Hard rock tunnel boring machine (TBM) boring control parameter intelligent decision method and system

Abstract

The present invention provides a hard rock TBM boring control parameter intelligent decision method and system. The method comprises the establishment of an engineering database, the establishment ofa rock-machine mutual feedback model and an intelligent control decision model based on the engineering database, the model parameter output, display and updating, an automatic / manual boring parametercontrol method and a model self-learning and self-updating algorithm, and the system comprises an engineering database unit, a rock-machine mutual feedback model unit, an intelligent control decisionmodel unit, a model parameter output real-time display module, an automatic / manual boring parameter control module, and a model self-learning and self-updating unit. According to the present invention, the rock-machine mutual feedback model predicts the state parameter of a current surrounding rock and senses the boring environment of a TBM real-timely according to the operation parameters of theequipment, the intelligent control decision model predicts an optimal control boring parameter according to the current boring environment, and adjusts the current boring parameter timely according to the control boring parameter to adapt to the current boring environment, thereby keeping the safe, efficient and stable boring.

Description

technical field [0001] The invention relates to the technical field of intelligent control of tunnel excavation equipment construction, in particular to an intelligent decision-making method and system for hard rock TBM excavation control parameters. Background technique [0002] Hard rock tunnel boring machine (hereinafter referred to as hard rock TBM) is a large-scale high-tech construction equipment specially used for excavating rock tunnels and underground passages. In the traditional construction method, the driver in the main control room evaluates the state parameters of the surrounding rock through the slow trial excavation process, and then repeatedly adjusts the excavation parameters until the excavation parameters remain stable. On the one hand, such an operation method will consume a lot of construction time due to continuous slow trial excavation and repeated adjustment of excavation parameters; The parameters cannot adapt to the current excavation environment,...

AI Technical Summary

Problems solved by technology

[0003] In view of the existing operation mode, only the main driver’s artificial experience can be used to guess the state parameters of the surrounding rock, and the time-consuming and labor-intensive, abnormal wear of the TBM cutter, the decline in the rock-breaking performance of the cutterhead system, etc. To solve technical problems such as key component damage and downtime, the present invention proposes an intelligent decision-making method and system for hard rock TBM tunneling control parameters, which uses data mining and machine learning technology to predict tunneling parameters, and can adapt to different environments through self-learning and self-updating. Rock, TBMs with different diameters, different performances, and the use of the same TBM in different stages of the life cycle

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