Hierarchical Product Data Navigation for Large RFLP Models
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Solution Overview
Problem
Current Product Data Management (PDM) systems face challenges in efficiently navigating and authoring large, complex product lifecycle data across various stages, particularly in managing hundreds of thousands of objects and performing impact analysis in a user-friendly and scalable manner.
Innovation Solution
The system employs a diagrammatic approach to navigate and author configured product lifecycle data by receiving requests for expanded details, parsing and configuring models, traversing to collect data, and packaging it for clients, enabling seamless integration with bill of materials and index BOM, and providing real-time navigable diagrams for understanding product lifecycle aspects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional PDM systems are used to manage large product lifecycle data, then data management capability is provided, but navigation and authoring efficiency deteriorates when handling hundreds of thousands of objects
Solution Approach 1:
The patent segments the large product lifecycle data model into hierarchical levels (level 1, level 2, etc.) where each level represents a manageable subset of the total data. This allows users to navigate and author data in smaller, organized portions rather than dealing with all hundreds of thousands of objects simultaneously, thereby maintaining efficiency while managing large quantities of data.
Solution Approach 2:
The patent introduces a new dimensional organization by creating multi-level hierarchical views of the product data. Instead of a single flat structure, data is organized across multiple levels that can be navigated vertically and horizontally, adding a dimensional aspect to data access that improves navigation efficiency in large datasets.
2Reliability
If detailed impact analysis is performed across product development stages, then decision accuracy improves, but system complexity and time consumption increases
Solution Approach 1:
The patent segments impact analysis into level-specific analyses where changes at one hierarchical level are analyzed in context with that level's data structure and relationships. This segmented approach maintains analysis accuracy by considering local contexts while reducing overall system complexity through modular, level-by-level processing rather than requiring simultaneous analysis of the entire product lifecycle data set.
Solution Approach 2:
The patent performs preliminary configuration and organization of data into hierarchical levels before conducting impact analysis. This preliminary structuring enables more efficient and accurate impact analysis by pre-establishing the contextual relationships and data organization needed for reliable analysis, thereby reducing the complexity and time required during the actual analysis phase.
3Loss of information
If comprehensive product lifecycle data is managed, then data completeness improves, but user interface usability and scalability deteriorates
Solution Approach 1:
The patent segments comprehensive product lifecycle data into organized hierarchical levels that present information in manageable, context-appropriate groups. This segmentation maintains data completeness by preserving all necessary information while improving interface usability by organizing it into navigable levels that prevent overwhelming users with the full scope of data at once.
Solution Approach 2:
The patent adds hierarchical level as a new dimension for data presentation, allowing comprehensive data to be accessed and manipulated through multi-level views. This dimensional organization enables users to navigate comprehensive data sets through structured levels, improving usability by providing contextual organization while maintaining complete data access capability.
Data Source
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AI summary
Methods for product data management and corresponding systems and computer-readable mediums. The systems and methods include receiving a request (525) for expanded details (445) about an architecture element (410) of a model (405) from an application client (325), parsing the request (525) to identify the expanded details (445) of the architecture element (410), identifying a structure (200) and configuration details (415) of the model (405), configuring the model (405) according to the structure (200) and configuration details (415), traversing the model (405) to collect the expanded details (445) for the architecture element (410), packaging the expanded details (445) into an application format (605) for the application client (325), and returning the expanded details (445).