Shield tunneling machine guiding prediction method, model training method, device and equipment
The invention discloses a shield tunneling machine guiding prediction method which comprises the following steps: obtaining real-time shield tunneling data of a shield tunneling machine; inputting thereal-time shield data into a guide prediction model to obtain prediction guide data; obtaining a gui...
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 | LI ZENGLIANG YUE LINHUI LIU YANHUI YU XIANGTAO |
| description | The invention discloses a shield tunneling machine guiding prediction method which comprises the following steps: obtaining real-time shield tunneling data of a shield tunneling machine; inputting thereal-time shield data into a guide prediction model to obtain prediction guide data; obtaining a guiding prediction result of the shield tunneling machine according to the prediction guiding data. The invention further discloses a guide prediction model training method, device and equipment and a storage medium. Due to the fact that the real-time shield data of the shield tunneling machine is thereal-time shield data collected when the shield tunneling machine operates, compared with mechanical analysis data, the real-time shield data is advantageous in that accuracy is higher, so that the shield guidance is predicted by inputting the real-time shield data into the guidance prediction model, the accuracy of the obtained prediction guidance data is higher, and the accuracy of the obtainedguidance prediction result |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN111931842A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN111931842A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN111931842A3</originalsourceid><addsrcrecordid>eNqNizEOwjAQBNNQIOAPRw-FCQWUKAJR0UAfWb4lPsk-m8Th_QiUB1CtZjQ7r9q7FwSmMqoiiHYUrfOioG4U_nLuweKKJKWI4hNvKCZGoNJb0d9j0oy3OJBVJrxGyRFaltXsacOA1bSLan05P5rrFjm1GLJ1UJS2uRljjrU57Hen-p_mA5D6PP4</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Shield tunneling machine guiding prediction method, model training method, device and equipment</title><source>esp@cenet</source><creator>LI ZENGLIANG ; YUE LINHUI ; LIU YANHUI ; YU XIANGTAO</creator><creatorcontrib>LI ZENGLIANG ; YUE LINHUI ; LIU YANHUI ; YU XIANGTAO</creatorcontrib><description>The invention discloses a shield tunneling machine guiding prediction method which comprises the following steps: obtaining real-time shield tunneling data of a shield tunneling machine; inputting thereal-time shield data into a guide prediction model to obtain prediction guide data; obtaining a guiding prediction result of the shield tunneling machine according to the prediction guiding data. The invention further discloses a guide prediction model training method, device and equipment and a storage medium. Due to the fact that the real-time shield data of the shield tunneling machine is thereal-time shield data collected when the shield tunneling machine operates, compared with mechanical analysis data, the real-time shield data is advantageous in that accuracy is higher, so that the shield guidance is predicted by inputting the real-time shield data into the guidance prediction model, the accuracy of the obtained prediction guidance data is higher, and the accuracy of the obtainedguidance prediction result</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2020</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=20201113&DB=EPODOC&CC=CN&NR=111931842A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201113&DB=EPODOC&CC=CN&NR=111931842A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LI ZENGLIANG</creatorcontrib><creatorcontrib>YUE LINHUI</creatorcontrib><creatorcontrib>LIU YANHUI</creatorcontrib><creatorcontrib>YU XIANGTAO</creatorcontrib><title>Shield tunneling machine guiding prediction method, model training method, device and equipment</title><description>The invention discloses a shield tunneling machine guiding prediction method which comprises the following steps: obtaining real-time shield tunneling data of a shield tunneling machine; inputting thereal-time shield data into a guide prediction model to obtain prediction guide data; obtaining a guiding prediction result of the shield tunneling machine according to the prediction guiding data. The invention further discloses a guide prediction model training method, device and equipment and a storage medium. Due to the fact that the real-time shield data of the shield tunneling machine is thereal-time shield data collected when the shield tunneling machine operates, compared with mechanical analysis data, the real-time shield data is advantageous in that accuracy is higher, so that the shield guidance is predicted by inputting the real-time shield data into the guidance prediction model, the accuracy of the obtained prediction guidance data is higher, and the accuracy of the obtainedguidance prediction result</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNizEOwjAQBNNQIOAPRw-FCQWUKAJR0UAfWb4lPsk-m8Th_QiUB1CtZjQ7r9q7FwSmMqoiiHYUrfOioG4U_nLuweKKJKWI4hNvKCZGoNJb0d9j0oy3OJBVJrxGyRFaltXsacOA1bSLan05P5rrFjm1GLJ1UJS2uRljjrU57Hen-p_mA5D6PP4</recordid><startdate>20201113</startdate><enddate>20201113</enddate><creator>LI ZENGLIANG</creator><creator>YUE LINHUI</creator><creator>LIU YANHUI</creator><creator>YU XIANGTAO</creator><scope>EVB</scope></search><sort><creationdate>20201113</creationdate><title>Shield tunneling machine guiding prediction method, model training method, device and equipment</title><author>LI ZENGLIANG ; YUE LINHUI ; LIU YANHUI ; YU XIANGTAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN111931842A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>LI ZENGLIANG</creatorcontrib><creatorcontrib>YUE LINHUI</creatorcontrib><creatorcontrib>LIU YANHUI</creatorcontrib><creatorcontrib>YU XIANGTAO</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LI ZENGLIANG</au><au>YUE LINHUI</au><au>LIU YANHUI</au><au>YU XIANGTAO</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Shield tunneling machine guiding prediction method, model training method, device and equipment</title><date>2020-11-13</date><risdate>2020</risdate><abstract>The invention discloses a shield tunneling machine guiding prediction method which comprises the following steps: obtaining real-time shield tunneling data of a shield tunneling machine; inputting thereal-time shield data into a guide prediction model to obtain prediction guide data; obtaining a guiding prediction result of the shield tunneling machine according to the prediction guiding data. The invention further discloses a guide prediction model training method, device and equipment and a storage medium. Due to the fact that the real-time shield data of the shield tunneling machine is thereal-time shield data collected when the shield tunneling machine operates, compared with mechanical analysis data, the real-time shield data is advantageous in that accuracy is higher, so that the shield guidance is predicted by inputting the real-time shield data into the guidance prediction model, the accuracy of the obtained prediction guidance data is higher, and the accuracy of the obtainedguidance prediction result</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN111931842A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Shield tunneling machine guiding prediction method, model training method, device and equipment |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T20%3A04%3A51IST&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=LI%20ZENGLIANG&rft.date=2020-11-13&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN111931842A%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 |