Edge Device Parameter Sampling Configuration via Cloud Model
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Solution Overview
Problem
Current data sampling methods for edge devices are inefficient, as they often require universal settings that are not scenario-specific, leading to excessive data collection or insufficient data, and updates are difficult to implement, causing resource wastage and accuracy issues.
Innovation Solution
An intelligent system and method for configuring parameter sampling, which includes an information acquisition unit, a sending unit, and a configuration information determining unit, connected to a cloud platform using a configuration model to recommend optimal sampling parameters and frequencies based on the edge device's usage and environment, avoiding excessive data collection while ensuring accurate device characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency data sampling is used to describe device characteristics more accurately, then measurement precision is improved, but network resource consumption increases
Solution Approach 1:
The patent dynamically adjusts sampling parameters (sampling rate, sampling interval) based on device characteristics, usage scenarios, and data importance levels. Instead of using fixed high-frequency sampling for all parameters, the system changes sampling parameters adaptively to match actual needs, reducing unnecessary data transmission while maintaining accurate characterization of critical device states.
2Ease of operation
If default data collection configuration is used, then ease of operation is improved, but adaptability deteriorates
Solution Approach 1:
The patent transforms static default configurations into dynamic, adaptive configurations. The system automatically adjusts data collection parameters based on real-time device states, usage scenarios, and environmental conditions. This dynamic approach maintains ease of operation (no manual configuration needed) while achieving scenario-specific adaptability through automated parameter optimization.
Solution Approach 2:
The system enables edge devices to self-configure their data collection parameters autonomously. Each device evaluates its own characteristics, usage context, and data importance levels to automatically determine optimal sampling rates and intervals, eliminating the need for manual configuration while achieving scenario-specific optimization.
3Device complexity
If sampling parameters are fixed, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent implements dynamic sampling parameter adjustment mechanisms that automatically adapt to different device types, usage scenarios, and data importance levels. The system maintains low operational complexity for users while achieving high adaptability through automated parameter optimization based on real-time conditions and pre-defined optimization models.
Data Source
AI summary
The present disclosure relates to a method, device, and system for configuring parameters, a computer device, a medium, and a product. A configuration device for configuring parameter sampling with respect to an edge device includes: one information acquiring unit, configured to acquire information related to the purpose and use environment of the edge device; one transmitting unit, configured to transmit the information to a cloud platform; and one configuration information determining unit, configured to receive configuration information for parameter sampling with respect to the edge device from the cloud platform, where the configuration information is configuration information determined as matching the information by the cloud platform utilizing a configuration model stored thereby.