Predicting method, device and equipment of geological hazard

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, high prediction accuracy and strong robustness.

Active Publication Date: 2020-02-21
杭州鲁尔物联科技有限公司
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  • Abstract
  • Description
  • Claims
  • 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,

Method used

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  • Predicting method, device and equipment of geological hazard
  • Predicting method, device and equipment of geological hazard
  • Predicting method, device and equipment of geological hazard

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Experimental program
Comparison scheme
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Embodiment 1

[0025] figure 1 It is a flow chart of a method for predicting geological disasters provided by Embodiment 1 of the present invention. This embodiment is applicable 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 direct impact factors and indirect impact factors.

[0027] Wherein, the monitoring area may be an area prone to geological disasters 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 geological dynamic activities or abnormal changes in the g...

Embodiment 2

[0044] figure 2 It is a flow chart of a geological disaster prediction method provided by Embodiment 2 of the present invention. This embodiment is a further refinement and supplement to the previous embodiment. The geological disaster prediction method provided by the embodiment of the present invention also includes: Perform outlier removal on the monitoring data and normalize the direct feature matrix and the indirect feature matrix.

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

[0046] Step 210, acquiring monitoring data of the monitoring area, wherein the monitoring data includes direct impact factors and indirect impact factors.

[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) ] means the direct impact factor, [X (4) ,…X (n) ] indicates an indirect impact factor.

[0048] Step 220, removin...

Embodiment 3

[0097] image 3 It is a schematic diagram of a geological disaster prediction device provided in Embodiment 3 of the present invention, 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] Wherein, the monitoring data acquisition module 310 is used to obtain the monitoring data of the monitoring area, wherein the monitoring data includes direct impact factors and indirect impact factors; the feature extraction module 320 is used to perform the direct impact factors and indirect impact 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 the deep learning network model containing attention mechanism, to obtain the direct feature matrix and indirect feature matrix according to the depth The output of the learning...

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Abstract

The invention discloses a predicting method, device and equipment of a geological hazard. The predicting method comprises the steps of: acquiring monitoring data, which comprises the direct impact factor and the indirect impact factor, of a monitored area, extracting features of the direct impact factor and the indirect impact factor so as to acquire a direct feature matrix and an indirect featurematrix, and, encoding and decoding the direct feature matrix and the indirect feature matrix based on a deep learning network model containing an attention mechanism, so as to predict the geologicalhazard of the monitored area according to the output of the deep learning network model. The method provided by the technical scheme of the embodiment of the invention divides the monitoring data intotwo groups and combines the deep learning network model containing the attention mechanism to process the data, so as to predict the geological hazard; and thus, the short-term prediction of the geological hazard is realized, and the prediction precision is high.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of data processing, and in particular to a geological disaster prediction method, device and equipment. Background technique [0002] China is a country prone to geological disasters. Collapses, landslides, and mud-rock flows are almost all over the mountainous and hilly areas of every province in the country, and tens of thousands to hundreds of thousands of new disaster points will appear every year. Nearly a thousand people die in geological disasters every year, and the direct economic loss is 8 billion to 10 billion yuan. The indirect loss caused by interrupting traffic and destroying production and living facilities is even more difficult to estimate. [0003] At this stage, many scholars have conducted a lot of research on landslide displacement prediction. From the specific time and accuracy, landslide displacement prediction can be divided into: long-term prediction (1-10 y...

Claims

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Application Information

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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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