Research On Cooperative Control Method Of Ming Equipment Based On Coal Flow Measurement And Scraper Conveyor Load Prediction In Fully Mechanized Mining Face

Coal mine intellectualization is the core technical support for the high-quality development of the coal industry and the inevitable direction of the technological revolution and upgrading development of the coal industry.As one of the key technologies of coal mine intellectualization,the realization of fully mechanized mining intellectualization will greatly promote the development of coal mine intellectualization.The shearer of the fully mechanized face cuts and loads coal,and the scraper conveyor transports and unloads coal.The two cooperate to continuously transport the coal flow out of the fully mechanized face.Therefore,in order to realize the intellectualization of the fully mechanized face,the intelligent control must be carried out for the mining and transportation cooperation process of the fully mechanized face.However,the environment of fully mechanized face is bad,and the coupling mechanism of shearer and scraper conveyor is complex.The collaborative intelligent control of mining and transportation in fully mechanized face has become one of the main factors restricting the intellectualization of the face.To solve this problem,this dissertation relies on the sub topic "Integrated and demonstration of intelligent mining safety technology in large mining height coalface"(grant No.:2017YFC0804310)of the national key research and development plan project "research and development of safety technology and equipment for intelligent mining of coal mine",and takes the collaborative intelligent control method of mining and transportation of fully mechanized mining face as the research main line,focusing on solving the problem of on-line monitoring of coal flow in fully mechanized mining face For the load prediction of scraper conveyor and the formulation of intelligent control strategy for mining and transportation coordination of fully mechanized mining face,the intelligent control method for mining and transportation coordination of working face is verified by establishing a field data acquisition system.The specific contents are as follows:(1)The intelligent control model and framework of mining and transportation coordination in fully mechanized coal face are put forward.In view of the lack of intelligent control methods for mining and transportation coordination in fully mechanized face,this dissertation deeply analyzes the production process and load characteristics of coal flow system in fully mechanized face,determines the basic idea of mining and transportation coordination intelligent control in fully mechanized face,establishes the model and framework of mining and transportation coordination intelligent control in fully mechanized face,and gives the key problems and solutions of mining and transportation coordination intelligent control in fully mechanized face,Lay a foundation for follow-up research.(2)Research on on-line measurement method of coal flow in fully mechanized coal face.Aiming at the problem of dynamic real-time monitoring of coal flow in fully mechanized face under dark light environment,a coal flow measurement scheme based on speckle structured light binocular vision is proposed,and a coal flow measurement device is designed and built;The Inverse Gauss Newton algorithm is used for sub-pixel matching of the coal flow speckle image sub-area to obtain the coal flow contour point cloud data of the fully mechanized mining face.Combined with the tetrahedral grid structure method,the coal flow is calculated.Finally,the performance of the proposed system is verified by the coal flow measurement experiment.The results show that the proposed method and system can accurately and quickly measure the coal flow of the scraper conveyor.(3)Research on load forecasting method of scraper conveyor in fully mechanized mining face.In view of the inaccurate load prediction of the scraper conveyor caused by the scarcity of valuable samples and the lack of diversity of the scraper conveyor current data,by analyzing the mixing characteristics of the coal flow system in the fully mechanized face,an extended hybrid Petri net(ENPN)is proposed to build the process control ENPN model of the coal flow system in the fully mechanized face,and generate the scraper conveyor current data samples under sudden working conditions;A conditional Wasserstein antagonism generation network model based on LSTM is proposed to expand the valuable rare current data samples of scraper conveyor and establish a complete and sufficient current data set;Based on the rough idea,a rough radial basis function neural network RRBFNN model is proposed to build a scraper conveyor load prediction model.(4)Research on intelligent control strategy of mining and transportation coordination in fully mechanized coal face.In view of the lack of intelligent control strategy of coal flow system in fully mechanized coal face,a collaborative intelligent control method based on on-line coal flow measurement and scraper conveyor load prediction is proposed.In this method,firstly,the current intensification model is established to obtain the current component that reflects the real load of the scraper conveyor in the fully mechanized mining face;Then,in view of the strong nonlinearity and spatiotemporal correlation in the multi input parameters,a random attention capsule neural network RSCAN is proposed to extract the characteristics of the coal flow system in the fully mechanized face;Finally,considering the coal flow load and coal flow,the intelligent control model of coal flow system in fully mechanized coal face based on stochastic attention capsule neural network RSCAN is established and verified by simulation.The results show that the proposed load current prediction method and the mining and transportation coordinated speed regulation method can effectively realize the load prediction of the coal flow system in the fully mechanized face and the adaptive speed regulation of the shearer.(5)Field data validation of the intelligent control model for mining and transportation coordination in fully mechanized coal face.In order to further verify the rationality and feasibility of the intelligent control model of the coal flow system in the fully mechanized face proposed in this dissertation,relying on the equipment of the 52605 fully mechanized face in Daliuta coal mine,the coal flow data acquisition system in the fully mechanized face is constructed and the data acquisition and algorithm verification are carried out.The results show that the average error of scraper conveyor load prediction is 13.88%,and the maximum R-squared value of mining and transportation collaborative control is 0.9046;It shows that the current data generation method,the coal flow measurement method,the current load prediction method and the mining and transportation collaborative intelligent control method proposed in this dissertation are reasonable and feasible,and can effectively realize the autonomous collaborative operation of the coal flow and transportation system in the fully mechanized mining face.The intelligent control method proposed in this dissertation provides a new solution for the autonomous cooperative operation of key equipment in fully mechanized face,and lays a foundation for the development of intelligence and less people in fully mechanized face.