Intelligent Landslide Deformation Monitoring via Sub-Area Segmentation
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Solution Overview
Problem
Existing deformation monitoring systems for mountain landslides lack accuracy and timeliness in detecting slope changes, leading to ineffective preventive measures and increased landslide impacts.
Innovation Solution
A data analysis-based intelligent deformation monitoring system that classifies mountainous areas into sub-regions based on composition and characteristics, employing specific detection sensors and methods for each type, enabling targeted and efficient monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a unified monitoring method is used for the entire mountainous area, then the system complexity is reduced, but the measurement precision and timeliness of deformation monitoring deteriorate
Solution Approach 1:
The patent divides the mountainous area into multiple sub-areas based on geological characteristics, soil types, and landslide risks. Each sub-area is assigned specific monitoring methods and sensors tailored to its characteristics, thereby improving measurement precision without excessively increasing overall system complexity through modular deployment
Solution Approach 2:
Different monitoring methods and sensor types are selected for different sub-areas based on their specific geological and environmental characteristics. This localized approach ensures that each area is monitored with the most appropriate technology, maximizing measurement precision while avoiding the need to deploy all possible monitoring methods across the entire region
2Measurement precision
If comprehensive monitoring methods are applied to all sub-areas, then the measurement precision is improved, but the device complexity and cost increase
Solution Approach 1:
The monitoring system is segmented into different modules suitable for different sub-area types. Each module contains the specific sensors and methods appropriate for that sub-area category, allowing comprehensive monitoring where needed while simplifying the overall system architecture through modular design
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as sensor selection, sampling frequency, and threshold values based on the specific characteristics of each sub-area. This allows comprehensive monitoring to be applied selectively rather than uniformly, improving precision where necessary while controlling system complexity and cost
3Loss of time
If real-time monitoring is implemented across the entire mountainous area, then the timeliness of risk detection is improved, but the loss of energy and cost increase
Solution Approach 1:
The mountainous area is divided into sub-areas with different risk levels. Real-time monitoring is concentrated in high-risk sub-areas where timely detection is critical, while lower-risk areas use less frequent or passive monitoring methods, thereby maintaining timeliness for critical risks while reducing overall energy consumption and cost
Solution Approach 2:
The system implements periodic monitoring with varying frequencies based on risk levels and environmental conditions. High-risk areas experience continuous or frequent monitoring, while low-risk areas are monitored periodically, optimizing the balance between timeliness and resource consumption
Data Source
AI summary
Disclosed is a data analysis-based intelligent deformation monitoring system for mountain landslides, which solves the technical problem in the prior art that when a landslide occurs in a landslide area, it is not possible to implement region-specific monitoring for landslide soil, thus failing to minimize the real-time impact of the landslide area. The present disclosure involves marking a sub-area where a landslide occurs as a landslide area, marking a real-time sliding direction within the landslide area as a landslide flow direction, setting areas at both sides of the landslide flow direction as landslide slopes, generating a control adjustment signal or a risk monitoring signal through landslide slope analysis, and sending the signal to a server.