Edge Device Analytics for Intelligent Data Throttling
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Solution Overview
Problem
Edge devices face challenges in managing and analyzing large volumes of raw data due to limited storage and computational capabilities, and existing solutions require manual effort and timely connectivity for updating instructions, which is inefficient and impractical for distributed networks.
Innovation Solution
A system where a central server aggregates data from edge devices, generates and updates global instructions using machine learning algorithms, and communicates these to edge devices for improving data intake and query processes, enabling iterative updates and efficient data processing even when devices are disconnected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If edge devices store and analyze massive quantities of raw data, then data analysis flexibility and completeness are improved, but computational intensity and storage requirements exceed edge device capabilities
Solution Approach 1:
The patent segments the data processing system into edge devices that collect and pre-process data locally, and a centralized server that performs comprehensive analysis. This division allows edge devices to maintain simplicity while the system as a whole achieves high analytical flexibility through the centralized server's processing of aggregated raw data from multiple sources.
Solution Approach 2:
The patent introduces a centralized server as an intermediary between edge devices and final data analysis. The server aggregates raw data from multiple edge devices, performs computationally intensive processing, and returns refined results or updated instructions to edge devices, thereby enabling flexible analysis without overburdening individual edge devices.
2Quantity of substance
If edge devices pre-process edge data to reduce volume, then data transmission and storage requirements are reduced, but analysis flexibility is limited to anticipated needs only
Solution Approach 1:
The patent applies preliminary action by having edge devices perform initial data collection and basic filtering before transmission. However, the system enhances this by allowing the centralized server to perform additional pre-processing and aggregation on the transmitted data, enabling both data volume reduction and maintained analysis flexibility through multi-stage processing.
Solution Approach 2:
The patent adds another dimension to data processing by implementing a hierarchical architecture where edge devices handle local pre-processing and a centralized server performs global processing. This multi-dimensional approach allows data to be processed at different levels of abstraction, maintaining flexibility while managing volume through distributed processing.
3Productivity
If analytics tools are implemented at edge devices, then local data processing capability is improved, but the tools fail to benefit from interconnectedness with other devices
Solution Approach 1:
The patent merges local edge device analytics capabilities with centralized server processing. Edge devices perform local analysis to improve immediate responsiveness, while simultaneously transmitting data to the centralized server that aggregates information from multiple devices. This combination allows each device to benefit from both local processing speed and global data patterns from the network.
Solution Approach 2:
The patent implements feedback mechanisms where the centralized server analyzes aggregated data from multiple edge devices and sends updated instructions, models, or parameters back to individual devices. This feedback loop enables edge devices to improve their local processing capabilities based on collective network learning, thereby utilizing interconnectedness to enhance individual productivity.
4Device complexity
If manual updates are used to refresh instructions on edge devices, then implementation simplicity is maintained, but time consumption and operational inefficiency increase
Solution Approach 1:
The patent implements self-service by enabling edge devices to automatically receive and apply updated instructions, data models, or configuration parameters from the centralized server without requiring manual intervention. The system autonomously manages instruction distribution and device updates, reducing both implementation complexity and update time through automated workflows.
Data Source
AI summary
The computerized method is shown and includes obtaining input at a local electronic device, generating a first result by processing the input according to a first rule set, wherein the first result provides a current status or a predicted status of the electronic device and transmitting the first result or indicia thereof to a remote server computer system. The computerized method additionally includes receiving a communication from the remote server computer system, wherein the communication includes an instruction to enable a second rule set, responsive to receiving the communication, enabling the second rule set, obtaining subsequent input at the local electronic device, and processing the subsequent input according to the second rule set. The computerized method may further include performing a statistical analysis on the input, and generating a predicated status of the electronic device based on an extrapolation process using a result of the statistical analysis.