一种边坡多参数融合监测与数据分析系统
By dividing the slope into segments based on slope height and monitoring multiple parameters, combined with edge preprocessing and cloud computing, the problems of resource imbalance, inaccurate data fusion, and prediction lag in existing slope monitoring systems have been solved, enabling accurate monitoring and early warning of slope risks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUIZHOU TRANSPORTATION PLANNING SURVEY & DESIGN ACADEME
- Filing Date
- 2026-01-21
- Publication Date
- 2026-07-17
AI Technical Summary
Existing slope monitoring systems fail to effectively consider slope height characteristics, resulting in an imbalance in the allocation of monitoring resources, inaccurate fusion of multi-source data, a lack of short-term local prediction capabilities in prediction models, and a lag in risk assessment, making it difficult to achieve early identification, early warning, and early response.
The slope is divided into slope height segmented units, and a multi-parameter sensor group is used for monitoring. Through edge preprocessing and cloud computing, combined with the limit equilibrium method, slope height correction factor and data-driven correction, regional instability indicators are generated. Moran's index and long and short-term neural networks are used to divide the synergistic impact units and predict the short-term instability probability. The risk is displayed on a slope height layered GIS map.
It has enabled the refined allocation of monitoring resources, improved the efficiency of multi-source data utilization and the accuracy of prediction, solved the problems of unbalanced allocation of monitoring resources, extensive data fusion and lagging prediction, and enhanced the intuitiveness of risk display and decision support.
Smart Images
Figure CN121579929B_ABST