Multi-Configuration Massive Model System for Vehicle Parts
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Solution Overview
Problem
The existing methods for manufacturing vehicles using massive model datasets are resource-intensive, requiring significant processing time, bandwidth, and storage space due to the need for separate datasets for each configuration, which increases exponentially with the number of configurations.
Innovation Solution
A multi-configuration massive model system that compares sets of parts across configurations to identify common and unique parts, allowing for a single massive model dataset to represent multiple configurations, reducing the need for separate datasets and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate massive model datasets are created for each configuration, then the display accuracy and completeness of each configuration is improved, but the processing time, bandwidth, and storage requirements increase exponentially
Solution Approach 1:
The patent merges multiple configuration-specific massive model datasets into a single unified dataset that contains all parts across all configurations. This consolidation eliminates the need to process and transmit separate datasets for each configuration, thereby reducing processing time and bandwidth requirements while maintaining complete display accuracy for any selected configuration.
Solution Approach 2:
The unified massive model dataset serves multiple functions simultaneously: it stores parts for all configurations in a single dataset, enables rapid switching between configurations through filtering, and provides complete information for any configuration without requiring separate dataset generation. This multi-functional approach resolves the contradiction between display accuracy and processing efficiency.
2Loss of information
If separate massive model datasets are created for each configuration, then the completeness of part information for each configuration is improved, but the storage space requirements increase
Solution Approach 1:
The patent combines multiple configuration-specific datasets into one unified dataset that stores all parts from all configurations. This merging eliminates redundant storage of common parts across configurations while maintaining complete part information for each configuration through selective filtering, thereby significantly reducing total storage space requirements.
Solution Approach 2:
Instead of creating separate physical copies of datasets for each configuration, the system creates a single unified copy that contains all parts. Configuration-specific views are generated through software filtering and selection rather than physical duplication, reducing storage space while preserving information completeness.
3Measurement precision
If separate massive model datasets are created for each configuration, then the accuracy of configuration-specific display is improved, but the bandwidth needed to transmit datasets increases
Solution Approach 1:
The patent merges multiple configuration datasets into a single unified dataset that is transmitted once over the network. This eliminates the need to transmit separate datasets for each configuration, thereby reducing bandwidth consumption and energy expenditure while maintaining the ability to display any configuration with full accuracy through client-side filtering.
Data Source
AI summary
A multi-configuration massive model system. The system comprises a processor unit and a comparator configured to run on the processor unit, a memory, and a configuration manager. The comparator compares sets of parts for two or more configurations of a vehicle to form a list comprising a group of common parts and a group of unique parts. The memory is configured to store a massive model dataset of the configurations of the vehicle with a list of the group of common parts and the group of unique parts for the configurations of the vehicle. The configuration manager, configured to run on the processor unit, receives input of a selected configuration and performs an action relating to the vehicle using the massive model dataset for the selected configuration of the vehicle with the list of the group of common parts and the groups of unique parts stored in the memory.