Data Processing And Analysis System For Mining Subsidence Mobile Deformation Based On Cloud Computing

With the rapid development of new technologies and methods such as cloud computing,quantum computing,machine learning,neural networks,genetic algorithms,nuclear correlation vector machines,et al,deformation monitoring technology is facing an update.The research of cloud-based mining subsidence deformation monitoring is a digital 1.An important part of smart mine construction.After coal is mined,it will cause movement and deformation of the rock layer and the surface.This movement and deformation will have a certain impact on the safe mining of mineral resources,the natural environment,and daily life of residents.Irreversible changes in the environment,damage to structures and even destruction.Therefore,it is very important to process the measured data in time during mining to grasp the movement and deformation of the surface and analyze its movement and deformation.Studying the establishment of a data processing system on a cloud platform is of great significance for real-time processing of subsidence monitoring data on the cloud,and timely display and sharing of achievement data.In view of this,this article studies the above problems,the main contents and results are:1.This article is based on the classic probability integral method model,using two probability integral method parameter selection models-using the modular vector method and the extreme gradient lifting tree algorithm(XgBoost)to find the fitting parameters and prediction value of the 1222(1)working face of Zhuji East Mine,and the predicted results obtained by the two methods are analyzed and compared with the measured values.Among them,the fitting error of the sinking value of the trend line using the modular vector method is-65.0??46.0mm,and the fitting medium error is 24.9mm;the fitting error of the sinking value of the trend line is-54.0?+42.0mm,the fitting medium error is 21.7mm.Using the extreme gradient lifting tree algorithm combined with the sinking coefficient as an example,this article was established a probability integral selection parameter model,and the accuracy and reliability of the settlement results were tested.It can be seen from the test results that the maximum absolute error calculated by the sinking coefficient q is-0.09 and the maximum relative error is-10.7%.2.This paper designs and implements a cloud platform-based mining subsidence deformation monitoring data processing and analysis system,and improves the existing software processing system functions and database systems,and builds a mining subsidence deformation monitoring cloud platform from the overall design and function realization.The system will provide users with 4 major functions:user management,project file management,online data processing and data mapping.It consists of 7 parts:the approximate position of the observing station,observation data,movement and deformation,drawing graphics,obtaining parameters,statistical reports,and system users.3.Taking Guqiao South Mine 1613(1)working face as research object,use mining subsidence deformation monitoring cloud platform to process mobile deformation value data,analyze deformation law,and predict mining subsidence.The predicted results show that the relative error of subsidence and horizontal movement in the middle of the sinking basin is small.The approximate accuracy of the edge part is slightly lower than that of the middle part,and the fitting effect of sinking is slightly worse than that of horizontal movement fitting.Among them,the error in the fitting of the sinking direction is 53mm,the error in the fitting of the horizontal movement fitting is 97mm,the error in the fitting of sinking is 61mm,and the error in the fitting of horizontal movement is 75mm.Figure[56]table[12]reference[105]...