A method and system for intelligent optimization of TBM control parameters based on rock characteristics

Abstract

This invention discloses a method and system for intelligently optimizing TBM control parameters based on rock cutting characteristics, belonging to the field of parameter control technology. The method includes: using real-time image acquisition and processing technology to denoise, enhance, and scale the ejected rock cuttings, acquiring image data of rock cutting particles and calculating particle size. Utilizing the gradation characteristics of the rock cuttings (such as coarsness index CI, uniformity coefficient Cu, and curvature coefficient Cc) and geological parameters, a specific energy analysis model based on machine learning is trained to predict the specific energy under the current rock cutting condition. Based on the specific energy prediction results, the thrust and torque control parameters of the TBM are optimized to achieve the lowest possible specific energy, thereby improving tunneling efficiency and reducing energy consumption. This method, through adaptive closed-loop control, can dynamically adjust the TBM's operating parameters according to real-time rock cutting characteristics and geological conditions, achieving a precise and efficient tunneling process and ensuring continuous optimization and stable operation under complex geological conditions.

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