Operating Condition Analysis Using Selective Parameter Retrieval
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Solution Overview
Problem
In the racing car industry, existing analysis systems face challenges in reducing data traffic between the car and the analysis system while effectively detecting and correcting inferior operating conditions, leading to increased demands for data processing that can hinder the racing event.
Innovation Solution
An analysis system that uses a model with processing logic to retrieve and evaluate only a subset of operation parameters, determining correlated operating conditions with high probability, and optionally retrieves additional parameters to ensure unambiguous classification, thereby reducing data transmission and enabling countermeasures for abnormal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all operation parameters are transmitted and evaluated between the racing car and the analysis system, then the detection accuracy of inferior operating conditions is improved, but the data traffic volume increases significantly
Solution Approach 1:
The patent extracts and transmits only the most relevant operation parameters needed for detecting inferior operating conditions, rather than transmitting all available parameters. The analysis system selectively retrieves specific parameters (e.g., engine temperature, RPM, fuel injection rate) that are most indicative of abnormal conditions, thereby reducing data traffic while maintaining detection accuracy.
Solution Approach 2:
The patent segments the operation parameters into different categories based on their relevance to detecting inferior operating conditions. Critical parameters that directly indicate abnormal conditions are transmitted with higher priority and frequency, while less critical parameters are transmitted selectively or with lower frequency, optimizing the balance between detection accuracy and data traffic volume.
2Productivity
If a model with pre-stored correlations is used to evaluate only a subset of parameters, then the data processing load is reduced, but the complexity of the analysis system increases
Solution Approach 1:
The patent implements preliminary action by pre-storing correlation models and relationships between operation parameters in the analysis system before actual analysis begins. These pre-computed models (including normal operating ranges, correlation matrices, and decision trees) enable the system to quickly evaluate incoming parameter subsets without performing complex real-time calculations, thus reducing processing load during actual operation.
Solution Approach 2:
The patent introduces an intermediary model layer that sits between the raw parameter data and the final detection output. This model acts as a mediator that translates subsets of operation parameters into meaningful assessments of operating conditions, simplifying the overall system architecture by separating data acquisition, model evaluation, and decision-making functions.
3Measurement precision
If additional operation parameters are retrieved to clarify ambiguous operating conditions, then the classification accuracy is improved, but the data traffic volume increases
Solution Approach 1:
The patent implements dynamic parameter retrieval where the set of transmitted operation parameters is not fixed but adapts based on the current analysis needs. When the model identifies ambiguous or uncertain operating conditions based on initially received parameters, the system dynamically requests additional specific parameters to resolve the ambiguity, rather than continuously transmitting all parameters. This dynamic approach maintains high classification accuracy while minimizing overall data traffic.
Solution Approach 2:
The patent employs feedback mechanisms where the analysis system evaluates the initial parameter set, determines whether the operating condition can be confidently classified, and if not, requests additional parameters. This feedback loop ensures that additional data is transmitted only when necessary to resolve specific ambiguities, optimizing the balance between classification accuracy and data traffic volume.
Data Source
AI summary
A system for controlling an operating condition of an operating system includes a data collector, controller and processing logic comprising a model of the operating system including sets of operation parameters and correlated operating conditions. Values of a portion of the operation parameters are retrieved and form an initial parameter set which is compared with correlated operating conditions. The processing logic determines a correlated operating condition with a corresponding probability of coincidence and for a matching operating condition with the highest probability of coincidence checks whether other matching operating conditions with a similar probability of coincidence exist. If another matching operating condition exists, an additional operation parameter with a similar probability of coincidence is obtained and the process is repeated from the comparison step until no other matching operating conditions with a similar probability of coincidence exist, at which point the matching operating condition is transmitted to the controller.