Electronic Device Performance Monitoring With Cloud Model Feedback
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Solution Overview
Problem
Advanced electronic devices face challenges in adapting to real-world environments due to variations in acoustic configurations, requiring manual tuning and calibration, which is time-consuming and costly.
Innovation Solution
A cloud-based platform uses machine learning to generate device-specific models based on performance data, adjusting device performance by comparing manufacturer specifications with real-world measurements and feeding back updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tuning and calibration is performed for each device to ensure consistent performance, then device performance consistency is improved, but time consumption and cost increase
Solution Approach 1:
The system enables devices to automatically monitor their own performance parameters and submit data to the cloud platform without manual intervention. The devices self-report operational data including error detections, sensor measurements, and performance metrics, eliminating the need for manual tuning while maintaining performance consistency across the device fleet.
Solution Approach 2:
The cloud-based platform receives performance data from devices, analyzes it using machine learning models, and generates updated models that are fed back to devices. This continuous feedback loop automatically adjusts device performance parameters based on real-world operational data, ensuring consistency without manual calibration.
2Reliability
If manual tuning and calibration is performed for each device to ensure consistent performance, then device performance consistency is improved, but manufacturing cost increases
Solution Approach 1:
Devices automatically monitor and report their own performance parameters to the cloud platform, eliminating the need for expensive manual calibration services. The self-reporting mechanism reduces labor costs while maintaining performance consistency through automated data collection and analysis.
Solution Approach 2:
The system replaces manual mechanical calibration processes with automated electronic monitoring and cloud-based machine learning analysis. Performance data is automatically collected, analyzed, and used to generate updated device models, substituting expensive manual tuning with cost-effective automated computational processes.
3Adaptability or versatility
If device-specific information is collected and machine learning models are generated to automatically adjust performance, then adaptability to real environments is improved, but device complexity increases
Solution Approach 1:
The cloud-based platform serves as an intermediary between devices and the machine learning model generation process. Devices simply collect and submit performance data to the cloud, where complex machine learning analysis is performed. This intermediary approach enables sophisticated environmental adaptation without requiring complex processing capabilities within the devices themselves.
4Reliability
If continuous performance monitoring and model updates are implemented, then device performance in real conditions is improved, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential performance parameters and error detection data from device operations for submission to the cloud platform. By selectively collecting only the most relevant data points needed for model improvement, the system reduces overall data processing requirements while maintaining the ability to generate accurate updated device models.
Data Source
AI summary
The present invention is related to a system and corresponding method and computer implemented software for improving performance of at least one electronic devices, said devices including at least one sensors and a model defining the device reaction in response to data produced by at least one of said sensors. The system includes a model generator configured to analyze the data produced from said sensors and the reactions to the data, and register errors in the reactions as compared with the intended reactions, wherein the model generator is configured to adjust the device model by minimizing errors between the actual output of the model and the desired output of the model, based on a set of samples recorded on said at least one electronic devices, wherein the recorded samples include information related to which of a number of predefined states the device is in at the occurrence of the error, the time of the occurred error, and the number of errors at the specified time and/or state.