Aquatic breeding environment monitoring management system and method

The invention relates to the technical field of data processing, in particular to an aquatic breeding environment monitoring management system and method.The method comprises the steps that all historical reference indexes are classified according to an environment influence factor set, and matched...

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Bibliographische Detailangaben
Hauptverfasser:	WANG DAHAI, LIU WEI, LI WENJING, ZHU CHUNLIANG, DUAN CHENGYI, OU YUNJIANG, FAN BOJIANG
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	AGRICULTURE ANIMAL HUSBANDRY ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVEREDIN A SINGLE OTHER SUBCLASS CALCULATING CARE OF BIRDS, FISHES, INSECTS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING FISHING FORESTRY HUMAN NECESSITIES HUNTING MEASURING MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR NEW BREEDS OF ANIMALS PHYSICS REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR TARIFF METERING APPARATUS TESTING TRAPPING
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Beschreibung
Zusammenfassung:	The invention relates to the technical field of data processing, in particular to an aquatic breeding environment monitoring management system and method.The method comprises the steps that all historical reference indexes are classified according to an environment influence factor set, and matched index categories are obtained; classifying the historical reference indexes of the matched index categories according to the frequency values and the linear offset degrees to obtain two feature categories, and obtaining adjustment parameters by combining the frequency value of each historical reference index in the matched index categories; and constructing a prediction linear regression equation according to the reference index data variable quantity and the adjustment parameter, performing nonlinear conversion on the prediction linear regression equation to obtain a prediction probability value of each prediction reference index, and performing weighting processing on all the prediction probability values to obta