CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data

CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data Jianbao Zhang, Deyi Wang, Shiping Bao, Liang Yi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3949085/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract The Chinese cooking process is complex and involves a wide variety of dishes, contains a large amount of data, which poses a great challenge to the intelligentization of cooking. To address this issue, this paper proposes a versatile, highly reliable, user-friendly, and intelligent data recorder for Chinese cuisine (Chinese Cuisine Intelligent Cooker Recorder-V1.0, CCICR-V1.0), which is designed to record the associated data generated during Chinese cooking and provide data support for intelligent cooking robot systems.CCICR-V1.0 is equipped with an STM32 microcontroller as the control core and integrated with temperature, pressure, and attitude sensors, enabling intelligent collection of data related to dishes, ingredients, and cookware movement during the cooking process. For high-frequency and multi-channel data acquisition tasks, CCICR-V1.0 adopts a multi-task data buffering storage method, effectively allocating CPU resources, and utilizes NAND Flash as the storage medium to ensure secure storage, management, and preservation of data. Through testing, CCICR-V1.0 has demonstrated its capability to achieve multi-channel, high-frequency data acquisition and storage in complex environments, resulting in an automation rate increase of 89.9%. The weighing module of CCICR-V1.0 exhibits a maximum relative error of 0.288%, while the non-contact infrared temperature measurement module shows a maximum absolute error and maximum relative error of only 0.65°C and 0.78%, respectively. The non-contact infrared temperature measurement module also achieves a cost reduction of 91.7% and an accuracy improvement of approximately 40%. CCICR-V1.0 effectively captures Chinese cooking data and possesses significant commercial value for widespread adoption and application. Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Electrical and electronic engineering Embeded system Chinese Cuisine STM32 Sensors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 30 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviews received at journal 18 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviews received at journal 02 Aug, 2024 Reviewers agreed at journal 17 Jul, 2024 Reviews received at journal 25 May, 2024 Reviewers agreed at journal 05 May, 2024 Reviewers invited by journal 27 Feb, 2024 Editor assigned by journal 24 Feb, 2024 Editor invited by journal 21 Feb, 2024 Submission checks completed at journal 21 Feb, 2024 First submitted to journal 11 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3949085","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274200362,"identity":"a5a13afd-c206-4cb5-8809-9c03fdf24f8f","order_by":0,"name":"Jianbao Zhang","email":"","orcid":"","institution":"Guangdong Mechanical and Electrical Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Jianbao","middleName":"","lastName":"Zhang","suffix":""},{"id":274200363,"identity":"8be3ae21-f5f1-4dc4-8511-29b42bf8b49b","order_by":1,"name":"Deyi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDCCA2DEwMDPwGAAFyFOi2QDKVrAwOAAsVr4jvcYHuapuWO3+fzirRs+1DDI8d1IYPxcgEeL5JkzBod5jj1L3nbjWdnNGccYjCVvJDBLz8CjxeBGjsHhHLbDyWY3zpjd5m1gSNxwI4GNmQeflvtvgFr+HU42ngHU8reBoZ6wlhs8Bodz2w7bGfD3mN1mbGBIMCCkRfJMWsHhv32HEyRusJXd7DkmYTjzzMNmaXxa+I4f3vxxxrfD9vz9h7fd+FFjI893PPngZ3xaGBg4wNGR2CCRAKIlgBjoPPyA/QGItGfgP0BA4SgYBaNgFIxYAAACgFu+f/lwRgAAAABJRU5ErkJggg==","orcid":"","institution":"Guangdong Mechanical and Electrical Polytechnic","correspondingAuthor":true,"prefix":"","firstName":"Deyi","middleName":"","lastName":"Wang","suffix":""},{"id":274200364,"identity":"c37da444-67c0-4d4e-9298-7c75f8f25e75","order_by":2,"name":"Shiping Bao","email":"","orcid":"","institution":"Guangdong Mechanical and Electrical Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Shiping","middleName":"","lastName":"Bao","suffix":""},{"id":274200365,"identity":"b1630ae1-8914-49e4-b76a-5cbfe729cca0","order_by":3,"name":"Liang Yi","email":"","orcid":"","institution":"Dalian University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2024-02-11 18:14:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3949085/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3949085/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-76638-w","type":"published","date":"2024-10-29T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68206514,"identity":"a5067935-72b7-4b02-9baa-ba6979b5ed5a","added_by":"auto","created_at":"2024-11-04 16:32:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1597714,"visible":true,"origin":"","legend":"","description":"","filename":"gjdzjb240221.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3949085/v1_covered_b3bc29f7-6207-4099-9f68-4129575eb010.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Embeded system, Chinese Cuisine, STM32, Sensors","lastPublishedDoi":"10.21203/rs.3.rs-3949085/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3949085/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Chinese cooking process is complex and involves a wide variety of dishes, contains a large amount of data, which poses a great challenge to the intelligentization of cooking. To address this issue, this paper proposes a versatile, highly reliable, user-friendly, and intelligent data recorder for Chinese cuisine (Chinese Cuisine Intelligent Cooker Recorder-V1.0, CCICR-V1.0), which is designed to record the associated data generated during Chinese cooking and provide data support for intelligent cooking robot systems.CCICR-V1.0 is equipped with an STM32 microcontroller as the control core and integrated with temperature, pressure, and attitude sensors, enabling intelligent collection of data related to dishes, ingredients, and cookware movement during the cooking process. For high-frequency and multi-channel data acquisition tasks, CCICR-V1.0 adopts a multi-task data buffering storage method, effectively allocating CPU resources, and utilizes NAND Flash as the storage medium to ensure secure storage, management, and preservation of data. Through testing, CCICR-V1.0 has demonstrated its capability to achieve multi-channel, high-frequency data acquisition and storage in complex environments, resulting in an automation rate increase of 89.9%. The weighing module of CCICR-V1.0 exhibits a maximum relative error of 0.288%, while the non-contact infrared temperature measurement module shows a maximum absolute error and maximum relative error of only 0.65°C and 0.78%, respectively. The non-contact infrared temperature measurement module also achieves a cost reduction of 91.7% and an accuracy improvement of approximately 40%. 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