Abnormal Air Pollution Emission Prediction Using Segmented Zone Classification
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Solution Overview
Problem
Current grid monitoring methods for air pollution emission are inaccurate in predicting abnormal emissions in a timely manner, requiring significant time and resources, and often result in false alarms or missed emissions due to real-time comparisons.
Innovation Solution
A method involving the division of areas into zones for predicting abnormal air pollution emissions, using a first set of features to determine potential emissions in a future time period and a second set of features to pinpoint the specific time of emission, employing prediction classifiers for precise forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If grid monitoring is used for air pollution emission prediction, then comprehensive coverage is achieved, but prediction accuracy for abnormal emissions deteriorates and time/resource consumption increases
Solution Approach 1:
The patent divides the monitoring area into multiple zones and further segments the prediction process into two stages: first predicting whether abnormal emission will occur in each zone, then predicting the specific time period within zones flagged as abnormal. This segmentation allows focused resources on high-risk areas while maintaining comprehensive coverage.
Solution Approach 2:
The patent performs preliminary filtering by first predicting which zones will experience abnormal emissions before conducting detailed time-period predictions. This preliminary action eliminates zones with normal emissions from further analysis, reducing computational burden and improving overall prediction efficiency.
2Reliability
If grid monitoring is used for air pollution emission prediction, then comprehensive monitoring is achieved, but time and resource consumption increases significantly
Solution Approach 1:
The patent segments the prediction process into two distinct stages: zone-level abnormal emission prediction and time-period prediction. This segmentation allows the system to quickly identify problematic zones without performing computationally intensive time-period predictions on all zones, thereby reducing overall time consumption while maintaining monitoring completeness.
Solution Approach 2:
The patent performs preliminary identification of zones with abnormal emissions before conducting detailed time-period analysis. This preliminary action filters out zones with normal emissions, preventing wasteful computation of time predictions for unaffected areas and significantly reducing total processing time.
3Productivity
If real-time comparison methods are used in grid monitoring, then emission detection is performed, but false alarms and missed emissions occur
Solution Approach 1:
The patent uses historical emission data and environmental features to predict future abnormal emissions before they occur. This preliminary prediction approach replaces reactive real-time comparison with proactive forecasting, reducing false alarms by identifying genuine abnormal patterns and preventing missed emissions through advance warning.
Solution Approach 2:
The patent transitions from single-point real-time comparison to a multi-dimensional prediction approach that incorporates historical data, environmental features, and temporal patterns. This dimensional expansion enables more accurate distinction between normal variations and genuine abnormal emissions, improving prediction accuracy.
Data Source
AI summary
A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.