Edge Cloud AI for Micro-Earthquake Monitoring
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Solution Overview
Problem
Existing micro-earthquake monitoring devices have fixed algorithm parameters that cannot be adjusted adaptively to changing environmental conditions, leading to poor accuracy in data collection for induced earthquakes with low signal-to-noise ratios.
Innovation Solution
An artificial intelligence calculation method based on edge cloud cooperation, where a remote server receives effective event data from edge calculation devices, performs transfer training on the micro-earthquake data analyzing model, and updates the model to adapt to actual conditions, enabling flexible parameter adjustment and improved data collection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed algorithm parameters are used in micro-earthquake collecting devices, then device complexity is reduced and ease of operation is improved, but measurement precision and adaptability to different environmental conditions deteriorate
Solution Approach 1:
The patent implements dynamic parameter adjustment by enabling the algorithm parameters to be automatically optimized based on real-time environmental conditions and ground coupling characteristics. The system transitions from static fixed parameters to dynamic adaptive parameters that continuously adjust to maintain high measurement precision across different monitoring scenarios.
Solution Approach 2:
The patent applies parameter changes by modifying the algorithm parameters based on actual detection environment characteristics. The system automatically adjusts parameters such as signal-to-noise ratio thresholds and event identification criteria according to the specific geological conditions, thereby improving measurement precision without requiring manual intervention.
2Adaptability or versatility
If fixed algorithm parameters are used in micro-earthquake collecting devices, then device complexity is reduced, but adaptability to different environmental conditions and ground coupling deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically optimize its own algorithm parameters without external intervention. The micro-earthquake collecting device performs self-adjustment based on real-time environmental feedback, achieving high adaptability while maintaining relatively simple device architecture by eliminating the need for complex manual configuration systems.
3Productivity
If manual data processing and analysis is used, then device complexity is reduced, but productivity and real-time monitoring capability deteriorate
Solution Approach 1:
The patent applies mechanics substitution by replacing manual mechanical data processing operations with automated electronic computing systems. The system uses computer algorithms to automatically identify and analyze micro-earthquake events, significantly improving productivity and enabling real-time monitoring while managing device complexity through software-based solutions.
4Measurement precision
If STA/LTA long short time window ratio method is used for event identification, then device complexity is reduced and ease of operation is improved, but measurement precision for low signal-to-noise ratio events deteriorates
Solution Approach 1:
The patent applies parameter changes by transitioning from fixed STA/LTA ratio parameters to dynamically adjustable parameters that adapt to different signal-to-noise ratio conditions. The system automatically modifies identification parameters based on the characteristics of the detected signals, improving measurement precision for low signal-to-noise ratio events while maintaining manageable device complexity through automated parameter optimization.
Data Source
AI summary
An artificial intelligence calculation method and apparatus for monitoring an earthquake in real time based on edge cloud cooperation is applied to a micro-earthquake data processing system. The micro-earthquake data processing system includes an edge calculation device and a remote server in communication connection with the edge calculation device. The remote server deploys a micro-earthquake data analyzing model based on an artificial intelligence to the edge calculation device in advance. The method includes steps of receiving, by the remote server, effective event data related to the micro-earthquake from the edge calculation device; performing a transfer training to the micro-earthquake data analyzing model by the remote server according to the effective event data; and updating the model after the micro-earthquake data analyzing model that has been transfer-trained is transmitted to the edge calculation device by the remote server.