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Method for predicting coal mixing indexes based on neural network algorithm

A neural network algorithm and coal blending technology, which is applied in the field of predicting coal blending indicators based on neural network algorithms, can solve the problems of heavy workload of staff and low work efficiency of operating personnel, and achieve the effect of improving work efficiency and reducing workload

Pending Publication Date: 2018-11-06
济南汇通远德科技有限公司
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Problems solved by technology

[0004] At present, the workload of the staff in predicting the coal blending index is heavy, and the work efficiency of the operators is low when operating the complex system

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  • Method for predicting coal mixing indexes based on neural network algorithm
  • Method for predicting coal mixing indexes based on neural network algorithm

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Embodiment Construction

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0025] see Figure 1~2 , in an embodiment of the present invention, a method for predicting a coal blending index based on a neural network algorithm includes the following steps:

[0026] (1) The basic data module sends data information to the predicted coal blending index module in the form of json data format transmission; the basic data module includes an input unit and a sending unit, and the sending unit sends data information to the database module, and...

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Abstract

The invention discloses a method for predicting coal mixing indexes based on a neural network algorithm, which comprises the steps that (1) a basic data module sends data information to a coal mixingindex prediction module; (2) the coal mixing index prediction module sends the data information to a database module; (3) the database module processes the data through a data processing mechanism andthen stores the processed data into a storage unit; (4) the coal mixing index prediction module sends a data receiving request to the database module; (5) the data processing mechanism encodes the data of the storage unit and then sends the encoded data to the coal mixing index prediction module; (6) a receiving unit sends an analysis request to the neural network algorithm and sends the data tothe neural network algorithm for analytical calculation after being granted; (7) the neural network algorithm completes the work of predicting the coal mixing indexes; and (8) the coal mixing indexesare outputted. The method disclosed by the invention effectively relieves the workload of workers when predicting the coal mixing indexes, and improves the working efficiency of operators when operating the complicated system.

Description

technical field [0001] The invention relates to a method for predicting a coal blending index, in particular to a method for predicting a coal blending index based on a neural network algorithm. Background technique [0002] Blended coal is a blended coal that is processed by blending several different types of coals with different properties in a certain proportion. Although it has some characteristics of its constituent coals, its comprehensive properties have changed, and it is actually a new "coal type" artificially processed. The basic principle of power coal blending is to make use of the differences in the properties of various coals, "learn from each other" and give full play to the advantages of each coal type, and finally make the blended coal achieve "best performance" in terms of comprehensive performance to meet user requirements . [0003] Neural network algorithm, logical thinking refers to the process of reasoning according to logical rules; it first conver...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/02
CPCG06Q10/06393G06Q50/02
Inventor 马志伟周世军徐传雪杨建光
Owner 济南汇通远德科技有限公司
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