Research On Attitude Parameters Prediction And Correction Of Roadheader

With the continuous development of China’s economy and society,the demand for railway,highway and other tunnel construction is increasing.A large number of construction requirements also put forward higher requirements for tunnel construction quality.Roadheader method has been widely used because of its safety,efficiency and other characteristics.In the process of roadheader tunneling,the roadheader machine operation mainly depends on the driver’s experience.At the same time,due to complex geological conditions and other factors,the roadheader machine is prone to misalignment,that is,the roadheader attitude deviates from the design tunnel axis.When the roadheader machine deviates from the deviation limit,it often depends on the driver’s experience,and the parameter setting has great uncertainty.At the same time,with the development of intelligence,the roadheader machine system is also undergoing changes and developing towards intelligence and science.Based on a Yellow River Tunnel Project in Jinan and the actual roadheader construction data,this paper takes the prediction of roadheader attitude parameters and intelligent deviation correction as the research object,mainly including the following work:(1)Starting from the tunneling process of roadheader construction,the influencing factors of roadheader posture are summarized.Establish the mechanical model of roadheader,and analyze the tunneling parameters related to roadheader attitude according to the actual collected construction data,so as to provide data basis for subsequent research.(2)Based on the characteristics of construction data,a prediction model framework of roadheader attitude parameters is proposed.Firstly,preprocess the data,mainly including: missing value processing,nonworking state data elimination,abnormal value processing and data normalization.Then,based on Pearson correlation analysis and random forest,the input features are further screened from the preliminary screening data.Finally,the prediction model of attitude parameters is constructed,the effect is verified based on the actual construction data,and compared with other models to verify the effectiveness.(3)A method for recommending operating parameters for roadheader attitude correction is proposed.The research purpose is to give the recommended value of operating parameters based on the deviation correction amount of each ring in the process of roadheader attitude correction.Firstly,process the collected construction data,separate the data of the deviation correction section,and calculate the deviation correction value of each ring in the tunneling process of the deviation correction section.Secondly,the construction data of the deviation correction section are discretized,the Apriori association rule algorithm is used to find the association rules between the operation parameters and the deviation correction amount.Then,the SVP-BP deviation correction prediction model is constructed,and the effectiveness of the prediction model is verified.Finally,for a given deviation correction amount of a certain ring,through iterative prediction in the corresponding operation parameter interval,a group of operation parameters with the predicted value closest to the expected deviation correction amount is used as the recommended value of the operation parameters of this ring.(4)Roadheader management system module development.Summarize the development process of intelligent algorithm module,and develop a roadheader attitude prediction and correction management system module based on the front-end and back-end separation technology.The overall design and implementation of the system function and structure are carried out,and a No SQL database is used for data storage,which improves the system performance and the scalability of storage space.