Research And Implementation Of Coal Mine Microseismic Time Series Big Data Storage And Management System Based On HBase

With the continuous development of my country’s industrialization,the mining intensity of coal mine resources is relatively high,and the frequency of microseismic events in coal mines caused by this has also increased year by year,which has caused harm to the personal safety of coal mine industry workers.A large number of studies have been carried out at home and abroad on the monitoring and analysis of microseismic events in coal mines.However,under the existing system,there are still some problems to be solved in the storage of coal mine microseismic time series big data and the management of microseismic event data,which need further research and exploration.At present,it is a common monitoring method to widely deploy microseismic monitoring sensors in coal mining areas.However,the widespread application of this method also raises some questions.First of all,a large amount of microseismic timeseries waveform data generated by large-scale deployment of monitoring sensors cannot be efficiently stored and managed.Secondly,the current classification of microseismic events still adopts the method of manual judgment by experts.Although this method guarantees the accuracy of event classification to a certain extent,the efficiency is low.This thesis researches and realizes the coal mine microseismic timing big data storage and management system based on HBase.The system has six core modules,including login module,waveform data storage module,microseismic event calculation module,microseismic event classification module,microseismic event query module and Station condition module.Among them,the login module realizes user identity verification and user authority control through the association among users,roles,and authority.The waveform data storage module mainly includes building index files,multi-threaded concurrent alignment and reading,and storage optimization modules.In order to further improve the storage efficiency of microseismic waveform time series big data,a coal mine microseismic time series big data based on HBase and Netty is proposed in this module.The storage framework includes the HBase data table structure,pre-partition strategy and primary key optimization strategy designed according to the characteristics of the microseismic waveform time series data.Redis’ s data forwarding middleware is distributed and deployed,providing asynchronous processing capabilities and reasonable resource allocation for stored procedures.The storage optimization framework solves the problem of high concurrent storage and effectively improves the storage efficiency of microseismic waveform data.The coal mine microseismic timing data calculation module includes microseismic wave identification calculation,microseismic arrival time positioning calculation,microseismic event magnitude calculation and microseismic event energy calculation.This module provides data support for subsequent microseismic event classification and microseismic event query modules.The microseismic event query module uses multi-dimensional query conditions to realize accurate query of coal mine microseismic events,including multi-dimensional query limiting conditions such as damage type query,energy range query,event type query,damage degree query,and time range query.The microseismic event classification module includes a data preprocessing submodule and a microseismic event automatic classification submodule,in which the data preprocessing part supplements and organizes the values ??of the three variables of microseismic magnitude,microseismic energy,and microseismic occurrence location.The microseismic event automatic classification sub-module implements the microseismic event classifier based on the random forest algorithm,and tunes key parameters during the training process.The accuracy of the classification of microseismic events is verified by experiments to be 84.39%,which can effectively provide technical support for the safety management of coal mine microseismic events.The station working condition module includes the station working condition monitoring function and the station working condition information query function.Station working condition monitoring function: the system sets up scheduled tasks to regularly monitor station working condition information.Query station working condition information function: users can query the qualified station working condition information according to the query constraints,and display the results to the user in the form of icons and tables.The system adopts B/S front-end and back-end separation architecture design.Among them,the underlying storage medium adopts HBase distributed database to provide raw data support for the system as a whole.The calculation results of microseismic events are stored in the relational database My SQL to provide data support for the big data visualization platform.The web backend is provided by the Spring Boot framework based on Java language Construction,the algorithm part is written based on the Python language,the front-end part adopts ECharts and Boot Strap framework,and all data services and databases are deployed on the cloud server to provide online services.