM association; Generating a multi-dimensional optimization scheme set according to an evaluation criterion, wherein the multi-dimensional optimization scheme set comprises component replacement, BOM structure adjustment and parameter threshold correction; the optimal scheme is screened through cost and reliability balance analysis, the implementation feasibility of the scheme is verified, and a final optimal scheme is generated after no supply chain bottleneck and structural conflict are confirmed; Based on the final optimization scheme, automatically updating corresponding fields of the calculation ready type BOM, synchronously generating a BOM version update log, and pushing the updated calculation ready type BOM to an MTBF (methyl tert-butyl function) calculation node; the MTBF computing node loads the updated calculation ready type BOM, multiplexes the product full life cycle actual working condition data and parameter adjustment rules, and executes secondary reliability calculation; And (3) carrying out validity check on the secondary calculation result, and outputting the reliability calculation result after optimization after confirming that the result meets reliability improvement expectations and no data abnormality.
8. The PLM-based BOM data management method of claim 7, wherein the specific acquisition process of the standardized reliability prediction report is as follows: Extracting three kinds of value-added contents of fault optimization traceability records, similar product reliability standard-alignment data and historical project verification results endorsement from a PLM system; Sorting, integrating and logically carding the extracted value-added content, and editing the content according to a chapter structure required by a bidding party; Adopting a standardized report template to unify fonts and formats, organically fusing the value-added content with the optimized reliability calculation result, and generating a manuscript report; embedding a one-key traceability function module, and associating key data in a report with original data, a calculation log and an optimization record of the PLM system; And carrying out compliance verification on the draft report, and generating a standardized reliability prediction report after the verification is passed.
9. A PLM-based BOM data management system, wherein the system is configured to perform a PLM-based BOM data management method as claimed in any one of claims 1-8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which is executed by a processor to implement a PLM-based BOM data management method according to any one of the preceding claims 1-8.

Description

BOM data management method and system based on PLM Technical Field The invention relates to the technical field of data processing, in particular to a BOM data management method and system based on PLM. Background With the rapid growth of market bidding requirements of companies, the importance of the reliability department in outputting reliability prediction reports is becoming more prominent. The accuracy and specialized level of these reports not only directly reflect the quality of the company's product design, but also become key convincers when compared with opponents in a strong market competition. To improve the efficiency and accuracy of this link, companies have introduced MTBF software aimed at optimizing the reliability prediction process of related items through software tools. However, in using this software for reliability prediction, a non-negligible challenge is that the BOM (bill of materials) of a specific component must be manually imported into the software for calculation. The process is huge in engineering quantity, and accuracy of input information and timely updating are difficult to ensure, so that a great amount of labor investment is caused, and an ideal output effect cannot be obtained. For companies that have developed PLM (product lifecycle management) projects, this traditional manual introduction approach has clearly failed to meet the increasing business needs. Therefore, it is particularly urgent to find a reliability prediction method that is more efficient, accurate and capable of seamlessly interfacing with PLM projects. Disclosure of Invention According to the invention, by constructing a BOM full-flow automatic closed-loop mechanism with the depth of PLM and MTBF, the reliability prediction efficiency and accuracy are greatly improved, the artificial error is avoided, the professional requirement of bidding on reporting is met, and the data support is provided for the product reliability optimization. The technical scheme provided by the invention is that the BOM data management method based on PLM comprises the following steps: acquiring BOM basic data of a product, complementing the missing parameters required by MTBF calculation by combining a built-in component reliability knowledge base of PLM, and obtaining a calculated ready BOM through format regularity and integrity check; splitting a function fragment according to a hierarchical structure of the calculation ready type BOM, deploying MTBF computing nodes, performing fragment calculation according to a parallel calling function, synchronizing actual working condition data of a full life cycle of a product to the MTBF computing nodes by using PLM, dynamically adjusting reliability parameters of components, fusing a computing result through PLM cooperation with a central hub, and checking consistency to obtain a dynamic reliability computing result; Analyzing a dynamic reliability calculation result through a decision tree algorithm, identifying BOM potential fault points and quantifying risks, pushing fault and risk information to a PLM system, generating an optimization scheme by combining a component reliability knowledge base by PLM, updating a calculation ready type BOM and synchronizing to an MTBF calculation node for secondary calculation, and obtaining an optimized reliability calculation result; Based on the optimized reliability calculation result, integrating key value-added content through the intelligent enhancement system, and regulating a report format according to bidding requirements to generate a standardized reliability prediction report. Preferably, the specific obtaining process of the computing ready type BOM is as follows: docking a product design module through a standardized interface of the PLM system, collecting BOM basic data, and forming an original BOM data set; invoking a built-in component reliability knowledge base of the PLM, carrying out parameter matching based on component identifiers in an original BOM data set, extracting core parameters required by MTBF calculation, completing complementation on unmatched missing parameters according to a similar component parameter complementation rule, and obtaining a complete BOM of parameters; According to the parameter format requirement preset by MTBF, carrying out field standardization and normalization on the BOM with complete parameters, and unifying parameter units and data types; and sequentially executing MTBF (methyl tert-butyl function) calculation core parameter missing verification, parameter format compliance verification and BOM hierarchical structure rationality verification, removing invalid data and abnormal levels, and generating a calculation ready type BOM after verification. Preferably, for the computing ready type BOM, splitting the function fragments according to the hierarchical structure, and deploying the MTBF computing node to call the function fragments in parallel for computing, the specific process is as