Spatial Indexing for Massive PDM Model Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Product Data Management (PDM) systems face challenges in efficiently managing and visualizing massive models with millions of parts, particularly in identifying visible components and handling large datasets with rich design histories, leading to storage and processing inefficiencies.
Innovation Solution
The implementation of a hierarchical product data structure with spatial indexing using occurrence nodes, anchor occurrence tables, and equivalency tables to compactly represent and query the spatial and structural information of product components, allowing for efficient identification of visible components and reducing storage and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional PDM systems store and process complete hierarchical product data structures with all occurrence nodes for massive models, then comprehensive product data management is achieved, but storage requirements and processing time increase significantly
Solution Approach 1:
The patent merges structurally equivalent occurrence nodes into a single representative node within each cell. Multiple occurrence nodes that share the same parent-component relationship and spatial location are combined, storing only one instance with references to all equivalent occurrences. This merging dramatically reduces storage requirements while preserving complete product data through the equivalence relationships.
Solution Approach 2:
The representative occurrence node serves multiple functions: it represents all equivalent occurrences, stores shared structural information, provides spatial indexing, and enables efficient querying. This universal node performs what would traditionally require multiple separate nodes, reducing overall data structure size while maintaining full functionality for product data management.
2Productivity
If traditional PDM systems process and render all components in massive models with millions of parts, then complete model visualization is achieved, but rendering time and computational resources increase significantly
Solution Approach 1:
The patent segments the product data structure into cell-based spatial units with representative occurrence nodes. Instead of processing all occurrence nodes uniformly, the system divides the model into spatial cells and processes only relevant cells and their representative nodes during rendering. This segmentation enables efficient culling of invisible components and dramatically improves rendering speed for massive models.
Solution Approach 2:
The patent performs preliminary spatial indexing and occurrence equivalence grouping before rendering operations. By pre-organizing occurrence nodes into representative nodes with spatial cell assignments, the system prepares the data structure for efficient query and rendering. This preliminary organization reduces computational complexity during actual rendering operations.
3Measurement precision
If spatial indexing is implemented using all occurrence nodes, then precise spatial location identification is achieved, but indexing storage and query processing overhead increase
Solution Approach 1:
The patent merges spatial indexing data for equivalent occurrence nodes into a single index entry for each representative occurrence node. Instead of creating separate index entries for every occurrence node, the system creates one index entry per representative node that covers all equivalent occurrences in that spatial cell. This maintains precise spatial location identification while minimizing indexing data volume.
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
Systems and methods for massive model visualization in product data management (PDM) systems comprises: - storing (505) a hierarchical product data structure that includes a plurality of occurrence nodes and component nodes; - creating (515) an occurrence equivalency table (410) from the hierarchical product data structure, that identifies at least one anchor occurrence node and at least one equivalent occurrence node, wherein product component corresponding to the equivalent occurrence node is spatially located within a specified distance threshold (delta) of product component corresponding to the anchor occurrence node; - creating (515) an anchor occurrence table corresponding to the hierarchical product data structure that lists a plurality of unique occurrence chain represented by the hierarchical product data structure, where each equivalent occurrence node is replaced by its corresponding anchor occurrence node, and that associates each listed unique occurrence chain with an associated cell index value.