Test Management System Data Set Segmentation for Vehicle Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current test management systems face challenges in efficiently managing and reproducing vehicle tests across different environments and models, leading to errors and inefficiencies due to the complexity of setting and managing data for various test scenarios.
Innovation Solution
A test management system that divides and manages data into sets for each execution mode and test environment, allowing for both actual and virtual model testing, with features like data distribution, time management, and log storage to facilitate consistent and reproducible tests across development, verification, and operation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If test data is managed without division across different execution modes and test environments, then data management is simplified, but setting errors occur and convenience of use deteriorates
Solution Approach 1:
The patent segments test data into distinct data sets based on execution modes (development, verification, operation) and test environments. Each data set contains only the data necessary for its specific purpose, preventing mixing of unrelated data and eliminating setting errors while maintaining organized management structures.
Solution Approach 2:
The patent introduces a new dimension of data organization by categorizing data sets along multiple axes: execution mode (development/verification/operation) and test environment type. This multi-dimensional classification enables precise data retrieval and prevents errors by ensuring the correct data set is used for each specific test scenario.
2Reliability
If test data is not divided into separate data sets for different execution modes and test environments, then data management is simpler, but setting errors occur and data accuracy deteriorates
Solution Approach 1:
The patent segments test data into distinct data sets based on execution modes (development, verification, operation) and test environments. Each data set contains only the data necessary for its specific purpose, preventing mixing of unrelated data and eliminating setting errors while maintaining organized management structures.
Solution Approach 2:
Each data set is configured with local quality characteristics specific to its execution mode and test environment. Development data sets contain configuration and parameter data, verification data sets contain test case and expected result data, and operation data sets contain actual test execution data. This localized data quality ensures accuracy for each specific test purpose.
3Measurement precision
If virtual models are not used for testing, then test accuracy for difficult-to-obtain models deteriorates, but system complexity and cost increase
Solution Approach 1:
The patent creates virtual model data sets that are copies or representations of actual vehicle models. These virtual models replicate the essential characteristics and behaviors of physical vehicles, enabling accurate testing of models that are difficult or expensive to obtain physically while maintaining test validity.
Solution Approach 2:
The system allows parameter changes between actual and virtual model data sets. By adjusting parameters such as model fidelity, test environment conditions, and data representation formats, the system can switch between actual vehicle testing and virtual model testing based on the specific requirements of each test scenario.
4Productivity
If tests are not reproduced using past results, then productivity improves through fresh testing, but loss of time increases due to redundant test setup
Solution Approach 1:
The patent stores test results and configuration data from past executions in structured data sets. When the same or similar tests need to be run again, the system retrieves and reuses these pre-prepared data sets, eliminating the need to recreate test configurations and significantly reducing setup time while maintaining test validity.
Data Source
AI summary
Disclosed are a system and a method for managing and performing a test using a data set. More particularly, the test management system enables data to be easily managed by generating and managing data sets for each execution mode and for each test environment.