Air Quality Prediction Model Using Virtual Sensor Data
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Solution Overview
Problem
Existing air quality prediction models struggle with accurate fine-grained predictions due to limited air quality monitoring stations, relying on coarse-grained predictions and lacking sufficient pollution source information.
Innovation Solution
A training method for an air quality prediction model that divides a target monitoring range into measurement and prediction regions, using pre-training and formal training with specific samples and objective functions to predict air quality based on spatial, historical, and environmental information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more air quality monitoring stations are deployed to cover every region, then prediction accuracy in fine-grained regions is improved, but construction and maintenance costs increase
Solution Approach 1:
The patent uses a prediction model to generate virtual air quality measurement data for regions without physical monitoring stations. The model copies and infers air quality information from nearby measured regions to create synthetic data, effectively replacing the need for physical monitoring infrastructure in those areas while maintaining prediction accuracy.
Solution Approach 2:
The prediction model acts as an intermediary between physical monitoring stations and regions without monitoring infrastructure. It processes data from available monitoring stations and translates it into predicted air quality values for target regions, bridging the gap between limited measurement data and comprehensive coverage requirements.
2Device complexity
If air quality prediction is performed at a coarse-grained level, then the prediction process is simpler, but the accuracy and usefulness of the prediction decreases
Solution Approach 1:
The patent divides the air quality prediction task into segmented components: region division into measurement and prediction regions, multi-stage training process (pre-training and formal training), and separate processing of different data types (spatial, temporal, environmental). This segmentation allows complex fine-grained predictions to be achieved through manageable, systematic steps rather than a single complex process.
Solution Approach 2:
The patent implements pre-training of the prediction model using synthetic data and pre-training objective functions before formal training with real data. This preliminary action prepares the model to handle fine-grained predictions by first learning patterns from simplified representations, reducing the complexity of the formal training process while maintaining high accuracy.
3Productivity
If a pre-trained model is used for formal training, then training efficiency is improved, but the model requires additional training data and computational resources
Solution Approach 1:
The patent performs pre-training of the air quality prediction model before formal training. During pre-training, the model learns fundamental patterns from synthetic data and simplified representations. This preliminary action establishes a solid foundation that accelerates formal training convergence and reduces the computational resources needed for fine-tuning with real data.
Solution Approach 2:
The patent changes the training parameters and objective functions between pre-training and formal training stages. Pre-training uses synthetic data with specific objective functions optimized for learning general patterns, while formal training uses real data with different objective functions optimized for accuracy. This parameter change allows efficient use of computational resources by adapting the training process to the specific requirements of each stage.
Data Source
AI summary
Provided are a training method for an air quality prediction model, a prediction method and apparatus, a device, a program, and a medium. The method includes the steps described below. A target monitoring range is divided into a plurality of regions; the air quality prediction model is pre-trained by adopting a pre-training sample and a pre-training objective function, where the pre-training sample includes measurement values; and the pre-trained air quality prediction model is trained by adopting a formal training sample and a formal training objective function, where the formal training sample includes the measurement values. The air quality prediction model is configured to predict air quality of the plurality of regions according to spatial information, historical information and environmental information.