Graph Neural Network for Spatio-Temporal Perception Prediction
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Solution Overview
Problem
Current prediction methods for spatio-temporal perception data, such as earthquakes and traffic flow, often ignore spatial correlations, leading to inaccurate predictions as they primarily focus on temporal features, neglecting the importance of spatial correlations in perception data.
Innovation Solution
A method and system utilizing a graph neural network to convert perception data into spatio-temporal graph data, incorporating both temporal and spatial features by constructing a perception data monitoring network, preprocessing data into a matrix sequence, and training a graph neural network model to predict future events, sending early warnings when predicted values exceed thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning or deep learning models (such as SVM or RNN) are used to predict perception data, then temporal correlation can be explored, but spatial correlation information is ignored, leading to insufficient prediction accuracy
Solution Approach 1:
The patent transitions from traditional 1D temporal sequence modeling to 2D spatio-temporal graph modeling by introducing spatial dimension. Perception data is represented as graph nodes with spatial relationships, allowing the model to simultaneously capture both temporal evolution and spatial correlations through graph neural network operations that propagate information across spatial nodes while respecting temporal sequences.
Solution Approach 2:
The patent combines multiple modeling approaches into a composite spatio-temporal graph neural network model. It integrates graph neural network operations for spatial relationship modeling with temporal sequence processing capabilities, creating a hybrid architecture that simultaneously handles both spatial and temporal correlations in perception data, rather than relying on a single modeling paradigm.
2Measurement precision
If perception data is processed using simple temporal models, then the model complexity remains low, but the ability to capture spatial relationships and improve prediction accuracy is limited
Solution Approach 1:
The patent segments the complex spatio-temporal modeling task into distinct functional components: graph construction module for spatial relationship establishment, graph neural network module for spatial feature extraction, and temporal sequence processing module for time-dependent pattern recognition. This segmentation allows each component to specialize in one aspect while working together to achieve comprehensive spatio-temporal prediction.
Solution Approach 2:
The graph neural network model serves multiple functions simultaneously: it captures spatial correlations through graph convolution operations, processes temporal sequences through recurrent connections, and performs prediction through the integrated architecture. This multi-functionality reduces the need for separate specialized models for spatial and temporal processing.
Data Source
AI summary
Disclosed are a method and system for predicting spatio-temporal perception information based on a graph neural network. The method includes the following steps: step S1: constructing a perception data monitoring network, and acquiring original perception data through data acquisition nodes in the perception data monitoring network; step S2: pre-processing the original perception data and converting the same into spatio-temporal graph perception data; step S3: constructing a graph neural network model, and training parameters of the graph neural network model by using the spatio-temporal graph perception data; and step S4: inputting given spatio-temporal graph perception data to the trained graph neural network model and outputting a predicted value, and sending early warning information when the predicted value exceeds a preset threshold.