Central Server Aggregates Edge Data for Dynamic Instruction Updates
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Solution Overview
Problem
Edge devices face challenges in adapting to changing environments and applications due to stale local instructions, and existing solutions for updating these instructions are either infeasible for remote locations or require excessive manual effort, especially when edge devices are intermittently connected.
Innovation Solution
A system that uses a central server to aggregate data from multiple edge devices, generate new or modified instructions, and iteratively update local instructions at each edge device, leveraging machine learning algorithms to improve data intake and query processes, allowing edge devices to adapt to changing conditions without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If edge devices store and analyze all raw data, then analysis flexibility and completeness improve, but computational load and storage requirements exceed edge device capabilities
Solution Approach 1:
The patent divides the data processing system into edge devices that collect and pre-process data locally, and a centralized server that performs complex analysis. This segmentation allows edge devices to maintain simplicity while the system as a whole achieves high analysis flexibility through the centralized server's capability to analyze all stored raw data.
Solution Approach 2:
The patent introduces a centralized server as an intermediary between edge devices and the analysis process. The server receives raw data from multiple edge devices, stores it, and performs comprehensive analysis, thereby enabling analysis flexibility without increasing the computational complexity of individual edge devices.
2Manufacturing precision
If local instructions are updated manually at remote edge devices, then instruction accuracy improves, but the effort and time required become excessive
Solution Approach 1:
The patent implements an automated feedback mechanism where the centralized server generates updated instructions based on analyzed data and automatically pushes these instructions to edge devices. This feedback loop ensures instruction accuracy is maintained through data-driven updates while eliminating manual intervention, thereby reducing operational effort.
Solution Approach 2:
The patent enables edge devices to automatically receive and apply updated instructions from the centralized server without requiring manual intervention. This self-service mechanism ensures that instructions remain accurate and up-to-date while significantly reducing the effort required for updates, especially for remotely located devices.
3Device complexity
If edge devices operate with stale local instructions, then device simplicity is maintained, but adaptability to changing environments deteriorates
Solution Approach 1:
The patent makes the instruction set dynamic by enabling automatic updates from the centralized server to edge devices. Instructions evolve based on changing environmental conditions and data patterns, allowing edge devices to maintain simplicity while adapting to new conditions through periodically updated instructions pushed by the server.
4Quantity of substance
If data is pre-processed at edge devices, then data volume for storage is reduced, but analysis flexibility is limited to predetermined needs
Solution Approach 1:
The patent adds a centralized server dimension to the data processing architecture. While edge devices perform minimal pre-processing to reduce data volume, the centralized server provides another dimension of processing capability where comprehensive analysis of all raw data can be performed, thereby maintaining analysis flexibility without requiring edge devices to handle large data volumes.
Data Source
AI summary
Disclosed is a technique that can be performed by a server computer system. The technique can include obtaining data from each of multiple endpoint devices to form global data. The global data can be generated by the endpoint devices in accordance with local instructions in each of the endpoint devices. The technique further includes generating global instructions based on the global data and sending the global instructions to a particular endpoint device. The global instructions configure the particular endpoint device to perform a data analytic operation that analyzes events. The events can include raw data generated by a sensor of the particular endpoint device.