Research On The Cooperative Speed Regulation Method Of Shearer And Scraper Conveyor Based On Rough Neural Network

The intelligentization of fully mechanized mining is an important direction for the development of intelligent mining technology in coal mines.The coordinated control of the shearer and the scraper conveyor is the premise to realize the intelligentization of fully mechanized mining equipment and the unmanned working face of fully mechanized mining.Based on the historical operation data of fully mechanized mining equipment,which contains rich cooperative control strategies and operation logic of shearer and scraper conveyor,this paper studies the cooperative speed regulation method of shearer and scraper conveyor.In the study of learning,a shearer-scraper conveyor cooperative speed regulation method based on rough neural network was designed.The main work and research results are as follows:(1)The key technology of shearer-scraper conveyor coordinated speed regulation is proposed.The cooperative working process of shearer-scraper conveyor is analyzed,combined with the background of big data intelligent mine,the basic idea of shearer-scraper conveyor cooperative speed regulation is determined,and the shearer-scraper conveyor collaboration based on rough nehral network is proposed.Speed regulation method,establish a coordinated speed regulation system framework,and lay the foundation for subsequent research.(2)Research on knowledge acquisition of cooperative speed regulation of shearer-scraper conveyor.Based on the knowledge acquisition of rough set theory,the parameters of the cooperative process monitoring of the shearer and the scraper conveyor are selected,the redundant features are removed from the various feature information,the key features are extracted,and the cooperative speed regulation of the shearer and the scraper conveyor is obtained.Minimal knowledge representation,build feature selection dataset,prepare for subsequent neural network learning.(3)Study on the learning method of shearer-scraper conveyor cooperative speed regulation neural network.Based on the deep LSTM neural network,a rough neural network was established to model the cooperative speed regulation process of the shearer and the scraper conveyor.The influence of different hyperparameters on the prediction accuracy of the cooperative speed regulation model was simulated and analyzed.Select and optimize,and compare and analyze the predictive ability of the optimized collaborative speed regulation model with other shallow learning algorithms.(4)On-site data verification of the shearer-scraper conveyor cooperative speed regulation method.In order to further verify the effectiveness of the shearer-scraper conveyor cooperative speed regulation method based on rough neural network proposed in this paper,the algorithm was verified by using the operation data of 43101 shearer and scraper conveyor in Yujialiang Coal Mine.It shows that the rough neural network algorithm proposed in this paper has higher accuracy than the neural network algorithm without rough set theory knowledge,and can better fit the cooperative working process of the shearer-scraper conveyor and realize the shearerscraper The plate conveyors work together and stably.The shearer-scraper conveyor speed regulation method based on rough neural network proposed in this paper provides a new solution for collaborative control of working face,and makes a useful exploration for the development of intelligentization of fully mechanized working face.The realization of low-carbon,intelligent and unmanned mining of coal mines has laid the foundation.