A prediction method, device and equipment for geological disasters

Abstract

The invention discloses a geological disaster prediction method, device and equipment. The prediction method includes: obtaining monitoring data of a monitoring area, wherein the monitoring data includes direct impact factors and indirect impact factors; the direct impact factors and The indirect influence factors are subjected to feature extraction to obtain a direct feature matrix and an indirect feature matrix; the direct feature matrix and the indirect feature matrix are encoded and decoded based on a deep learning network model containing an attention mechanism, so as to obtain the direct feature matrix and the indirect feature matrix according to the deep learning network model The output of is to predict the geological hazards in the monitoring area. In the technical solution of the embodiment of the present invention, the monitoring data is divided into two groups, and combined with the deep learning network model including the attention mechanism to process the data to predict geological disasters, realizing short-term prediction of geological disasters with high prediction accuracy.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of data processing, and in particular, to a method, device and equipment for predicting geological disasters. Background technique [0002] China is a country prone to geological disasters. Collapses, landslides, and debris flows are almost everywhere in the mountainous and hilly areas of every province in the country, and tens of thousands to hundreds of thousands of new disaster spots appear every year. Nearly 1,000 people are killed by geological disasters every year, and the direct economic loss is 8-10 billion yuan. The indirect loss caused by interruption of traffic and destruction of production and living facilities is even more difficult to estimate. [0003] At this stage, many scholars have done a lot of research on landslide displacement prediction. From the specific time and accuracy, landslide displacement prediction can be divided into: long-term prediction (more than 1-10...

AI Technical Summary

Problems solved by technology

This is because in the short-term prediction, the sensor is affected by external factors (such as temperature), which causes the hourly monitoring value to fluctuate violently, and there is a lot of noise interference. Therefore, it is difficult to obtain its deformation trend and give real-time and accurate prediction. bring considerable difficulty

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