| The 13 th Five-Year Plan for the development of the coal industry clearly pointed out that the coal industry should continue to deepen reforms,strengthen coal integration innovation,and promote the integrated application of modern information technologies such as the Internet of Things,big data,and cloud computing in the coal industry.Under the new situation,the intelligent construction of coal preparation plants has been continuously explored and advanced,and it has found that there are still difficulties in equipment,technology,technology and management.At the same time,it has exposed the remaining problems in the development of information technology,Hindering its rapid advancement.Aiming at the problems of repeated collection of coal preparation process data,redundant data and low utilization rate in the development of coal preparation plants,this paper first sorted out and explained the basic data of coal preparation production according to the source of data information.And Separately classified the coal preparation production information based on the production link and the collection frequency.Then,this paper studies the data collection methods of different sources and the data exchange method between the data platform and different demanding parties,and designs general data collection rules and methods.It also uses Web API technology to design the data transmission interface between the data platform and different demanding parties,which provides basic data support for the subsequent coal preparation production intelligence and information fusion.This paper studies the data storage structure,strategy and security of the coal preparation standard data platform.Using outlier zeroing and the compression of Spinning Door Transformation with dynamic deviation to preprocess online data,using national and industry standards as the norm,it stores production data in the form of dictionary tables and data tables,and formulate coding rules and storage strategies.At the same time,data security and backup issues are considered in the data storage.On this basis,this paper builds a coal preparation standard production data platform.On the basis of the above research,aiming at the problems such as the complicated mechanism of the jigging process and the difficulty of measuring and characterizing the looseness,by analyzing the influence of the jig sorting process and the operating parameters,an LS-SVM looseness soft-sensing model based on buoy jump height,raw coal feed rate,jig frequency,damper period and other parameters is proposed.The simulation prediction results show that the predicted value of the bed looseness has a high degree of agreement with the actual value,and the correlation coefficient reaches 0.9705,which basically meets the needs of jigging production status evaluation.At the same time,this paper proposes an information fusion framework aiming at evaluating and optimizing the sorting state,and based on the correlation analysis of the sorting process state and influencing factors,collecting and summarizing the experience of experts and operators,using information rules such as production rules for decision fusion Means,to achieve the state evaluation of the jigging process,and give specific strategies to optimize the production state.Finally,the SQL Server database and Visio Studio 2017 development platform were used for the overall design and implementation of the jigging process detection information fusion system.This paper has 32 figures,32 tables and 72 references. |
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