Strong mine pressure risk prediction method and device

Strong mine pressure risk prediction method and device

The invention discloses a strong mine pressure risk prediction method and device. The method comprises the following steps: acquiring multiple groups of historical high mine pressure danger data; describing the section of the to-be-excavated working face by using the historical attribute indexes to...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: TIAN YINSU, ZHANG LONG, LI XUANLIANG, ZHANG CHUANJIU, SONG XIAOFEI
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
ELECTRIC DIGITAL DATA PROCESSING
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 a strong mine pressure risk prediction method and device. The method comprises the following steps: acquiring multiple groups of historical high mine pressure danger data; describing the section of the to-be-excavated working face by using the historical attribute indexes to obtain strong mine pressure danger data of the section of the to-be-excavated working face; determining the similarity between a first data sequence corresponding to the strong mine pressure danger data and a second data sequence corresponding to historical strong mine pressure danger data; selecting historical strong mine pressure danger data corresponding to the second data sequence of which the similarity is greater than a similarity threshold as training data; training a predetermined neural network by using the training data to obtain a strong mine pressure danger level prediction model; and determining a strong mine pressure danger level corresponding to the strong mine pressure danger data through a strong m