Geological disaster monitoring and early warning method based on artificial intelligence

By introducing horizontal and vertical convolution kernels into the geological disaster detection network of the convolutional neural network and building the first loss function, the problem of insufficient ability to describe timing changes characteristics is solved, and the accuracy of landslide detection is improved.

CN119763303AActive Publication Date: 2025-04-04WUHAN ZHONGDI YUNSHEN TECH CO LTD
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Patent Information

Application Number
CN202510266808.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-07
Publication Date
2025-04-04
Estimated Expiration
2045-03-07

AI Technical Summary

Technical Problem

The convolutional neural network has poor ability to describe the timing change characteristics of image information, resulting in relatively low detection accuracy in landslide detection.

Method used

A geological disaster detection network based on convolutional neural network is designed. By introducing lateral convolution kernels and longitudinal convolution kernels into each convolution layer, and building a first loss function to control the timing change characteristics of the network learning mountain image information.

Benefits of technology

By adjusting the structure of the convolutional neural network, it can learn the timing variation characteristics of image information more effectively, thereby improving the accuracy of the geological disaster detection network for landslide monitoring.

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Abstract

The present invention relates to the field of geological disaster monitoring, and in particular to a geological disaster monitoring and early warning method based on artificial intelligence. The method comprises the steps of: obtaining a historical mountain video; obtaining a pre-constructed geological disaster detection network, wherein each convolution layer in the geological disaster detection network comprises a plurality of convolution kernel groups, and each convolution kernel group comprises a horizontal convolution kernel and a vertical convolution kernel; inputting a preset number of frames of historical mountain images in the historical mountain video into the geological disaster detection network, performing convolution processing using the horizontal convolution kernel to obtain a historical mountain image after horizontal convolution, and performing convolution processing using the vertical convolution kernel on all the historical mountain images after horizontal convolution obtained in the convolution kernel group to obtain a historical mountain image after vertical convolution; constructing a first loss function; and performing network training control using the first loss function to realize geological disaster monitoring. Accurate monitoring of geological disasters can be achieved by adjusting the network structure and the loss function.
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