High-universality landslide displacement rate early warning method

A displacement rate and displacement deformation rate technology, applied in data processing applications, instruments, biological neural network models, etc., can solve the problems of high complexity of deep learning models, long training and prediction time, and high misjudgment rate, to prevent training. Insufficient data, strong generality, and accurate prediction effect

Active Publication Date: 2022-05-03
CHENGDU UNIVERSITY OF TECHNOLOGY +1
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AI Technical Summary

Benefits of technology

This patented technology uses deep learning techniques called convolutional networks (CNN) to improve image processing capabilities by making better predictions on landslide history compared to previous methods like regression analysis or machine vision algorithms. These improvements make them faster at identifying changes caused by tectonic activity without requiring extensive human labor. Additionally, these systems have been developed over many years ago and provide improved functionality even after being updated frequently due to advancements such as software updates or hardware upgrades. Overall, this innovation allows us to accurately identify potential hazards during leaks and take appropriate actions based upon their severity level.

Problems solved by technology

This patented technical problem addressed in this patents relates to accurately detecting when potholes happen during forest fires due to factors like environmental conditions (temperature) changes over long periods of time without requiring too much computing resources. Deep Learning models can be trained with enough data from different parts of the world that makes them highly accurate even if only part needs to receive feedback about their performance.

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  • High-universality landslide displacement rate early warning method
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  • High-universality landslide displacement rate early warning method

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Embodiment 1

[0025] The specific steps of the high generality landslide displacement early warning rate method in the present embodiment are as follows:

[0026] S100: Collect historical landslide data in different landslide areas before and after the landslide, including the displacement of the landslide mass, displacement deformation rate and time of occurrence of the landslide, and preprocess the historical landslide data.

[0027] According to the time series curve analysis of the acceleration failure stage before the landslide instability at home and abroad, it can be seen that the landslide presents four different motion forms after entering the acceleration stage: linear, wave, gradual and instantaneous. The historical landslide data collected in this step include the landslide data before and after the landslide in the landslide area under 4 different movement forms. The preprocessing in the present invention includes: filtering the displacement amount and displacement deformation rat

Embodiment 2

[0038] In this embodiment, the early warning in step S400 includes the prediction of the displacement deformation rate of the landslide-prone point and the classification of the landslide stage.

[0039]For landslides, the displacement deformation rate itself has a nonlinear relationship with time, but in most cases, the reciprocal of the velocity has a certain linear relationship with time, so when the neural network predicts the reciprocal of the displacement deformation rate, the displacement deformation rate It is easier to find the correlation by making a coordinate diagram with time and combining it with the landslide stage classification diagram.

[0040] In order to further illustrate the advantages of using the reciprocal of the speed compared with the direct use of speed, this embodiment uses the relevant monitoring data of a landslide-prone site in Chongqing for illustration. figure 2 The reciprocal of the displacement deformation rate-time difference diagram shown is

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Abstract

The invention discloses a high-universality landslide displacement rate early warning method, which comprises the following steps: S100, collecting historical landslide data of different landslide regions before and after landslide, and preprocessing the historical landslide data; s200, training a large neural network model by adopting historical landslide data, and performing knowledge distillation on the trained large neural network model to obtain a small neural network model; s300, dividing the landslide risk area into a plurality of easy landslide points, collecting historical landslide data of each easy landslide point where landslide has occurred, preprocessing the historical landslide data, and training and finely tuning the small neural network model by using the historical landslide data to obtain a small neural network model corresponding to the landslide risk area; and S400, preprocessing the real-time landslide data of each easy landslide point in the landslide risk area, and inputting the preprocessed data into the small neural network model for early warning. According to the method, the accuracy is improved while the early warning complexity is reduced, and the universality is extremely high.

Description

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Claims

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Application Information

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Owner CHENGDU UNIVERSITY OF TECHNOLOGY
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