Customer service risk evaluation system modeling method and system based on artificial intelligence

The invention discloses a customer service risk evaluation system modeling method and system based on artificial intelligence. The method comprises the steps of data collection, data preprocessing, appeal risk evaluation system construction, power failure risk evaluation system construction, load ri...

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Hauptverfasser:	YU HONGFU, YANG XIAOYAN, SU HONGYU, MO LIANGYUAN, ZHU DI, WANG LICHAO, HUANG KAI, WEI LIUMU, WU CHANGSHAN, LI LIN, FU HUA, ZHU NING, LIU NA, LIAO QIUYUAN, HUANG YUSHAN
Format:	Patent
Sprache:	chi ; eng
Schlagworte:	CALCULATING CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONVERSION OR DISTRIBUTION OF ELECTRIC POWER COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING ELECTRICITY GENERATION PHYSICS SYSTEMS FOR STORING ELECTRIC ENERGY 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 a customer service risk evaluation system modeling method and system based on artificial intelligence. The method comprises the steps of data collection, data preprocessing, appeal risk evaluation system construction, power failure risk evaluation system construction, load risk evaluation system construction, value added service risk evaluation system construction and customer service risk evaluation system construction. The invention relates to the technical field of power demand customer service risk prediction, in particular to a customer service risk evaluation system modeling method and system based on artificial intelligence, and the method comprises the steps: carrying out the multi-target data generation through employing a variational automatic encoder, and optimizing the data quality; multiple artificial intelligence models are constructed for prediction, so that prediction errors are reduced; according to the method, prediction results of the three models are superposed, fle