Tropical Instability Wave Early Warning via Temporal-Spatial Attention Fusion
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Solution Overview
Problem
Traditional numerical simulation methods for predicting tropical instability waves are complex and difficult to implement accurately, lacking efficiency in predicting sea surface temperatures and timely warnings for regions vulnerable to these waves.
Innovation Solution
A tropical instability wave early warning method based on temporal-spatial cross-scale attention fusion, using convolutional and deconvolutional networks to generate multi-scale spatial data, and a bilateral local attention mechanism for cross-scale spatial map fusion, optimizing neural networks for efficient prediction and early warning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional numerical simulation methods based on physical equations are used to predict tropical instability waves, then the model can consider complex processes such as ocean dynamics and atmosphere-ocean interaction, but the implementation becomes very difficult and accuracy is hard to achieve
Solution Approach 1:
The patent replaces traditional numerical simulation methods based on physical equations with a deep learning-based neural network model. This substitution transitions from a physics-equation-driven mechanical system to a data-driven intelligent system that automatically learns complex patterns from historical sea surface temperature data, thereby reducing implementation difficulty while maintaining prediction accuracy for tropical instability waves
Solution Approach 2:
The patent changes the fundamental parameters of the prediction system by transitioning from explicit physical equation parameters to learned neural network parameters. The model learns optimal parameters automatically from data through training, replacing the need for manual specification of complex physical parameters and processes, thus simplifying implementation while capturing complex ocean-atmosphere interactions
2Productivity
If deep learning models are applied to oceanography fields, then prediction accuracy and timeliness can be improved, but the application is still in its infancy and requires pertinant design
Solution Approach 1:
The patent segments the deep learning model into distinct functional components: convolutional layers for feature extraction, pooling layers for dimensionality reduction, and attention mechanisms for focusing on critical regions. This segmentation makes the complex network design more manageable and implementable while maintaining high prediction timeliness for tropical instability wave forecasting
Solution Approach 2:
The patent introduces attention mechanisms as intermediary components that mediate between raw sea surface temperature data and final predictions. These attention mechanisms selectively highlight important spatial and temporal features, simplifying the overall network design by automatically identifying critical patterns without requiring manual feature engineering
3Measurement precision
If convolutional neural networks are used to extract features from sea surface temperature data, then the encoding capacity for spatial information can be improved, but more data processing and computation are required
Solution Approach 1:
The patent applies preliminary action by using pooling layers to reduce data dimensionality before feeding into deeper network layers. This pre-processing step compresses spatial information while retaining essential features, thereby improving spatial encoding capacity in subsequent layers while reducing computational energy consumption in the overall model
Data Source
AI summary
The present disclosure discloses a tropical instability wave early warning method based on temporal-spatial cross-scale attention fusion, including performing cross-scale spatial map fusion on the multi-scale feature maps by a bilateral local attention mechanism, calculating a prediction loss by the global feature description map, and combining the prediction loss and the regularization loss for optimization training of neural networks; predicting a sea surface temperature at a moment T based on the optimally trained neural networks, selecting data at K moments before the moment T and inputting the data into the optimally trained neural networks, outputting a predicted value of tropical instability waves by the optimally trained neural networks, and drawing a temporal-spatial image of the tropical instability waves by associating the predicted value with coordinates, so as to achieve early warning of the tropical instability waves. The device includes a processor and a memory.