A prediction method, device and equipment for geological disasters

A technology for geological disasters and prediction methods, applied in the field of data processing, can solve the problems of noise interference, violent fluctuation of monitoring values, difficulty in real-time and accurate prediction, etc., and achieve the effects of high speed, strong robustness and high prediction accuracy.

Active Publication Date: 2022-06-24
杭州鲁尔物联科技有限公司
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  • Application Information

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

Method used

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  • A prediction method, device and equipment for geological disasters
  • A prediction method, device and equipment for geological disasters
  • A prediction method, device and equipment for geological disasters

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Embodiment 1

[0025] figure 1 It is a flow chart of a method for predicting geological disasters provided in the first embodiment of the present invention. This embodiment can be applied to the situation of predicting geological disasters. The method can be executed by a geological disaster prediction device. The device can use software and implemented in hardware, such as figure 1 As shown, the method specifically includes the following steps:

[0026] Step 110 , acquiring monitoring data of the monitoring area, wherein the monitoring data includes a direct impact factor and an indirect impact factor.

[0027] The monitoring area may be a geological disaster-prone area or a set area, and may include one or more target areas or monitoring points. Geological disasters include landslides, collapses, soil erosion, salinization, land subsidence, earthquakes, debris flows, etc., and refer to natural disasters that are mainly caused by geodynamic activities or abnormal changes in the geological...

Embodiment 2

[0044] figure 2 This is a flowchart of a method for predicting geological disasters provided in the second embodiment of the present invention. This embodiment is a further refinement and supplement to the previous embodiment. The method for predicting geological disasters provided by the embodiment of the present invention further includes: Perform outlier removal on the monitoring data and normalize the direct feature matrix and the indirect feature matrix.

[0045] like figure 2 As shown, the method includes the following steps:

[0046] Step 210: Acquire monitoring data of the monitoring area, wherein the monitoring data includes a direct impact factor and an indirect impact factor.

[0047] Exemplarily, it is assumed that the monitoring data is: X=[X (1) , X (2) , X (3) …X (n) ], where X (c) , c=1, 2, 3...n, is an m-dimensional column vector, [X (1) , X (2) , X (3) ] represents the direct impact factor, [X (4) ,...X (n) ] indicates indirect influence factors...

Embodiment 3

[0097] image 3 is a schematic diagram of a geological disaster prediction device provided in the third embodiment of the present invention, such as image 3 As shown, the device includes: a monitoring data acquisition module 310 , a feature extraction module 320 and a disaster prediction module 330 .

[0098] Among them, the monitoring data acquisition module 310 is used to acquire monitoring data of the monitoring area, wherein the monitoring data includes direct influence factors and indirect influence factors; the feature extraction module 320 is used to perform the analysis on the direct influence factors and indirect influence factors. feature extraction to obtain direct feature matrix and indirect feature matrix; disaster prediction module 330 for encoding and decoding the direct feature matrix and indirect feature matrix based on a deep learning network model including an attention mechanism to The output of the learning network model predicts the geological hazard in...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01N33/24G06K9/62G06N3/04G06N3/08
CPCG01N33/24G06N3/08G06N3/045G06F18/23
Inventor 郑增荣董梅宋杰胡辉
Owner 杭州鲁尔物联科技有限公司
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