一种空气质量监测站点的聚类方法及相关设备
By calculating the spatiotemporal consistency and spatial correlation values of air quality monitoring stations, a partitioning objective function is constructed for iterative updates, which solves the problem of low clustering accuracy of air quality monitoring stations in existing technologies and achieves more accurate and stable station partitioning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 湖南工商大学
- Filing Date
- 2026-04-30
- Publication Date
- 2026-07-17
AI Technical Summary
Existing clustering methods for air quality monitoring stations are insufficient in distinguishing between spatially similar but temporally different or spatially distant but temporally consistent sites. They are also susceptible to noise and anomalies, resulting in poor zoning stability and low accuracy.
By calculating the spatiotemporal consistency value and spatial correlation value among air quality monitoring stations, a membership matrix is constructed. The membership matrix is then iteratively updated using a partitioning objective function to obtain the final membership matrix for clustering.
It improves the accuracy of air quality monitoring station clustering, reflects the dynamic correlation of pollution processes, enhances the representativeness and stability of zoning results, and reduces the phenomenon of underrepresentation or overrepresentation of groups.
Smart Images
Figure CN122153507B_ABST