Aircraft structural part design method based on machine learning
The invention discloses an aircraft structural part design method based on machine learning. The aircraft structural part design method comprises the following steps: 1) inputting design requirements and related parameters of an aircraft structural part required to be designed through a human-comput...
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creator | WANG WANJIONG HAO BO ZHANG PENG WANG JIE YIN XINGCHAO WANG MINGYANG |
description | The invention discloses an aircraft structural part design method based on machine learning. The aircraft structural part design method comprises the following steps: 1) inputting design requirements and related parameters of an aircraft structural part required to be designed through a human-computer interaction interface; 2) carrying out feature analysis on the aircraft wing structural member through OpenCv auxiliary feature recognition; (3) similar instances are retrieved based on rule type selection and instance reasoning, instances with high similarity are selected, and on the basis of the instances, designers modify the instances according to requirements needing to be designed; 4) classifying the extracted features according to a k-nearest neighbor algorithm, and adding the features as training learning results into a training library of machine learning; and 5) storing the corrected instance and the design solution as new instances in an instance library as a new instance. According to the method, kno |
format | Patent |
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The aircraft structural part design method comprises the following steps: 1) inputting design requirements and related parameters of an aircraft structural part required to be designed through a human-computer interaction interface; 2) carrying out feature analysis on the aircraft wing structural member through OpenCv auxiliary feature recognition; (3) similar instances are retrieved based on rule type selection and instance reasoning, instances with high similarity are selected, and on the basis of the instances, designers modify the instances according to requirements needing to be designed; 4) classifying the extracted features according to a k-nearest neighbor algorithm, and adding the features as training learning results into a training library of machine learning; and 5) storing the corrected instance and the design solution as new instances in an instance library as a new instance. According to the method, kno</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2022</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=20220531&DB=EPODOC&CC=CN&NR=114564793A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,777,882,25545,76296</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220531&DB=EPODOC&CC=CN&NR=114564793A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>WANG WANJIONG</creatorcontrib><creatorcontrib>HAO BO</creatorcontrib><creatorcontrib>ZHANG PENG</creatorcontrib><creatorcontrib>WANG JIE</creatorcontrib><creatorcontrib>YIN XINGCHAO</creatorcontrib><creatorcontrib>WANG MINGYANG</creatorcontrib><title>Aircraft structural part design method based on machine learning</title><description>The invention discloses an aircraft structural part design method based on machine learning. The aircraft structural part design method comprises the following steps: 1) inputting design requirements and related parameters of an aircraft structural part required to be designed through a human-computer interaction interface; 2) carrying out feature analysis on the aircraft wing structural member through OpenCv auxiliary feature recognition; (3) similar instances are retrieved based on rule type selection and instance reasoning, instances with high similarity are selected, and on the basis of the instances, designers modify the instances according to requirements needing to be designed; 4) classifying the extracted features according to a k-nearest neighbor algorithm, and adding the features as training learning results into a training library of machine learning; and 5) storing the corrected instance and the design solution as new instances in an instance library as a new instance. According to the method, kno</description><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>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>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHBwzCxKLkpMK1EoLikqTS4pLUrMUShILCpRSEktzkzPU8hNLcnIT1FISixOTVHIB_ITkzMy81IVclITi_Iy89J5GFjTEnOKU3mhNDeDoptriLOHbmpBfnxqcUFicmpeakm8s5-hoYmpmYm5pbGjMTFqAN0oMW8</recordid><startdate>20220531</startdate><enddate>20220531</enddate><creator>WANG WANJIONG</creator><creator>HAO BO</creator><creator>ZHANG PENG</creator><creator>WANG JIE</creator><creator>YIN XINGCHAO</creator><creator>WANG MINGYANG</creator><scope>EVB</scope></search><sort><creationdate>20220531</creationdate><title>Aircraft structural part design method based on machine learning</title><author>WANG WANJIONG ; HAO BO ; ZHANG PENG ; WANG JIE ; YIN XINGCHAO ; WANG MINGYANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114564793A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><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>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>WANG WANJIONG</creatorcontrib><creatorcontrib>HAO BO</creatorcontrib><creatorcontrib>ZHANG PENG</creatorcontrib><creatorcontrib>WANG JIE</creatorcontrib><creatorcontrib>YIN XINGCHAO</creatorcontrib><creatorcontrib>WANG MINGYANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WANG WANJIONG</au><au>HAO BO</au><au>ZHANG PENG</au><au>WANG JIE</au><au>YIN XINGCHAO</au><au>WANG MINGYANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Aircraft structural part design method based on machine learning</title><date>2022-05-31</date><risdate>2022</risdate><abstract>The invention discloses an aircraft structural part design method based on machine learning. The aircraft structural part design method comprises the following steps: 1) inputting design requirements and related parameters of an aircraft structural part required to be designed through a human-computer interaction interface; 2) carrying out feature analysis on the aircraft wing structural member through OpenCv auxiliary feature recognition; (3) similar instances are retrieved based on rule type selection and instance reasoning, instances with high similarity are selected, and on the basis of the instances, designers modify the instances according to requirements needing to be designed; 4) classifying the extracted features according to a k-nearest neighbor algorithm, and adding the features as training learning results into a training library of machine learning; and 5) storing the corrected instance and the design solution as new instances in an instance library as a new instance. According to the method, kno</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
title | Aircraft structural part design method based on machine learning |
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