A chaos theory and integrated learning-based coal mining machine cutting height prediction system

A technology of chaos theory and integrated learning, applied in the field of signal processing, can solve problems such as low precision and slow speed

Inactive Publication Date: 2019-06-18
ZHEJIANG UNIV
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Problems solved by technology

[0003] In view of the urgent demand for the automatic prediction of shearer cutting height under the complicated working conditions of the current coal mining industry, the purpose of the present invention is to provide a high-precision shearer cutting height prediction system based on chaos theory and integrated learning, in order to overcome the The shortcomings of manual adjustment, such as low precision and slow speed, are of great significance to the realization of green, economical, efficient and refined coal mining

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  • A chaos theory and integrated learning-based coal mining machine cutting height prediction system
  • A chaos theory and integrated learning-based coal mining machine cutting height prediction system
  • A chaos theory and integrated learning-based coal mining machine cutting height prediction system

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Embodiment 1

[0053] reference figure 1 , figure 2 , A shearer cutting height prediction system based on chaos theory and integrated learning, including data preprocessing module 5, chaotic characteristic calculation module 6, phase space reconstruction module 7, shearer cutting height limit gradient boosting (eXtreme Gradient Boosting , XGBoost) model modeling module 8 and shearer cutting height limit gradient boosting (eXtreme Gradient Boosting, XGBoost) model prediction module 9. The field data acquisition sensor 1, the database 2, the shearer cutting height prediction system based on chaos theory and integrated learning 3, and the result display module 4 are connected in sequence. The field data acquisition sensor 1 is connected to the historical cutting height of the shearer The signal is collected, and the data is stored in the database 2. The database 2 contains historical shearer cutting height data to provide data support for the shearer cutting height prediction system 3 based on ...

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Abstract

The invention discloses a chaos theory and integrated learning-based coal mining machine cutting height prediction system. The system is used for predicting the cutting height of the coal mining machine. The system comprises a data preprocessing module, a Chaotic characteristic calculation module, a Phase space reconstruction module, a coal mining machine cutting height extreme gradient boosting (eXtrome Grade Boosting) modeling module, and a coal mining machine cutting height extreme gradient boosting (eXtrome Grade Boosting, XGBoost) model prediction module. The chaotic characteristics of the cutting height sequence of the coal mining machine can be mined, and the cutting height of the coal mining machine can be quickly and accurately predicted.

Description

Technical field [0001] The invention relates to the field of signal processing, the field of chaos theory and the field of integrated learning, in particular to a shearer cutting height prediction system that combines chaos theory and integrated learning. Background technique [0002] Currently, countries all over the world are striving to develop their economies, and energy demand continues to grow. Coal accounts for a huge proportion of the world's primary energy consumption. However, safe coal production has always been an important factor restricting coal production. Therefore, it is very important to vigorously improve the automation, mechanization, and information level of the coal mining process. As the key equipment of the coal mining face, the shearer is of great significance in the coal mining production process. The adjustment and control of the height of the cutting drum is the key process of manual operation of the shearer when the shearer is mining coal in the und...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N7/08G06N20/20E21C35/24
Inventor 徐志鹏古有志刘兴高张泽银
Owner ZHEJIANG UNIV
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