Animal Sensor Data Analysis for AI Earthquake Prediction
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Solution Overview
Problem
Conventional technologies have not sufficiently utilized animal behavioral data for earthquake prediction, leaving room for improvement.
Innovation Solution
A system that collects data from sensors attached to animals, analyzes it using machine learning algorithms and generative AI, and generates prediction information to provide advance warning of earthquakes through regional disaster prevention systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If animal behavioral data is collected and analyzed using conventional methods, then earthquake prediction capability is improved, but data processing efficiency and accuracy are insufficient
Solution Approach 1:
The patent replaces conventional mechanical data processing methods with generative AI and large language models. The system uses AI to automatically analyze animal sensor data, extract behavioral patterns, and generate prediction information, substituting manual or rule-based analysis with intelligent algorithms that process data more efficiently and accurately.
Solution Approach 2:
The patent transforms animal sensor data into standardized digital formats suitable for AI processing. By converting diverse sensor inputs (acceleration, heart rate, temperature) into uniform data structures and features that large language models can process, the system enables efficient analysis while maintaining the integrity of the original behavioral information.
2Measurement precision
If animal sensor data is digitized and processed through AI systems, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer consisting of data processing modules and feature extraction components that bridge raw sensor data and the generative AI model. This intermediary structure organizes and pre-processes data before AI analysis, reducing the complexity burden on the core prediction system while maintaining high measurement precision.
Solution Approach 2:
The system divides the earthquake prediction process into distinct functional modules: sensor data collection, data digitization, feature extraction, AI analysis, and prediction output. This segmentation allows each component to be optimized independently, managing overall system complexity while achieving high prediction precision through specialized processing at each stage.
Data Source
AI summary
The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data from sensors attached to animals. The analysis unit analyzes the data collected by the collection unit. The generation unit generates prediction information based on the analysis results obtained by the analysis unit. The provision unit provides the prediction information generated by the generation unit.