Hexagonal IoT Sensor Grid for Air Pollution Prediction
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Solution Overview
Problem
Current air quality monitoring systems in smart cities lack effective methods to predict and manage air quality variations across different areas, particularly in urban environments where multiple pollution sources contribute to complex air pollution scenarios.
Innovation Solution
A method and system utilizing an Internet of Things (IoT) framework that includes a sensor network platform for collecting environmental data, a regional prediction model based on machine learning to forecast air pollution, and an iterative prediction approach that considers time-series features of hexagonal areas and their adjacent regions, enabling accurate air quality determination and source identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional air quality monitoring systems are used, then basic air quality data can be collected, but prediction accuracy and source identification capability are insufficient
Solution Approach 1:
The system segments the monitoring area into multiple hexagonal grid zones, with each zone having dedicated sensors and prediction models. This segmentation allows for localized high-precision prediction while managing overall system complexity through modular architecture
Solution Approach 2:
The patent introduces a spatial dimension by dividing the area into hexagonal grids and using iterative prediction across adjacent zones. This dimensional approach transforms single-point monitoring into area-wide predictive analysis, significantly improving prediction accuracy through spatial correlation
2Measurement precision
If comprehensive environmental data from multiple sources is collected, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system merges multiple data sources (air quality sensors, weather stations, traffic data, satellite imagery) into a unified prediction framework. By combining these diverse data types through the iterative regional prediction model, the system achieves comprehensive analysis while managing complexity through integrated processing
Solution Approach 2:
The patent introduces an intermediary iterative prediction mechanism that processes data from multiple sources sequentially across hexagonal zones. This intermediary layer transforms raw multi-source data into refined predictions step-by-step, reducing processing complexity compared to simultaneous multi-source analysis
3Measurement precision
If iterative prediction across adjacent areas is implemented, then air quality prediction accuracy is enhanced, but computational time increases
Solution Approach 1:
The system performs preliminary data preparation by organizing environmental data into hexagonal grid structures and pre-processing time-series features before iterative prediction. This preliminary organization reduces computational overhead during the iterative process, mitigating time loss while maintaining accuracy
Data Source
AI summary
The present disclosure provides a method and a system for area management in a smart city based on an Internet of Things. The method includes obtaining environmental monitoring data in a target area through a sensor network platform, the environmental monitoring data including at least one of air quality data, weather data, and satellite image data, predicting an air pollution situation in the target area through a regional prediction model based on the environmental monitoring data, the regional prediction model being a machine learning model, and sending prompt information, which is determined based on the air pollution situation in the target area, to a user platform through a service platform, wherein the target area is a hexagonal area; and the regional prediction model includes seven regional models and an air quality determination model; wherein a prediction mode of the regional prediction model is an iterative prediction.