Intelligent product quality risk monitoring method and system based on convolutional neural network

Intelligent product quality risk monitoring method and system based on convolutional neural network

The invention discloses an intelligent product quality risk monitoring method and system based on a convolutional neural network. The intelligent product quality risk monitoring method based on the convolutional neural network comprises the following steps of data acquisition and preprocessing, robo...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: WANG JING, LIU GEFEI, WANG YANFENG, REN QIAN, ZHANG XIN, GAO, YIN, CAI XIAOZHAN, XU YINGCHENG
Format: Patent
Sprache:chi ; eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:The invention discloses an intelligent product quality risk monitoring method and system based on a convolutional neural network. The intelligent product quality risk monitoring method based on the convolutional neural network comprises the following steps of data acquisition and preprocessing, robot dog appearance damage inspection, robot dog working state inspection and robot dog interaction inspection. According to the invention, the quality risk data of the intelligent robot dog is obtained and preprocessed; the appearance damage condition of the intelligent robot dog is judged based on the convolutional neural network model of the appearance image of the intelligent robot dog, and early warning processing is carried out; performing analysis according to the exercise performance data to obtain an exercise index of the robot dog and performing early warning processing; the robot dog interaction index is obtained through analysis according to the robot dog instruction data, and early warning processing is c