Model Element Identification via Multi-Stage Segmentation
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Solution Overview
Problem
Existing model comparison tools face difficulties in identifying corresponding elements between different models, especially when multiple non-unique elements are connected, as they rely on unique identifiers, neighbors, and positions, which are not always reliable.
Innovation Solution
A computer-implemented method that builds sets of elements with identical identifiers, compares unique identifiers, positions, and neighbors to determine corresponding elements, and iteratively refines the identification process to handle cases with multiple non-unique elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unique identifiers (OID) are used for element comparison, then comparison is always unique, but elements with the same comparison-identifier cannot be distinguished when multiple non-unique elements exist
Solution Approach 1:
The patent segments the identification process into multiple stages: first comparing unique OIDs, then comparing comparison-identifiers, and finally using positional information and neighbor relationships to resolve ambiguities when multiple elements share the same comparison-identifier. This multi-level segmentation allows the system to handle both unique and non-unique elements effectively.
Solution Approach 2:
The patent introduces positional information (top/left coordinates) and neighbor relationships as intermediary criteria to mediate the comparison process. When unique identifiers are insufficient, these intermediaries serve as additional differentiation factors to identify corresponding elements among non-unique elements.
2Measurement precision
If comparison relies on neighbors and positions, then non-unique elements can be distinguished, but these conditions are rarely fulfilled in real modeling scenarios
Solution Approach 1:
The patent implements a dynamic comparison approach where the system adaptively selects which criteria to use based on the specific situation. It first attempts unique OID comparison, then falls back to comparison-identifier matching, and only when necessary uses positional and neighbor-based differentiation. This dynamic strategy ensures broad applicability while maintaining precision when needed.
Solution Approach 2:
The patent changes the parameters used for comparison based on the uniqueness of elements. For unique elements, it uses OID; for non-unique elements, it transitions to using comparison-identifier, position, and neighbor relationships. This parameter adaptation allows the system to handle diverse modeling scenarios effectively.
3Measurement precision
If multiple criteria (identifier, position, neighbors) are used for comparison, then accuracy improves, but complexity of the comparison algorithm increases
Solution Approach 1:
The patent performs preliminary filtering by first comparing unique OIDs and then comparison-identifiers before resorting to more complex positional and neighbor-based analysis. This preliminary action eliminates many cases early in the process, reducing the overall complexity by avoiding unnecessary application of complex criteria to cases that can be resolved with simpler methods.
Data Source
AI summary
Certain example embodiments relate to systems and/or methods for identifying corresponding elements in different models (e.g., two or more different models), where multiple non-unique elements are connected with one another. The techniques of certain example embodiments are suitable for comparing the different models and identifying corresponding elements, regardless of whether the different models are different versions of one another, represent transformed and/or merged models, and/or were copied and subsequently modified from a source model. The techniques also advantageously leverage a more deterministic approach that is more consistent across different comparisons and when the source and target models are swapped.