Research On Intelligent Adjustment Height Of Shearer Drum Based On Adaptive Fuzzy Reasoning Petri Net

The coal industry has always been an important traditional energy industry in China,as well as an important part of the national economy,so the intelligent process of the coal industry is closely related to the intelligent process of China’s national economy and society.One of the key indicators of intelligent mining is that the shearer needs to adjust the drum cutting height adaptively with the change of rock strata during operation,namely the drum intelligent elevation technology.Due to the complex working face environment and the nonlinear and fuzzy characteristics of drum regulation,it is extremely difficult to construct the heightening controller,Based on the in-depth analysis of shearer drum intelligent heightening technology at home and abroad,a fuzzy reasoning Petri net model is proposed combining fuzzy reasoning and fuzzy Petri net,and this model is applied to shearer drum intelligent heightening technology.The main contents include:(1)Taking the drum intelligent heightening technology as the research purpose,a fuzzy reasoning Petri net model is proposed to apply the shearer rolling simplification intelligent regulation by studying fuzzy reasoning and fuzzy Petri net expression of drum heightening control rules.In this model,membership information of variable state can be obtained by fuzzifying variables,and then more abstract conclusions can be obtained by inference operation of membership information,and the exact value of conclusion can be output by defuzzifying the conclusion,The model can reflect the nonlinearity and attribute mapping of adaptive system and can adjust parameters adaptively to improve the accuracy of conclusion.(2)In order to improve the modeling efficiency and output accuracy of fuzzy inference petri net model,this paper proposes an autonomous model generation method and constructs model adaptive algorithm and inference algorithm,so as to build adaptive fuzzy inference Petri net.In this method,fuzzy changes are constructed by building fuzzy sets on the domain and inference changes are constructed by extracting fuzzy rules of fuzzy sets on the domain.The adaptive algorithm continuously optimizes the threshold and confidence parameters of the model to reduce the output error of the model.Then,the self-generated model method is algorithmized to improve the modeling speed,and their algorithm flow is given together with adaptive and inference algorithms.(3)In order to further verify the feasibility and effectiveness of adaptive fuzzy inference Petri net for intelligent drum raising,this paper adopts actual production data of coal mine to build an intelligent drum raising model and verify it.The data is taken from the real time monitoring data of 43101 fully mechanized mining face in Yujialiang Coal Mine.The results show that the adaptive fuzzy inference Petri net proposed in this paper can effectively realize the intelligent energy adjustment of coal mining,and the self-generated model method can generate the adaptive fuzzy inference Petri net model more efficiently.