Temporal Vehicle Model Datasets Using Change Lists for 3D Visualization
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Solution Overview
Problem
Current systems for managing large three-dimensional model datasets of vehicles, such as commercial airplanes, require significant resources for storage and bandwidth due to the need to maintain multiple snapshots of configurations over time, and lack efficient methods to track and visualize design changes across different points in time.
Innovation Solution
A temporal multi-configuration model dataset system that compares prior and current parts lists to detect changes, updates the model dataset by adding models for parts added and not removing models for parts removed, and generates visualizations for selected points in time using change lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple complete snapshots of model datasets are stored for different configurations and time points, then visualization of vehicle states at any point in time is enabled, but storage requirements and processing resources increase significantly
Solution Approach 1:
The patent segments the model dataset into a base configuration dataset and multiple configuration difference datasets. Each configuration difference dataset contains only the changes (added, removed, or modified parts) relative to the base configuration, rather than storing complete snapshots. This segmentation enables efficient storage while maintaining the ability to reconstruct any historical configuration by applying the appropriate difference datasets to the base dataset.
Solution Approach 2:
The patent extracts only the essential change information from complete configuration snapshots. Instead of storing entire model datasets for each configuration, the system extracts and stores only the differences (additions, removals, modifications) as configuration difference datasets. This extraction approach significantly reduces storage requirements while preserving the ability to accurately reconstruct and visualize any historical vehicle state.
2Reliability
If complete model datasets are transmitted over the network for each requested configuration, then accurate visualization is provided, but network bandwidth consumption increases
Solution Approach 1:
The system extracts and transmits only the configuration difference data rather than complete model datasets. When a user requests visualization of a specific configuration, the system transmits the base configuration dataset along with only the relevant configuration difference datasets needed to reconstruct that specific configuration state. This dramatically reduces network bandwidth consumption while maintaining visualization accuracy.
3Loss of information
If multiple complete configuration datasets are maintained, then comprehensive tracking of design changes is enabled, but processing time and computational resources increase
Solution Approach 1:
The patent segments change tracking into a hierarchical structure with a base configuration and layered configuration difference datasets. Each difference dataset captures changes at a specific configuration level, allowing the system to track design changes comprehensively while processing only the necessary difference datasets rather than comparing complete configurations. This segmentation reduces computational overhead for change tracking.
Solution Approach 2:
The system performs preliminary organization of configuration data into difference datasets during data ingestion, rather than performing complete dataset comparisons when visualization is requested. By pre-processing and structuring the configuration differences in advance, the system enables rapid retrieval and application of changes when users request historical configurations, significantly reducing processing time.
4Manufacturing precision
If the model dataset is updated by removing models for removed parts, then accuracy of current configuration is maintained, but ability to visualize historical configurations is degraded
Solution Approach 1:
The patent segments the configuration data into a persistent base dataset and time-stamped configuration difference datasets. When parts are removed from the current configuration, the system records this removal in the configuration difference dataset rather than permanently deleting the model from the base dataset. This segmentation allows the system to maintain accurate current configurations while preserving the ability to reconstruct historical configurations by applying the appropriate sequence of difference datasets to the base dataset.
Data Source
AI summary
A temporal multi-configuration model dataset system comprises a computer system configured to: compare a prior parts list for a vehicle at a point in time to a current parts list in which comparing the prior parts list with the current parts list results in a comparison; determine change lists for the parts that changed using the comparison; append models to a model dataset for the vehicle in response to parts added to the vehicle, wherein models are not removed from the model dataset in response to parts being removed from the vehicle; determine display parts in the model dataset present for a selected point in time in response to receiving a request to visualize the vehicle at the selected point in time in which the display parts are determined using the set of change lists; and display a visualization of the vehicle using the display parts on a display system.