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Shearer Cutting Height Prediction System Based on Chaos Theory and Integrated Learning

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

Inactive Publication Date: 2021-04-09
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
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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

Method used

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  • Shearer Cutting Height Prediction System Based on Chaos Theory and Integrated Learning
  • Shearer Cutting Height Prediction System Based on Chaos Theory and Integrated Learning
  • Shearer Cutting Height Prediction System Based on Chaos Theory and Integrated Learning

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Embodiment 1

[0053] refer to 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, and 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. On-site data acquisition sensor 1, database 2, shearer cutting height prediction system 3 based on chaos theory and integrated learning, and result display module 4 are connected in sequence. The signal is collected, and the data is stored in the database 2, which contains the historical shearer cutting height data to provide data support for the shearer cutting height prediction system 3 based on chaos theory and integrated learning, based on The results predicted by the shearer cutting height prediction ...

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Abstract

The invention discloses a shearer cutting height prediction system based on chaos theory and integrated learning, which is used to predict the cutting height of the coal shearer, including a data preprocessing module, a chaos characteristic calculation module, a phase space reconstruction module, and a mining machine. Coal machine cutting height limit gradient boosting (eXtreme Gradient Boosting, XGBoost) model modeling module and coal mining machine cutting height limit gradient boosting (eXtreme Gradient Boosting, XGBoost) model prediction module. The invention can excavate the chaotic characteristics of the cutting height sequence of the coal shearer, and quickly and accurately predict the cutting height of the coal shearer.

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 combined with chaos theory and integrated learning. Background technique [0002] At present, countries around the world are striving to develop their economies, and energy demand continues to grow. Coal accounts for a large proportion of the world's primary energy consumption. However, coal safety production has always been an important factor restricting coal production. Therefore, it is very important to vigorously improve the automation, mechanization and informatization 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 cutting drum height is the key process of manually controlling the shearer when the shearer is mining coal in the...

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Application Information

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Patent Type & Authority Patents(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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