Method and system for judging the possibility of landslide based on mechanical changes of time fusion

Abstract

The present invention relates to a method and system for judging the possibility of landslides based on mechanical changes integrated with time. The method includes the steps of: establishing a triangular mechanical change model according to the mountain mechanical model and the triangle method; collecting mountain data at each time monitoring point, and Delete abnormal data; use random forest to establish the relationship between the impact factor and the internal property cohesion c and internal friction angle φ; for the retained data, according to the triangular mechanical change model and the impact factor and c and φ, calculate the safety factor of the mountain, and preliminarily judge the state of the mountain; if the state of the mountain is a high-risk state, use the biological growth model to predict the landslide time of the mountain. The present invention makes full use of multi-sensor resources for time series data fusion, integrates local incomplete information obtained from multiple channels and directions according to time series, and eliminates redundancy and contradictions that may exist between multi-source information in time and space .

Description

technical field [0001] The invention relates to the technical field of geological disaster monitoring, in particular to a method and a system for judging the possibility of landslides based on time-integrated mechanical changes. Background technique [0002] With the rapid development of railways, highways and tourism, landslides are common natural disasters that threaten people's lives and property safety. Landslide monitoring and prediction has become an important research direction. [0003] The traditional monitoring directions of mountain slopes are mainly divided into surface deformation monitoring, deep deformation monitoring, related physical quantity monitoring, and related monitoring of landslide formation activities. Through the landslide prediction and analysis of the collected data, the prediction can be mainly divided into two aspects: on the one hand The spatial displacement prediction of the landslide is carried out through regression prediction, mainly using...

AI Technical Summary

Problems solved by technology

The shortcomings of monitoring can be mainly divided into three aspects: limited single variable of slope analysis, complex calculation of various internal variable relationships, and inaccurate time prediction. There are shortcomings of limited by meteorological conditions, unstable monitoring, and poor integration and coordination of monitoring technology.

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