Hot-plugging Edge Computing Architecture for Energy Stations
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Solution Overview
Problem
Comprehensive energy power stations require advanced computational capabilities to manage and coordinate multiple forms of energy, predict system behavior, and optimize resource utilization, but existing technologies lack efficient edge computing solutions for on-site data processing.
Innovation Solution
A hot-plugging edge computing system is introduced, comprising an elastic database, a data processing module, and hot-plugging computing modules. This system enables rapid data processing and analysis by dynamically assigning tasks to computing modules based on status information and task attributes, utilizing a model-free distributed data processing architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If traditional centralized computing architecture is used for energy storage power stations, then system stability is maintained, but computational power and data processing efficiency are insufficient
Solution Approach 1:
The patent divides the centralized computing system into multiple independent edge computing terminals that can be distributed across different locations within the energy storage power station. Each terminal processes local data independently, eliminating the single-point bottleneck of centralized computing while maintaining system stability through modular architecture.
Solution Approach 2:
The patent transitions from a single-dimensional centralized computing model to a multi-dimensional distributed edge computing architecture. By adding spatial distribution as a new dimension, the system achieves both increased computational power through parallel processing and maintained stability through decentralized operation.
2Productivity
If edge computing terminals are added to increase computational power, then data processing efficiency improves, but system complexity and power consumption increase
Solution Approach 1:
The patent implements a dynamic task allocation mechanism where edge computing terminals are activated only when computational tasks exceed the processing capacity of the central controller. This partial activation approach ensures that additional computing power is utilized only when necessary, optimizing the balance between processing efficiency and power consumption.
Solution Approach 2:
The system incorporates real-time monitoring of computational load and power consumption metrics. Based on feedback from these measurements, the central controller dynamically adjusts the activation state of edge computing terminals, ensuring optimal power efficiency while maintaining required data processing throughput.
3Speed
If multiple edge computing terminals are deployed for parallel processing, then computing speed increases, but system reliability and ease of maintenance deteriorate
Solution Approach 1:
The patent designs edge computing terminals with standardized interfaces and unified communication protocols, making them universally interchangeable. This universality ensures that any terminal can replace another without affecting system reliability, and simplifies maintenance by allowing identical replacement units to be deployed quickly.
Solution Approach 2:
The system implements a hot-swappable architecture where faulty edge computing terminals can be quickly removed and replaced with standby units without shutting down the entire system. The replaced terminals can then be recovered, repaired, and returned to service, maintaining continuous operational reliability while enabling parallel processing for high computing speed.
4Adaptability or versatility
If dynamic task allocation is implemented among computing modules, then resource utilization improves, but system complexity increases
Solution Approach 1:
The patent implements a self-service task allocation mechanism where edge computing terminals automatically report their available computational resources and current task status to the central controller. The system uses simple rule-based algorithms to automatically distribute tasks based on current system state, achieving high resource utilization flexibility without requiring complex manual task management or sophisticated scheduling software.
Data Source
AI summary
A hot-plugging edge computing system includes at least an elastic database, a data processing module, and hot-plugging computing modules. The elastic database is configured to store data through an SSD array with a configurable number of SSDs. The data processing module is connected to the elastic database and each of the hot-plugging computing modules, and controls data storage in the elastic database, monitors and assigns a task to each of the hot-plugging computing modules. Each of the hot-plugging computing modules performs data computing based on the corresponding task assigned by the data processing module, and returns a computing result to the corresponding data processing module.