Intelligent Prediction Methods Of The Coal Mining Face Pressure Based On Granular Computing

China is a major coal producer in the world,and the coal industry has provided an important guarantee for the rapid development of China’s economy.However,the safety issues of mine operations are always major hidden dangers for the development of the coal industry.Among a variety of coal mine accidents,the roof accident ranks the first place.Therefore,the applications of big data and artificial intelligence technology in the construction of intelligent mine show very important practical significance for solving practical safety production problems.How to use big data and artificial intelligence technology to predict the coal mining face pressure to ensure the safety,economy,and efficient mining of coal mines is the top priority of current research.However,compared with traditional numerical modeling methods which aim to pursue the modeling accuracy,in big data era,how to mine the inherent information association through data modeling,so as to develop a strategy of supporting decision making which is more conforming to human thinking and cognition,becomes a challenging problem in data mining and data analytics.The main purpose of this thesis is to introduce Granular Computing(GrC)to traditional data analysis modeling,thus to establish a novel granular ore pressure prediction model.The thesis includes the following main research contents:(1)Granular subset selection based on the ore pressure sequencesIn practical data analysis area,a commonly encountered situation can be described as follows:due to the large scale of data size,the computational complexity of data modeling increases in an index form.In the meanwhile,some data elements in the data set have less impact on the integral characteristics of the data set.Therefore,to effectively reduce the computational complexity in system modeling,it is considered to use some subsets of data to represent the main features of the entire data set.In addition,in order to make up for the performance loss which is caused by system modeling based on the subsets of data,the modeling accuracy can be ensured to a certain extent by introducing granular computing into data modeling and analysis.This thesis designs a granular model for data analyzes based on the optimized subsets of data,which can help with the data analytics from a new perspective of granular computing.The experimental results indicate that the proposed method can effectively reduce the computational complexity and improve the accuracy of system modeling.(2)Construction of spatio-temporal information granules based on the ore pressure sequencesThe ore pressure sequence obtained by the integrated working surface stent monitoring system are not only with time relationships,but also with spatial relationships because of the effection of the spatial dynamic changes of the coal seam structure.In this thesis,the spatio-temporal association characteristics of the ore pressure data are fully considered.Firstly,the tightly associated bracket is obtained by using the maximum information coefficient method.Then,the spatio-temporal characteristics are extracted by combining the convolutional neural network with the bidirectional long-short-term memory(BiLSTM),and the attention mechanism is involved to improve the entire performance of the model,so as to obtain the sequence with spatio-temporal features.Finally,the description and characterization of a new ore pressure data is established by constructing the spatio-temporal information granules.(3)The prediction of ore pressure sequence based on granular computingIn order to further improve the prediction performance of ore pressure,this thesis proposes a new framework of granular ore sequence prediction model.By designing different granular optimization allocation strategy,a collection of interval-valued information granules with semantic characteristics performs as the outputs of system modeling.By taking the information granules as the basic units of data analytics,the problem of dynamic prediction of mine roof pressure can be further solved.As the experimental results shown,the ground pressure prediction based on granular computing can effectively predict a collections of reasonable confidence intervals,which have higher reliabilities than traditional parameter numerical prediction models.