Simulation Data Signature Analysis for Deviation Detection
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Solution Overview
Problem
Manual comparison of simulation data between current and prior runs is tedious and time-consuming, and conventional applications often generate false positives due to pre-programmed results that require frequent updates.
Innovation Solution
A method and system that create data extract files, generate unique signatures for each file, and compare consolidated signatures between simulation runs to automatically identify deviations, using hashing algorithms to determine if the current run deviates from the prior run.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of simulation data is performed, then accuracy of deviation detection is improved, but time consumption increases
Solution Approach 1:
The patent extracts only the critical information from simulation logs by generating data extract files that contain only the necessary data points for comparison. This extraction is performed automatically and efficiently, reducing the amount of data that needs to be manually reviewed while maintaining accuracy in deviation detection.
Solution Approach 2:
The patent creates a simplified representation of simulation data through data extract files and signatures. Instead of manually comparing entire log files, the system compares extracted key data points and their corresponding signatures, which provides a fast and accurate method for deviation detection without reviewing all原始 data.
2Productivity
If conventional applications with pre-programmed results are used, then initial analysis speed is improved, but reliability decreases due to false positives
Solution Approach 1:
The patent implements a dynamic comparison system that automatically adapts to changes in test environments and simulation data. Instead of relying on static pre-programmed results, the system dynamically generates data extracts and compares them against current simulation runs, allowing it to accurately identify deviations even when test conditions change.
Solution Approach 2:
The system provides feedback by comparing current simulation data with previous runs and automatically identifying deviations. This feedback mechanism allows the system to learn from previous results and adapt to changing conditions, eliminating false positives while maintaining fast analysis speeds through automated comparison.
3Reliability
If conventional applications are updated to keep up with test environment changes, then reliability is improved, but time consumption increases
Solution Approach 1:
The patent implements a self-service system that automatically adapts to test environment changes without requiring manual updates to conventional applications. The system automatically generates data extracts, creates signatures, and performs comparisons, allowing it to self-update and adapt to new test conditions independently, thus maintaining reliability without the time cost of manual reprogramming.
4Loss of information
If manual review of simulation logs is performed, then completeness of analysis is improved, but productivity decreases
Solution Approach 1:
The patent extracts only the essential information from simulation logs into data extract files, which contain the critical data points needed for deviation detection. This extraction process automatically identifies and isolates relevant information, ensuring complete analysis of important elements while dramatically improving productivity by eliminating the need to manually review entire log files.
Data Source
AI summary
A method for analysing simulation data is disclosed. In some embodiments, the method includes creating a set of data extract files from simulation logs associated with a current simulation run. The method further includes generating a unique signature for each of the set of data extract files. The method further includes creating a signature log file comprising the unique signature generated for each of the set of data extract files. The method further includes generating a consolidated signature for the signature log file. The method further includes comparing the consolidated signature with a prior consolidated signature generated for a prior simulation run. The method further includes determining whether the current simulation run deviates from the prior simulation run, based on the comparison between the consolidated signature and the prior consolidated signature.