Signal Data Reference Model Adaptation for Vehicle Bus Systems
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Solution Overview
Problem
Existing methods for processing data associated with signals transmitted via bus systems, such as those in vehicles, struggle to efficiently provide and adapt reference data for statistical models, especially under changing environmental conditions.
Innovation Solution
A method that intermittently provides and modifies reference data for statistical models by using dynamic averages based on unweighted and weighted values, allowing for efficient online adaptation during operation, and includes the calculation of standard deviations to characterize signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reference data is provided intermittently and modified dynamically during operation, then adaptability to changing environmental conditions is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic adaptation of reference data by continuously modifying statistical model parameters (mean and standard deviation) based on weighted averages of signal characteristics. The system adjusts reference values in real-time according to changing environmental conditions while maintaining computational efficiency through incremental updates rather than complete reprocessing.
Solution Approach 2:
The patent changes statistical parameters (mean and standard deviation) of the reference data dynamically. By updating these parameters based on weighted averages of current signal characteristics, the system adapts to environmental changes without requiring complete data reprocessing, thus managing complexity through parameter transformation rather than system reconfiguration.
2Measurement precision
If dynamic averages are calculated over predefined numbers of values, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary calculation of weighted averages over predefined numbers of signal values to establish reference data before actual processing begins. By pre-computing these statistical parameters, the system achieves high measurement precision while minimizing real-time processing requirements, as the reference framework is already in place for immediate use.
Solution Approach 2:
The patent maintains continuous calculation of dynamic averages as signal values become available, rather than performing batch processing. This continuous approach allows the system to accumulate precise statistical data in the background while providing immediate reference values for signal processing, effectively eliminating the trade-off between precision and processing time.
3Reliability
If statistical models are updated continuously, then reliability of signal processing is improved, but use of energy increases
Solution Approach 1:
The patent applies partial updates to the statistical model rather than complete reprocessing. By updating only the necessary parameters (mean and standard deviation) based on new signal data, the system maintains high reliability through continuous adaptation while consuming significantly less energy than would be required for complete model regeneration.
Solution Approach 2:
The patent implements feedback mechanisms where current signal characteristics continuously refine the reference data through weighted average calculations. This feedback loop allows the statistical model to self-correct and adapt to changing conditions with minimal computational overhead, maintaining reliability without excessive energy consumption through efficient iterative updates.
Data Source
AI summary
A computer-implemented method for processing data which are associated for example with a signal transmittable and/or transmitted via a bus system, for example of a vehicle, including: at least intermittent provision of reference data for a statistical model which characterizes at least one average of at least one characteristic of the signal on the basis of a first average determined, for example dynamically, over a predefinable unweighted first number of values for the characteristic, and at least intermittent modification of the reference data at least in part on the basis of a second average determined, for example dynamically, over a predefinable weighted second number of values for the characteristic.