Mountain torrent early warning method and system based on cross-source interpretable deep learning model

The invention relates to the technical field of mountain torrent early warning, and particularly discloses a mountain torrent early warning method and system based on a cross-source interpretability deep learning model, and the method comprises the steps: obtaining mountain torrent parameters and en...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: YAO YANQIU, XING PENGFEI, LI QINGLIANG, GENG QINGTIAN, YAO YIFEI, YU FANHUA
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator YAO YANQIU
XING PENGFEI
LI QINGLIANG
GENG QINGTIAN
YAO YIFEI
YU FANHUA
description The invention relates to the technical field of mountain torrent early warning, and particularly discloses a mountain torrent early warning method and system based on a cross-source interpretability deep learning model, and the method comprises the steps: obtaining mountain torrent parameters and environment parameters in an early warning region, and calculating the correlation degree of the mountain torrent parameters and the environment parameters; environment parameters are selected and combined according to the correlation degree, and corresponding mountain torrent parameters are connected to serve as samples of the early warning area; counting samples of different early warning areas, training a neural network model, and synchronously constructing an interpretation content library; and when the neural network model is applied, determining supplementary content of an output result according to the explanation content library. According to the method, other possible inputs of the same output can be obtaine
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118334830A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118334830A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118334830A3</originalsourceid><addsrcrecordid>eNqNzDEKwkAQRuE0FqLeYTxAwLAWaSUoNlrZh0321wQ2M8vOBMntbbS3es3HWxfDTWY2PzKZ5Aw2gs9xobfPPPKLJtgggTwH0kUNE3VeEUiY-iyqpcqce9DIhpwyzHcRFIBEEb-HBMRtsXr6qNh9uyn2l_OjuZZI0kKT78GwtrlXVe3csXaHk_vHfABbaUDm</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Mountain torrent early warning method and system based on cross-source interpretable deep learning model</title><source>esp@cenet</source><creator>YAO YANQIU ; XING PENGFEI ; LI QINGLIANG ; GENG QINGTIAN ; YAO YIFEI ; YU FANHUA</creator><creatorcontrib>YAO YANQIU ; XING PENGFEI ; LI QINGLIANG ; GENG QINGTIAN ; YAO YIFEI ; YU FANHUA</creatorcontrib><description>The invention relates to the technical field of mountain torrent early warning, and particularly discloses a mountain torrent early warning method and system based on a cross-source interpretability deep learning model, and the method comprises the steps: obtaining mountain torrent parameters and environment parameters in an early warning region, and calculating the correlation degree of the mountain torrent parameters and the environment parameters; environment parameters are selected and combined according to the correlation degree, and corresponding mountain torrent parameters are connected to serve as samples of the early warning area; counting samples of different early warning areas, training a neural network model, and synchronously constructing an interpretation content library; and when the neural network model is applied, determining supplementary content of an output result according to the explanation content library. According to the method, other possible inputs of the same output can be obtaine</description><language>chi ; eng</language><subject>ALARM SYSTEMS ; CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; ORDER TELEGRAPHS ; PHYSICS ; SIGNALLING ; SIGNALLING OR CALLING SYSTEMS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240712&amp;DB=EPODOC&amp;CC=CN&amp;NR=118334830A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240712&amp;DB=EPODOC&amp;CC=CN&amp;NR=118334830A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>YAO YANQIU</creatorcontrib><creatorcontrib>XING PENGFEI</creatorcontrib><creatorcontrib>LI QINGLIANG</creatorcontrib><creatorcontrib>GENG QINGTIAN</creatorcontrib><creatorcontrib>YAO YIFEI</creatorcontrib><creatorcontrib>YU FANHUA</creatorcontrib><title>Mountain torrent early warning method and system based on cross-source interpretable deep learning model</title><description>The invention relates to the technical field of mountain torrent early warning, and particularly discloses a mountain torrent early warning method and system based on a cross-source interpretability deep learning model, and the method comprises the steps: obtaining mountain torrent parameters and environment parameters in an early warning region, and calculating the correlation degree of the mountain torrent parameters and the environment parameters; environment parameters are selected and combined according to the correlation degree, and corresponding mountain torrent parameters are connected to serve as samples of the early warning area; counting samples of different early warning areas, training a neural network model, and synchronously constructing an interpretation content library; and when the neural network model is applied, determining supplementary content of an output result according to the explanation content library. According to the method, other possible inputs of the same output can be obtaine</description><subject>ALARM SYSTEMS</subject><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>ORDER TELEGRAPHS</subject><subject>PHYSICS</subject><subject>SIGNALLING</subject><subject>SIGNALLING OR CALLING SYSTEMS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNzDEKwkAQRuE0FqLeYTxAwLAWaSUoNlrZh0321wQ2M8vOBMntbbS3es3HWxfDTWY2PzKZ5Aw2gs9xobfPPPKLJtgggTwH0kUNE3VeEUiY-iyqpcqce9DIhpwyzHcRFIBEEb-HBMRtsXr6qNh9uyn2l_OjuZZI0kKT78GwtrlXVe3csXaHk_vHfABbaUDm</recordid><startdate>20240712</startdate><enddate>20240712</enddate><creator>YAO YANQIU</creator><creator>XING PENGFEI</creator><creator>LI QINGLIANG</creator><creator>GENG QINGTIAN</creator><creator>YAO YIFEI</creator><creator>YU FANHUA</creator><scope>EVB</scope></search><sort><creationdate>20240712</creationdate><title>Mountain torrent early warning method and system based on cross-source interpretable deep learning model</title><author>YAO YANQIU ; XING PENGFEI ; LI QINGLIANG ; GENG QINGTIAN ; YAO YIFEI ; YU FANHUA</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118334830A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>ALARM SYSTEMS</topic><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>ORDER TELEGRAPHS</topic><topic>PHYSICS</topic><topic>SIGNALLING</topic><topic>SIGNALLING OR CALLING SYSTEMS</topic><toplevel>online_resources</toplevel><creatorcontrib>YAO YANQIU</creatorcontrib><creatorcontrib>XING PENGFEI</creatorcontrib><creatorcontrib>LI QINGLIANG</creatorcontrib><creatorcontrib>GENG QINGTIAN</creatorcontrib><creatorcontrib>YAO YIFEI</creatorcontrib><creatorcontrib>YU FANHUA</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>YAO YANQIU</au><au>XING PENGFEI</au><au>LI QINGLIANG</au><au>GENG QINGTIAN</au><au>YAO YIFEI</au><au>YU FANHUA</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Mountain torrent early warning method and system based on cross-source interpretable deep learning model</title><date>2024-07-12</date><risdate>2024</risdate><abstract>The invention relates to the technical field of mountain torrent early warning, and particularly discloses a mountain torrent early warning method and system based on a cross-source interpretability deep learning model, and the method comprises the steps: obtaining mountain torrent parameters and environment parameters in an early warning region, and calculating the correlation degree of the mountain torrent parameters and the environment parameters; environment parameters are selected and combined according to the correlation degree, and corresponding mountain torrent parameters are connected to serve as samples of the early warning area; counting samples of different early warning areas, training a neural network model, and synchronously constructing an interpretation content library; and when the neural network model is applied, determining supplementary content of an output result according to the explanation content library. According to the method, other possible inputs of the same output can be obtaine</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN118334830A
source esp@cenet
subjects ALARM SYSTEMS
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
ORDER TELEGRAPHS
PHYSICS
SIGNALLING
SIGNALLING OR CALLING SYSTEMS
title Mountain torrent early warning method and system based on cross-source interpretable deep learning model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T11%3A33%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=YAO%20YANQIU&rft.date=2024-07-12&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118334830A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true