Research And Implementation Of Intelligent Optimization Technology Of Shield Machine Parameters Based On Deep Learning

In recent years,artificial intelligence-related technologies have developed rapidly,and artificial intelligence technology has been given new impetus in all walks of life.However,as one of China's major supporting industries,the infrastructure industry is rarely integrated with artificial intelligence.And with the development of urbanization,the construction of shield tunnels is in full swing,but the research on the relationship between the parameters of shield tunnels has not kept up with the development of the times,the parameter setting of the shield machine is still at the stage of manual experience control.Secondly,during the operation of the shield machine,a large amount of construction history information under different terrain and geological characteristics is collected,which is usually only used for statistics and display,and has not been fully analyzed and used.Therefore,the research and implementation of a data processing system for rapid processing and timely feedback of construction data is particularly urgent and important for the construction of underground projects,especially major underground projects.Based on this purpose,this thesis investigates many algorithms related to parameter prediction,such as polynomial regression,random forest,BP neural network,etc.Through the investigation and experiment of these three algorithms,this thesis found that the BP neural network model has higher accuracy and better effect.What's more,during the implementation of the model,the related problems of data preprocessing were noticed.For example,the parameters collected by the shield machine are up to several hundred dimensions.If the models are used,the efficiency is not high,and it may not be accurate.And the collected data often has missing data and outliers,if it is not processed,it will easily affect the accuracy of the model.Aiming at this problem,this thesis investigates different data processing methods to sort out a suitable data pre-processing process.The process is divided into two steps,one is to perform feature selection through data analysis,and the other is to improve the training effect of the model through data smoothing.Experiments have found that this process does help improve the accuracy of the model.After the algorithm is implemented,the corresponding system bearer is needed.In this thesis,considering the data characteristics of the shield machine and the need for system scalability in the future,a microservice architecture shield machine parameter prediction system is constructed.Before implementing the system,detailed requirement analysis,architecture design and database design were carried out.Finally,a shield machine parameter prediction system integrating the four functions of system management module,algorithm module,parameter prediction module and system operation and maintenance module was realized.After careful testing,the system was found to meet design requirements.