一种基于智慧视觉与多源数据融合的空气监测站非现场监管方法及系统
By integrating intelligent vision with multi-source data, and combining physical diffusion models and dynamic visual sampling, the problems of fragmented multimodal data and misaligned evidence collection time at air monitoring stations have been solved. This has enabled precise, efficient, and low-cost off-site monitoring of air monitoring stations and provided a high-confidence chain of evidence.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HENAN PROVINCIAL ECOLOGICAL ENVIRONMENT MONITORING & SAFETY CENT
- Filing Date
- 2026-04-09
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies in air quality monitoring stations suffer from problems such as fragmented multimodal data, misaligned evidence collection time, and limited computing power at the edge, making off-site supervision difficult.
By using a method based on intelligent vision and multi-source data fusion, the differential sequence of pollutant concentration time series data and base station data is used, combined with a physical diffusion model to calculate the delay time, and the sampling method of the visual neural network is dynamically adjusted to achieve cross-modal causal determination of data and video features.
It enables precise, efficient, and low-cost off-site monitoring of air monitoring stations, providing a high-confidence closed-loop evidence chain from behavior to result, and reducing the computing resource consumption of edge computing devices.
Smart Images
Figure CN121999442B_ABST