Electric tower intelligent classification tower arrangement method based on deep learning

The invention relates to an electric tower intelligent classification tower arrangement method based on deep learning. Comprising the following steps: S1, acquiring a plurality of corresponding transmission line path data sets based on data characteristics of a plurality of road network open resourc...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: CHEN XINGYUN, CHEN XIANG, WANG XIANRI, CHENG JIANPING, LIU ZHIWEI, CHEN YUANHAO, LI XIAOGANG, LIN JIANHAO, WEI ZHEN, NIE KEJIAN, FU BENZHAO, SHI XIAOLIN, YU XINMIN, LIN RUIZONG
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 CHEN XINGYUN
CHEN XIANG
WANG XIANRI
CHENG JIANPING
LIU ZHIWEI
CHEN YUANHAO
LI XIAOGANG
LIN JIANHAO
WEI ZHEN
NIE KEJIAN
FU BENZHAO
SHI XIAOLIN
YU XINMIN
LIN RUIZONG
description The invention relates to an electric tower intelligent classification tower arrangement method based on deep learning. Comprising the following steps: S1, acquiring a plurality of corresponding transmission line path data sets based on data characteristics of a plurality of road network open resources, and preprocessing the transmission line path data sets to obtain corresponding preprocessed data sets; s2, obtaining a corresponding electric tower data set, and making a label used for training the deep neural network model; s3, adopting a classification deep neural network model with an attention mechanism to perform classification processing on each node in the preprocessed data set to obtain an electric tower type; and S4, visualizing an algorithm result, and providing an external interface. 本发明涉及一种基于深度学习的电塔智能分类排塔方法。包括:步骤S1、基于多个路网开放资源的数据特点,获取相应的多个输电线路路径数据集,对所述输电线路路径数据集进行预处理,获取相应的预处理数据集;步骤S2、获取相应的电塔数据集,制作用于训练深度神经网络模型的标签;步骤S3、采用带有注意力机制的分类深度神经网络模型,对所述预处理数据集中的各个节点进行分类处理,获取电塔类型;步骤S4、算法结果可视化,并提供对外使用接口。
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116740430A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116740430A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116740430A3</originalsourceid><addsrcrecordid>eNqNyrEKwjAQBuAsDqK-Q3wAoaVFZykVJycXp3Imf-tBegnJga-vQh_A6Vu-tXn0AU4zO6vxjWxZFCHwBFHrApXCIztSjrIEyplkwvwLM_QVvX1Sgbff4YFkAygLy7Q1q5FCwW5xY_aX_t5dD0hxQEnkINChu9X18dRWbVOdm3_OB0wkO0s</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Electric tower intelligent classification tower arrangement method based on deep learning</title><source>esp@cenet</source><creator>CHEN XINGYUN ; CHEN XIANG ; WANG XIANRI ; CHENG JIANPING ; LIU ZHIWEI ; CHEN YUANHAO ; LI XIAOGANG ; LIN JIANHAO ; WEI ZHEN ; NIE KEJIAN ; FU BENZHAO ; SHI XIAOLIN ; YU XINMIN ; LIN RUIZONG</creator><creatorcontrib>CHEN XINGYUN ; CHEN XIANG ; WANG XIANRI ; CHENG JIANPING ; LIU ZHIWEI ; CHEN YUANHAO ; LI XIAOGANG ; LIN JIANHAO ; WEI ZHEN ; NIE KEJIAN ; FU BENZHAO ; SHI XIAOLIN ; YU XINMIN ; LIN RUIZONG</creatorcontrib><description>The invention relates to an electric tower intelligent classification tower arrangement method based on deep learning. Comprising the following steps: S1, acquiring a plurality of corresponding transmission line path data sets based on data characteristics of a plurality of road network open resources, and preprocessing the transmission line path data sets to obtain corresponding preprocessed data sets; s2, obtaining a corresponding electric tower data set, and making a label used for training the deep neural network model; s3, adopting a classification deep neural network model with an attention mechanism to perform classification processing on each node in the preprocessed data set to obtain an electric tower type; and S4, visualizing an algorithm result, and providing an external interface. 本发明涉及一种基于深度学习的电塔智能分类排塔方法。包括:步骤S1、基于多个路网开放资源的数据特点,获取相应的多个输电线路路径数据集,对所述输电线路路径数据集进行预处理,获取相应的预处理数据集;步骤S2、获取相应的电塔数据集,制作用于训练深度神经网络模型的标签;步骤S3、采用带有注意力机制的分类深度神经网络模型,对所述预处理数据集中的各个节点进行分类处理,获取电塔类型;步骤S4、算法结果可视化,并提供对外使用接口。</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</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=20230912&amp;DB=EPODOC&amp;CC=CN&amp;NR=116740430A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76419</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20230912&amp;DB=EPODOC&amp;CC=CN&amp;NR=116740430A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CHEN XINGYUN</creatorcontrib><creatorcontrib>CHEN XIANG</creatorcontrib><creatorcontrib>WANG XIANRI</creatorcontrib><creatorcontrib>CHENG JIANPING</creatorcontrib><creatorcontrib>LIU ZHIWEI</creatorcontrib><creatorcontrib>CHEN YUANHAO</creatorcontrib><creatorcontrib>LI XIAOGANG</creatorcontrib><creatorcontrib>LIN JIANHAO</creatorcontrib><creatorcontrib>WEI ZHEN</creatorcontrib><creatorcontrib>NIE KEJIAN</creatorcontrib><creatorcontrib>FU BENZHAO</creatorcontrib><creatorcontrib>SHI XIAOLIN</creatorcontrib><creatorcontrib>YU XINMIN</creatorcontrib><creatorcontrib>LIN RUIZONG</creatorcontrib><title>Electric tower intelligent classification tower arrangement method based on deep learning</title><description>The invention relates to an electric tower intelligent classification tower arrangement method based on deep learning. Comprising the following steps: S1, acquiring a plurality of corresponding transmission line path data sets based on data characteristics of a plurality of road network open resources, and preprocessing the transmission line path data sets to obtain corresponding preprocessed data sets; s2, obtaining a corresponding electric tower data set, and making a label used for training the deep neural network model; s3, adopting a classification deep neural network model with an attention mechanism to perform classification processing on each node in the preprocessed data set to obtain an electric tower type; and S4, visualizing an algorithm result, and providing an external interface. 本发明涉及一种基于深度学习的电塔智能分类排塔方法。包括:步骤S1、基于多个路网开放资源的数据特点,获取相应的多个输电线路路径数据集,对所述输电线路路径数据集进行预处理,获取相应的预处理数据集;步骤S2、获取相应的电塔数据集,制作用于训练深度神经网络模型的标签;步骤S3、采用带有注意力机制的分类深度神经网络模型,对所述预处理数据集中的各个节点进行分类处理,获取电塔类型;步骤S4、算法结果可视化,并提供对外使用接口。</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyrEKwjAQBuAsDqK-Q3wAoaVFZykVJycXp3Imf-tBegnJga-vQh_A6Vu-tXn0AU4zO6vxjWxZFCHwBFHrApXCIztSjrIEyplkwvwLM_QVvX1Sgbff4YFkAygLy7Q1q5FCwW5xY_aX_t5dD0hxQEnkINChu9X18dRWbVOdm3_OB0wkO0s</recordid><startdate>20230912</startdate><enddate>20230912</enddate><creator>CHEN XINGYUN</creator><creator>CHEN XIANG</creator><creator>WANG XIANRI</creator><creator>CHENG JIANPING</creator><creator>LIU ZHIWEI</creator><creator>CHEN YUANHAO</creator><creator>LI XIAOGANG</creator><creator>LIN JIANHAO</creator><creator>WEI ZHEN</creator><creator>NIE KEJIAN</creator><creator>FU BENZHAO</creator><creator>SHI XIAOLIN</creator><creator>YU XINMIN</creator><creator>LIN RUIZONG</creator><scope>EVB</scope></search><sort><creationdate>20230912</creationdate><title>Electric tower intelligent classification tower arrangement method based on deep learning</title><author>CHEN XINGYUN ; CHEN XIANG ; WANG XIANRI ; CHENG JIANPING ; LIU ZHIWEI ; CHEN YUANHAO ; LI XIAOGANG ; LIN JIANHAO ; WEI ZHEN ; NIE KEJIAN ; FU BENZHAO ; SHI XIAOLIN ; YU XINMIN ; LIN RUIZONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116740430A3</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>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>CHEN XINGYUN</creatorcontrib><creatorcontrib>CHEN XIANG</creatorcontrib><creatorcontrib>WANG XIANRI</creatorcontrib><creatorcontrib>CHENG JIANPING</creatorcontrib><creatorcontrib>LIU ZHIWEI</creatorcontrib><creatorcontrib>CHEN YUANHAO</creatorcontrib><creatorcontrib>LI XIAOGANG</creatorcontrib><creatorcontrib>LIN JIANHAO</creatorcontrib><creatorcontrib>WEI ZHEN</creatorcontrib><creatorcontrib>NIE KEJIAN</creatorcontrib><creatorcontrib>FU BENZHAO</creatorcontrib><creatorcontrib>SHI XIAOLIN</creatorcontrib><creatorcontrib>YU XINMIN</creatorcontrib><creatorcontrib>LIN RUIZONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CHEN XINGYUN</au><au>CHEN XIANG</au><au>WANG XIANRI</au><au>CHENG JIANPING</au><au>LIU ZHIWEI</au><au>CHEN YUANHAO</au><au>LI XIAOGANG</au><au>LIN JIANHAO</au><au>WEI ZHEN</au><au>NIE KEJIAN</au><au>FU BENZHAO</au><au>SHI XIAOLIN</au><au>YU XINMIN</au><au>LIN RUIZONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Electric tower intelligent classification tower arrangement method based on deep learning</title><date>2023-09-12</date><risdate>2023</risdate><abstract>The invention relates to an electric tower intelligent classification tower arrangement method based on deep learning. Comprising the following steps: S1, acquiring a plurality of corresponding transmission line path data sets based on data characteristics of a plurality of road network open resources, and preprocessing the transmission line path data sets to obtain corresponding preprocessed data sets; s2, obtaining a corresponding electric tower data set, and making a label used for training the deep neural network model; s3, adopting a classification deep neural network model with an attention mechanism to perform classification processing on each node in the preprocessed data set to obtain an electric tower type; and S4, visualizing an algorithm result, and providing an external interface. 本发明涉及一种基于深度学习的电塔智能分类排塔方法。包括:步骤S1、基于多个路网开放资源的数据特点,获取相应的多个输电线路路径数据集,对所述输电线路路径数据集进行预处理,获取相应的预处理数据集;步骤S2、获取相应的电塔数据集,制作用于训练深度神经网络模型的标签;步骤S3、采用带有注意力机制的分类深度神经网络模型,对所述预处理数据集中的各个节点进行分类处理,获取电塔类型;步骤S4、算法结果可视化,并提供对外使用接口。</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN116740430A
source esp@cenet
subjects CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
title Electric tower intelligent classification tower arrangement method based on deep learning
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T00%3A27%3A14IST&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=CHEN%20XINGYUN&rft.date=2023-09-12&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116740430A%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