EFFLUENT QUALITY PREDICTION DEVICE AND EFFLUENT QUALITY PREDICTION METHOD
To eliminate abnormal data included in time-series data from a plurality of sensors at a water treatment site.SOLUTION: In an effluent quality prediction device (100), a pre-processing unit (103a) performs filtering to a learning dataset, which is a portion of learning data for a first prescribed ti...
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| creator | HUANG LAN TAKEHARA TERUMI OKUBO AKIRA KIYOTAKI KAZUO HOCHIN TERUHISA MORIMOTO MITSURU |
| description | To eliminate abnormal data included in time-series data from a plurality of sensors at a water treatment site.SOLUTION: In an effluent quality prediction device (100), a pre-processing unit (103a) performs filtering to a learning dataset, which is a portion of learning data for a first prescribed time in learning data. A model generation unit (103c) generates a prediction model for predicting effluent quality data a prescribed time after the learning dataset. Meanwhile, an effluent quality prediction unit (105) applies the prediction model to the latest data stored in a latest data database (104) and predicts effluent quality data.SELECTED DRAWING: Figure 1
【課題】水処理場の複数のセンサからの時系列データに含まれる異常データを除去する。【解決手段】放流水質予測装置（１００）において、前処理部（１０３ａ）は、学習データの中の第１の所定時間分の学習データを学習データセットとし、学習データセットにフィルタ処理を行う。モデル生成部（１０３ｃ）は、学習データセットから所定時間後の放流水質データを予測するための予測モデルを生成する。また、放流水質予測部（１０５）は、直近データデータベース（１０４）に記憶される直近データに、予測モデルを適用して放流水質データを予測する。【選択図】図１ |
| format | Patent |
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【課題】水処理場の複数のセンサからの時系列データに含まれる異常データを除去する。【解決手段】放流水質予測装置（１００）において、前処理部（１０３ａ）は、学習データの中の第１の所定時間分の学習データを学習データセットとし、学習データセットにフィルタ処理を行う。モデル生成部（１０３ｃ）は、学習データセットから所定時間後の放流水質データを予測するための予測モデルを生成する。また、放流水質予測部（１０５）は、直近データデータベース（１０４）に記憶される直近データに、予測モデルを適用して放流水質データを予測する。【選択図】図１</description><language>eng ; jpn</language><subject>CALCULATING ; CHEMISTRY ; COMPUTING ; CONTROL OR REGULATING SYSTEMS IN GENERAL ; CONTROLLING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS ; METALLURGY ; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS ; PHYSICS ; REGULATING ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR ; TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE</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=20220121&DB=EPODOC&CC=JP&NR=2022015250A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220121&DB=EPODOC&CC=JP&NR=2022015250A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HUANG LAN</creatorcontrib><creatorcontrib>TAKEHARA TERUMI</creatorcontrib><creatorcontrib>OKUBO AKIRA</creatorcontrib><creatorcontrib>KIYOTAKI KAZUO</creatorcontrib><creatorcontrib>HOCHIN TERUHISA</creatorcontrib><creatorcontrib>MORIMOTO MITSURU</creatorcontrib><title>EFFLUENT QUALITY PREDICTION DEVICE AND EFFLUENT QUALITY PREDICTION METHOD</title><description>To eliminate abnormal data included in time-series data from a plurality of sensors at a water treatment site.SOLUTION: In an effluent quality prediction device (100), a pre-processing unit (103a) performs filtering to a learning dataset, which is a portion of learning data for a first prescribed time in learning data. A model generation unit (103c) generates a prediction model for predicting effluent quality data a prescribed time after the learning dataset. Meanwhile, an effluent quality prediction unit (105) applies the prediction model to the latest data stored in a latest data database (104) and predicts effluent quality data.SELECTED DRAWING: Figure 1
【課題】水処理場の複数のセンサからの時系列データに含まれる異常データを除去する。【解決手段】放流水質予測装置（１００）において、前処理部（１０３ａ）は、学習データの中の第１の所定時間分の学習データを学習データセットとし、学習データセットにフィルタ処理を行う。モデル生成部（１０３ｃ）は、学習データセットから所定時間後の放流水質データを予測するための予測モデルを生成する。また、放流水質予測部（１０５）は、直近データデータベース（１０４）に記憶される直近データに、予測モデルを適用して放流水質データを予測する。【選択図】図１</description><subject>CALCULATING</subject><subject>CHEMISTRY</subject><subject>COMPUTING</subject><subject>CONTROL OR REGULATING SYSTEMS IN GENERAL</subject><subject>CONTROLLING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>FUNCTIONAL ELEMENTS OF SUCH SYSTEMS</subject><subject>METALLURGY</subject><subject>MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS</subject><subject>PHYSICS</subject><subject>REGULATING</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><subject>TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPB0dXPzCXX1C1EIDHX08QyJVAgIcnXxdA7x9PdTcHEN83R2VXD0c1HAp8zXNcTD34WHgTUtMac4lRdKczMoubmGOHvophbkx6cWFyQmp-allsR7BRgZGBkZGJoamRo4GhOlCAConi20</recordid><startdate>20220121</startdate><enddate>20220121</enddate><creator>HUANG LAN</creator><creator>TAKEHARA TERUMI</creator><creator>OKUBO AKIRA</creator><creator>KIYOTAKI KAZUO</creator><creator>HOCHIN TERUHISA</creator><creator>MORIMOTO MITSURU</creator><scope>EVB</scope></search><sort><creationdate>20220121</creationdate><title>EFFLUENT QUALITY PREDICTION DEVICE AND EFFLUENT QUALITY PREDICTION METHOD</title><author>HUANG LAN ; TAKEHARA TERUMI ; OKUBO AKIRA ; KIYOTAKI KAZUO ; HOCHIN TERUHISA ; MORIMOTO MITSURU</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_JP2022015250A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; jpn</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>CHEMISTRY</topic><topic>COMPUTING</topic><topic>CONTROL OR REGULATING SYSTEMS IN GENERAL</topic><topic>CONTROLLING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>FUNCTIONAL ELEMENTS OF SUCH SYSTEMS</topic><topic>METALLURGY</topic><topic>MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS</topic><topic>PHYSICS</topic><topic>REGULATING</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><topic>TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE</topic><toplevel>online_resources</toplevel><creatorcontrib>HUANG LAN</creatorcontrib><creatorcontrib>TAKEHARA TERUMI</creatorcontrib><creatorcontrib>OKUBO AKIRA</creatorcontrib><creatorcontrib>KIYOTAKI KAZUO</creatorcontrib><creatorcontrib>HOCHIN TERUHISA</creatorcontrib><creatorcontrib>MORIMOTO MITSURU</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HUANG LAN</au><au>TAKEHARA TERUMI</au><au>OKUBO AKIRA</au><au>KIYOTAKI KAZUO</au><au>HOCHIN TERUHISA</au><au>MORIMOTO MITSURU</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>EFFLUENT QUALITY PREDICTION DEVICE AND EFFLUENT QUALITY PREDICTION METHOD</title><date>2022-01-21</date><risdate>2022</risdate><abstract>To eliminate abnormal data included in time-series data from a plurality of sensors at a water treatment site.SOLUTION: In an effluent quality prediction device (100), a pre-processing unit (103a) performs filtering to a learning dataset, which is a portion of learning data for a first prescribed time in learning data. A model generation unit (103c) generates a prediction model for predicting effluent quality data a prescribed time after the learning dataset. Meanwhile, an effluent quality prediction unit (105) applies the prediction model to the latest data stored in a latest data database (104) and predicts effluent quality data.SELECTED DRAWING: Figure 1
【課題】水処理場の複数のセンサからの時系列データに含まれる異常データを除去する。【解決手段】放流水質予測装置（１００）において、前処理部（１０３ａ）は、学習データの中の第１の所定時間分の学習データを学習データセットとし、学習データセットにフィルタ処理を行う。モデル生成部（１０３ｃ）は、学習データセットから所定時間後の放流水質データを予測するための予測モデルを生成する。また、放流水質予測部（１０５）は、直近データデータベース（１０４）に記憶される直近データに、予測モデルを適用して放流水質データを予測する。【選択図】図１</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING CHEMISTRY COMPUTING CONTROL OR REGULATING SYSTEMS IN GENERAL CONTROLLING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES FUNCTIONAL ELEMENTS OF SUCH SYSTEMS METALLURGY MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS PHYSICS REGULATING SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE |
| title | EFFLUENT QUALITY PREDICTION DEVICE AND EFFLUENT QUALITY PREDICTION METHOD |
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