Algorithm Version-Compatible Test Data Determination
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Solution Overview
Problem
Existing methods for virtual testing and simulation of vehicle devices and functions are limited, as they typically support only one specific version of the algorithm or software release, leading to misconfigurations and incorrect test executions, which waste computing resources and incur unnecessary costs.
Innovation Solution
A computer-implemented method and system that allows for error-free execution of virtual tests and simulations by selecting and providing configuration-compatible test and simulation data sets for different algorithm versions, enabling the use of existing data sets with a current algorithm version and avoiding migration efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single instance of an algorithm is used to execute virtual tests and simulations, then resource usage is optimized and cost-effectiveness is improved, but compatibility across multiple algorithm versions becomes difficult to achieve
Solution Approach 1:
The algorithm instance is designed to handle multiple algorithm versions through version identification and compatibility checking mechanisms. The system can execute test and simulation data created with different algorithm versions using a single universal instance, making the system multi-functional across version boundaries.
Solution Approach 2:
The system changes the version parameter to identify and handle different algorithm versions. By detecting the version parameter in test and simulation data, the system can adjust its execution behavior accordingly, allowing a single instance to adapt to multiple version parameters without requiring separate instances for each version.
2Adaptability or versatility
If test and simulation data are created with different algorithm versions, then versatility and adaptability are improved, but misconfigurations and incorrect test executions increase
Solution Approach 1:
The system implements feedback through version identification and compatibility checking. Before executing test or simulation data, the system detects the algorithm version embedded in the data and verifies compatibility with the current algorithm instance. This feedback mechanism prevents misconfigurations and incorrect executions by ensuring version match between data and execution environment.
Solution Approach 2:
The system performs preliminary version detection and compatibility verification before executing test or simulation data. This preliminary action identifies potential misconfigurations in advance, allowing the system to prevent incorrect test executions before they occur, thereby maintaining reliability while supporting multiple algorithm versions.
3Manufacturing precision
If separate algorithm instances are used for different algorithm versions, then version-specific accuracy is maintained, but computing time and resources are wasted
Solution Approach 1:
Instead of maintaining separate algorithm instances for each version, the system uses a single universal instance that can handle multiple versions through version detection and compatibility checking. This eliminates the need to run multiple separate instances, saving computing time and resources while maintaining version-specific test accuracy through proper version identification.
Solution Approach 2:
The system merges the functionality of multiple version-specific algorithm instances into a single unified instance. By combining version detection, compatibility checking, and execution capabilities into one instance, the system achieves the same version-specific accuracy as separate instances would provide, but without the overhead of maintaining and executing multiple separate instances.
4Adaptability or versatility
If migration efforts are required to ensure compatibility with different algorithm versions, then version compatibility is achieved, but integration efforts and costs increase
Solution Approach 1:
The system performs self-service through automatic version detection and compatibility verification. Instead of requiring manual migration efforts to ensure compatibility with different algorithm versions, the system automatically identifies the version in test and simulation data and verifies compatibility with the current algorithm instance. This self-service approach achieves version compatibility without requiring complex integration or migration efforts.
Solution Approach 2:
The system performs preliminary version detection and compatibility checking before execution, eliminating the need for subsequent migration efforts. By identifying the algorithm version upfront and verifying compatibility, the system prevents the need for complex integration work later in the process, reducing overall integration complexity and costs.
Data Source
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AI summary
The invention relates to a computer-implemented method and system for determining test and/or simulation data (24a, 24b, 24c, 24d; 124a, 124b, 124c, 124d) that are compatible with a specific version of an algorithm (A) performing the virtual test and/or simulation, in particular test and/or simulation configuration, and comprising determining (S2) a data set (D1, D2) of test and/or simulation data (24a, 24b, 24c, 24d) supporting a compatibility requirement (14a, 14b) of the first configuration (10) and/or the second configuration (12).124a, 124b, 124c, 124d), and output (S3) the specified dataset (D1, D2) of test and/or simulation data (24a, 24b, 24c, 24d; 124a, 124b, 124c, 124d) supporting the compatibility requirement (14a, 14b) of the first configuration (10) and/or the second configuration (12).