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Rainfall type landslide displacement trend prediction method based on monitoring data time sequence diagram self-learning

A technology for monitoring data and trend prediction, applied in the field of geospatial data processing, to achieve the effect of improving predictability

Active Publication Date: 2021-05-14
浙江中海达空间信息技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, what time series feeds back is constantly changing dynamic information. How to express this dynamic information intuitively becomes the difficulty of applying this method to landslide monitoring data learning.

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  • Rainfall type landslide displacement trend prediction method based on monitoring data time sequence diagram self-learning
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  • Rainfall type landslide displacement trend prediction method based on monitoring data time sequence diagram self-learning

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[0045] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0046] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0047] Below in conjunction with accompanying drawing and embodiment the present invention will be further descr...

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Abstract

The invention relates to a rainfall type landslide displacement trend prediction method based on monitoring data time sequence diagram self-learning. The rainfall type landslide displacement trend prediction method comprises the following steps: 1, performing multi-factor associated time sequence waveform analysis; 2, constructing a sequence diagram matched with waveform features; 3, establishing a self-supervised representation learning model of the time sequence diagram; and 4, performing rainfall type landslide displacement tendency prediction. According to the deep trend prediction analysis method provided by the invention, the change characteristics of monitoring data in time sequence are fully utilized, and an interpretable evolution graph model is provided, so that the problems that landslide related factors are difficult to select and effective information is difficult to obtain due to a complex movement mechanism of rainfall-type landslide are avoided; the landslide displacement in the future time can be scientifically predicted, and a support is provided for improving the predictability of rainfall type landslide risk judgment.

Description

technical field [0001] The invention belongs to the technical field of geospatial data processing, and in particular relates to a method for predicting the displacement trend of rainfall-type landslides based on the self-learning of monitoring data sequence diagrams. Background technique [0002] Due to the widespread, lossy, and threatening effects of landslide hazards, effective and rapid identification of them has become an urgent problem to be solved today. Regional rainfall-type landslides are one of the main types of geological disasters in my country, which have the characteristics of mass, simultaneous, explosive and large-scale disasters. According to statistics, most of the major landslides in Southwest China in the past 40 years were caused by rainfall, which is the main inducing factor for the instability of such landslides. Landslide deformation monitoring data is important information for early warning of various landslide risks. However, due to the characteri...

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Application Information

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IPC IPC(8): G06F30/27G06N3/08G06Q10/04
CPCG06N3/08G06Q10/04G06F30/27
Inventor 谢潇伍庭晨张叶廷鄂超刘铭崴
Owner 浙江中海达空间信息技术有限公司