Intelligent planning method for combat resource cross-domain allocation problem

The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment;...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: LYU NAIBING, ZHAO RUOFAN, DONG QIAN, WANG YI, LIU QINGGUO, WANG CAIHONG, XU XINYUE, XING GUYAN
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 LYU NAIBING
ZHAO RUOFAN
DONG QIAN
WANG YI
LIU QINGGUO
WANG CAIHONG
XU XINYUE
XING GUYAN
description The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116187787A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116187787A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116187787A3</originalsourceid><addsrcrecordid>eNqNyjEOwjAMAMAsDAj4g3lAhwqJdEUVCBZY2Cs3ddtIjh0l5v8sPIDpltu610OMmONCYpAZRaIskMhWnWDWAkHTiAaFqn5KIAhFa20mTRgFkFkDWlSBXHRkSnu3mZErHX7u3PF2fff3hrIOVDMGErKhf7btue287_zl9M_5Aq7KN7Q</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Intelligent planning method for combat resource cross-domain allocation problem</title><source>esp@cenet</source><creator>LYU NAIBING ; ZHAO RUOFAN ; DONG QIAN ; WANG YI ; LIU QINGGUO ; WANG CAIHONG ; XU XINYUE ; XING GUYAN</creator><creatorcontrib>LYU NAIBING ; ZHAO RUOFAN ; DONG QIAN ; WANG YI ; LIU QINGGUO ; WANG CAIHONG ; XU XINYUE ; XING GUYAN</creatorcontrib><description>The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the</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 ; 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&amp;date=20230530&amp;DB=EPODOC&amp;CC=CN&amp;NR=116187787A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76294</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230530&amp;DB=EPODOC&amp;CC=CN&amp;NR=116187787A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LYU NAIBING</creatorcontrib><creatorcontrib>ZHAO RUOFAN</creatorcontrib><creatorcontrib>DONG QIAN</creatorcontrib><creatorcontrib>WANG YI</creatorcontrib><creatorcontrib>LIU QINGGUO</creatorcontrib><creatorcontrib>WANG CAIHONG</creatorcontrib><creatorcontrib>XU XINYUE</creatorcontrib><creatorcontrib>XING GUYAN</creatorcontrib><title>Intelligent planning method for combat resource cross-domain allocation problem</title><description>The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the</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>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>eNqNyjEOwjAMAMAsDAj4g3lAhwqJdEUVCBZY2Cs3ddtIjh0l5v8sPIDpltu610OMmONCYpAZRaIskMhWnWDWAkHTiAaFqn5KIAhFa20mTRgFkFkDWlSBXHRkSnu3mZErHX7u3PF2fff3hrIOVDMGErKhf7btue287_zl9M_5Aq7KN7Q</recordid><startdate>20230530</startdate><enddate>20230530</enddate><creator>LYU NAIBING</creator><creator>ZHAO RUOFAN</creator><creator>DONG QIAN</creator><creator>WANG YI</creator><creator>LIU QINGGUO</creator><creator>WANG CAIHONG</creator><creator>XU XINYUE</creator><creator>XING GUYAN</creator><scope>EVB</scope></search><sort><creationdate>20230530</creationdate><title>Intelligent planning method for combat resource cross-domain allocation problem</title><author>LYU NAIBING ; ZHAO RUOFAN ; DONG QIAN ; WANG YI ; LIU QINGGUO ; WANG CAIHONG ; XU XINYUE ; XING GUYAN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116187787A3</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>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>LYU NAIBING</creatorcontrib><creatorcontrib>ZHAO RUOFAN</creatorcontrib><creatorcontrib>DONG QIAN</creatorcontrib><creatorcontrib>WANG YI</creatorcontrib><creatorcontrib>LIU QINGGUO</creatorcontrib><creatorcontrib>WANG CAIHONG</creatorcontrib><creatorcontrib>XU XINYUE</creatorcontrib><creatorcontrib>XING GUYAN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LYU NAIBING</au><au>ZHAO RUOFAN</au><au>DONG QIAN</au><au>WANG YI</au><au>LIU QINGGUO</au><au>WANG CAIHONG</au><au>XU XINYUE</au><au>XING GUYAN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Intelligent planning method for combat resource cross-domain allocation problem</title><date>2023-05-30</date><risdate>2023</risdate><abstract>The invention provides an intelligent planning method for a combat resource cross-domain allocation problem. The intelligent planning method comprises the following steps: designing a communication-based multi-agent reinforcement learning combat resource cross-domain allocation solving environment; establishing a proximity strategy optimization network model for cross-domain deployment of multi-agent reinforcement learning combat resources based on communication; training a communication-based multi-agent reinforcement learning combat resource cross-domain deployment proximity policy optimization network model to obtain a trained proximity policy optimization network model; the trained proximity strategy optimization network model is used for testing, and a combat resource cross-domain scheduling problem is solved; and multi-agent combat resource cross-domain deployment deep reinforcement learning network optimization is carried out for application scene changes, and self-learning and online upgrading of the</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN116187787A
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
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
title Intelligent planning method for combat resource cross-domain allocation problem
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T14%3A02%3A26IST&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=LYU%20NAIBING&rft.date=2023-05-30&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116187787A%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