Cognitive Pattern Templates for Engine Mix Settings
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Solution Overview
Problem
Current methods for configuring engine mix settings in high-performance race cars are laborious, time-consuming, and fail to identify hidden patterns in large data sets, leading to suboptimal performance and inefficient use of settings that could significantly impact lap times and race results.
Innovation Solution
A system utilizing cognitive analysis and pattern templates to analyze racing scenarios, associate specific vehicle settings with optimal conditions, and generate optimized engine mix settings through a guided interaction with a cognitive system, allowing for the composition and publication of reusable automotive settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods (spreadsheets, analysis software) are used to create mix settings maps, then teams can analyze racing data, but the process becomes laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis methods (spreadsheets, manual software operations) with an automated cognitive system that uses machine learning and pattern recognition algorithms to automatically analyze racing data and generate mix settings maps, eliminating manual labor while maintaining or improving analysis quality
Solution Approach 2:
The system enables self-service automation where the cognitive system independently performs data analysis, pattern recognition, and settings map generation without requiring manual intervention at each step, allowing teams to automatically create optimized settings maps from raw racing data
2Loss of information
If comprehensive data analysis is performed on large data sets (terabytes per season), then hidden patterns can be identified, but current methods fail to efficiently process and utilize this data
Solution Approach 1:
The patent replaces manual data analysis methods with cognitive computing systems that use machine learning algorithms to automatically process and analyze large datasets, enabling efficient detection of hidden patterns in terabytes of racing data that would be impossible to analyze manually
Solution Approach 2:
The system creates reusable mix settings maps that can be copied and applied across multiple races and conditions, allowing teams to leverage patterns identified in one dataset to optimize performance in various racing scenarios without reanalyzing all data each time
3Adaptability or versatility
If limited number of settings are programmed into engine management systems before qualifying, then race rules are satisfied, but the driver has insufficient options to optimize for specific situations and conditions
Solution Approach 1:
The patent segments the mix settings map into multiple distinct settings optimized for specific racing conditions (e.g., different track sections, weather conditions, tire states), allowing the driver to select appropriate settings for each situation while maintaining compliance with race rules through structured organization
Solution Approach 2:
The system creates dynamic mix settings maps that can be adjusted based on real-time conditions, allowing the driver to optimize settings during the race based on actual track conditions, tire wear, and weather changes while maintaining compliance with pre-programmed requirements
Data Source
AI summary
Race car settings (e.g., Formula 1 engine mix settings) are developed for particular racing goals such as faster lap time, better acceleration, less vehicle wear, etc., using pattern templates that are derived from historical racing scenarios. The historical scenarios provide data on racing settings, racing results, and racing conditions such as squad information, equipment information, and environmental information. A cognitive (deep question answering) system can select an initial pattern template based on current racing conditions, and present suggested vehicle settings to the user (driver) using the initial pattern template. The driver can select from different candidate values for various factors, which may lead to the presentation of additional suggestions or the use of additional pattern templates. The final settings map is created based on the employed pattern templates and the driver selections.