Prediction method and system for national ecological environment information visualization based on PredRNN
The invention discloses a PredRNN-based nationwide ecological environment information visualization prediction method and system, and the method comprises the steps: embedding a PredRNN deep learning model in a prediction function of a visualization system, introducing the induction deviation of gro...
Gespeichert in:
Hauptverfasser: | , , , , , , , |
---|---|
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 | SHI HAOYUAN TU LIJING BIAN JINGE ZHANG YANCONG TAO ZHENG ZHAO DEHAN WU GUODONG ZHANG NENGXIANG |
description | The invention discloses a PredRNN-based nationwide ecological environment information visualization prediction method and system, and the method comprises the steps: embedding a PredRNN deep learning model in a prediction function of a visualization system, introducing the induction deviation of group invariance in space into space-time prediction through employing a convolution layer, and carrying out the prediction of the prediction function. The method has higher modeling capability and higher calculation efficiency for the historical observation sequence. Real-time ecological environment data are obtained through a web crawler technology, the data are processed and then enter a PredRNN prediction model for prediction, a visualization tool is called through static resources, a main page is constructed, service and request are responded based on a cloud server side, and the data are obtained and visually displayed. And finally obtained visual data can be used for query of the majority of users.
本发明公开了一种基于Pr |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN115907148A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN115907148A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN115907148A3</originalsourceid><addsrcrecordid>eNqNjUEKwkAQBHPxIOofxgcIBhXjUYLiKYh4D-PuRAd3Z0J2DejrTdQHeOpquqCHyf3YkGUTWQU8xZtaQLEQniGSh0obEOxHdEBGnV7Z9CgtNyqeJAJLZ_mPBC2HBzp-fdsFA1nooP84FcU4GVToAk1-OUqm-905P8yo1pJCjYaEYpkXabrazNfpMtsu_nHeVQ1CNQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Prediction method and system for national ecological environment information visualization based on PredRNN</title><source>esp@cenet</source><creator>SHI HAOYUAN ; TU LIJING ; BIAN JINGE ; ZHANG YANCONG ; TAO ZHENG ; ZHAO DEHAN ; WU GUODONG ; ZHANG NENGXIANG</creator><creatorcontrib>SHI HAOYUAN ; TU LIJING ; BIAN JINGE ; ZHANG YANCONG ; TAO ZHENG ; ZHAO DEHAN ; WU GUODONG ; ZHANG NENGXIANG</creatorcontrib><description>The invention discloses a PredRNN-based nationwide ecological environment information visualization prediction method and system, and the method comprises the steps: embedding a PredRNN deep learning model in a prediction function of a visualization system, introducing the induction deviation of group invariance in space into space-time prediction through employing a convolution layer, and carrying out the prediction of the prediction function. The method has higher modeling capability and higher calculation efficiency for the historical observation sequence. Real-time ecological environment data are obtained through a web crawler technology, the data are processed and then enter a PredRNN prediction model for prediction, a visualization tool is called through static resources, a main page is constructed, service and request are responded based on a cloud server side, and the data are obtained and visually displayed. And finally obtained visual data can be used for query of the majority of users.
本发明公开了一种基于Pr</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</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&date=20230404&DB=EPODOC&CC=CN&NR=115907148A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76293</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230404&DB=EPODOC&CC=CN&NR=115907148A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SHI HAOYUAN</creatorcontrib><creatorcontrib>TU LIJING</creatorcontrib><creatorcontrib>BIAN JINGE</creatorcontrib><creatorcontrib>ZHANG YANCONG</creatorcontrib><creatorcontrib>TAO ZHENG</creatorcontrib><creatorcontrib>ZHAO DEHAN</creatorcontrib><creatorcontrib>WU GUODONG</creatorcontrib><creatorcontrib>ZHANG NENGXIANG</creatorcontrib><title>Prediction method and system for national ecological environment information visualization based on PredRNN</title><description>The invention discloses a PredRNN-based nationwide ecological environment information visualization prediction method and system, and the method comprises the steps: embedding a PredRNN deep learning model in a prediction function of a visualization system, introducing the induction deviation of group invariance in space into space-time prediction through employing a convolution layer, and carrying out the prediction of the prediction function. The method has higher modeling capability and higher calculation efficiency for the historical observation sequence. Real-time ecological environment data are obtained through a web crawler technology, the data are processed and then enter a PredRNN prediction model for prediction, a visualization tool is called through static resources, a main page is constructed, service and request are responded based on a cloud server side, and the data are obtained and visually displayed. And finally obtained visual data can be used for query of the majority of users.
本发明公开了一种基于Pr</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNjUEKwkAQBHPxIOofxgcIBhXjUYLiKYh4D-PuRAd3Z0J2DejrTdQHeOpquqCHyf3YkGUTWQU8xZtaQLEQniGSh0obEOxHdEBGnV7Z9CgtNyqeJAJLZ_mPBC2HBzp-fdsFA1nooP84FcU4GVToAk1-OUqm-905P8yo1pJCjYaEYpkXabrazNfpMtsu_nHeVQ1CNQ</recordid><startdate>20230404</startdate><enddate>20230404</enddate><creator>SHI HAOYUAN</creator><creator>TU LIJING</creator><creator>BIAN JINGE</creator><creator>ZHANG YANCONG</creator><creator>TAO ZHENG</creator><creator>ZHAO DEHAN</creator><creator>WU GUODONG</creator><creator>ZHANG NENGXIANG</creator><scope>EVB</scope></search><sort><creationdate>20230404</creationdate><title>Prediction method and system for national ecological environment information visualization based on PredRNN</title><author>SHI HAOYUAN ; TU LIJING ; BIAN JINGE ; ZHANG YANCONG ; TAO ZHENG ; ZHAO DEHAN ; WU GUODONG ; ZHANG NENGXIANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN115907148A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>SHI HAOYUAN</creatorcontrib><creatorcontrib>TU LIJING</creatorcontrib><creatorcontrib>BIAN JINGE</creatorcontrib><creatorcontrib>ZHANG YANCONG</creatorcontrib><creatorcontrib>TAO ZHENG</creatorcontrib><creatorcontrib>ZHAO DEHAN</creatorcontrib><creatorcontrib>WU GUODONG</creatorcontrib><creatorcontrib>ZHANG NENGXIANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SHI HAOYUAN</au><au>TU LIJING</au><au>BIAN JINGE</au><au>ZHANG YANCONG</au><au>TAO ZHENG</au><au>ZHAO DEHAN</au><au>WU GUODONG</au><au>ZHANG NENGXIANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Prediction method and system for national ecological environment information visualization based on PredRNN</title><date>2023-04-04</date><risdate>2023</risdate><abstract>The invention discloses a PredRNN-based nationwide ecological environment information visualization prediction method and system, and the method comprises the steps: embedding a PredRNN deep learning model in a prediction function of a visualization system, introducing the induction deviation of group invariance in space into space-time prediction through employing a convolution layer, and carrying out the prediction of the prediction function. The method has higher modeling capability and higher calculation efficiency for the historical observation sequence. Real-time ecological environment data are obtained through a web crawler technology, the data are processed and then enter a PredRNN prediction model for prediction, a visualization tool is called through static resources, a main page is constructed, service and request are responded based on a cloud server side, and the data are obtained and visually displayed. And finally obtained visual data can be used for query of the majority of users.
本发明公开了一种基于Pr</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN115907148A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
title | Prediction method and system for national ecological environment information visualization based on PredRNN |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T05%3A18%3A52IST&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=SHI%20HAOYUAN&rft.date=2023-04-04&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN115907148A%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 |