Edge Analytics Architecture for Low-Latency Industrial Control
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Solution Overview
Problem
Current industrial systems lack the capability to generate predictive and prescriptive actions in real-time, especially in settings with limited network connectivity and handling noisy sensor data.
Innovation Solution
A distributed analytics system with an edge processing device that performs predictive and prescriptive analytics independently, using an architect subsystem to deploy analytic models and modify them based on system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If centralized data analysis approach is used, then data processing capability is improved, but latency between sensor data collection and prescription of action increases
Solution Approach 1:
The patent segments the centralized analytics system into distributed edge analytics nodes deployed throughout the industrial system. Each edge node processes data locally, eliminating the single-point bottleneck of centralized processing while maintaining parallel processing capabilities across multiple nodes, thus reducing latency without sacrificing processing power.
Solution Approach 2:
The patent introduces a spatial dimension to data processing by distributing analytics nodes across the physical layout of the industrial system. Instead of all data traveling to a central location, processing occurs at multiple locations simultaneously, adding a spatial distribution dimension that reduces transmission latency while maintaining aggregate processing capability.
2Loss of information
If network connectivity is assumed available, then data transmission capability is improved, but system reliability in limited connectivity settings deteriorates
Solution Approach 1:
The patent enables edge analytics nodes to operate autonomously with local data storage and processing capabilities. Each node can independently perform analytics and generate prescriptions without requiring continuous network connectivity, making the system self-sufficient in limited connectivity environments while maintaining data transmission capability when connectivity is available.
Solution Approach 2:
The patent implements adaptive parameters that allow the system to dynamically adjust between centralized and decentralized operation modes based on network connectivity conditions. When connectivity is limited, the system transitions to autonomous edge operation; when connectivity is available, it leverages cloud-based resources, thus maintaining reliability across varying connectivity scenarios.
3Adaptability or versatility
If big data analysis is performed at data center level, then analytical capability is improved, but deployment complexity and distance from real-time operation increases
Solution Approach 1:
The patent segments the big data analytics platform into modular edge nodes that can be independently deployed throughout the industrial system. Each node contains essential analytics capabilities locally, eliminating the need for complex centralized data center deployment while maintaining advanced analytical capability through distributed intelligence.
4Measurement precision
If more sensors are installed, then data quality and insight capability are improved, but system complexity and cost increases
Solution Approach 1:
The patent extracts and prioritizes only the critical sensors and data elements necessary for specific analytics objectives at each edge node. Rather than installing and processing data from all available sensors system-wide, each edge node selectively processes relevant data locally, improving measurement precision for critical parameters while reducing overall system complexity.
Data Source
AI summary
A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.