Intelligent prediction method for cutter suction dredger construction productivity

Abstract

The invention discloses an intelligent prediction method for cutter suction dredger construction productivity. The method comprises the steps of (1) collecting construction monitoring data of all parts of the dredger; (2) establishing a feature selection model influencing the productivity of the cutter suction dredger according to a Relief algorithm to obtain an optimal feature set; (3) performingnoise processing on the optimal feature set according to a continuous mean denoising method to obtain a denoised feature data set; (4) selecting an extreme boosting tree ensemble learning predictionmodel according to the denoised feature data set, and predicting the productivity of the cutter suction dredger; and (5) evaluating and measuring the productivity prediction result according to various evaluation indexes. According to the method, characteristic elements which are considered to influence the productivity in traditional experience are broken through, new effective factors which influence the productivity are selected, main characteristics are selected by using a data mining method, and the characteristics are subjected to machine learning, so that the productivity of the cuttersuction dredger is accurately predicted, and a new way is found for predicting the productivity of the cutter suction dredger.

Description

technical field [0001] The present invention relates to construction productivity prediction technology for channel excavation, harbor basin excavation, and water dredging in dredging projects, water conservancy projects, etc., and at the same time involves feature selection methods in the field of data mining, machine learning prediction methods, etc., and particularly relates to a winch Intelligent prediction method of construction productivity of suction dredger. Background technique [0002] Dredging is an earthwork project that uses specific mechanical equipment to excavate underwater sediments, sand, rocks, etc. in order to widen and deepen the water area. The dredger is the core equipment of this work here, and there are a large number of dredgers in the world, especially in Western Europe, Japan, the United States and Southeast Asia. They serve the construction and maintenance of coastal and inland waterways, the construction of coastal industrial zones and the deve...

AI Technical Summary

Problems solved by technology

However, dredging operation efficiency is a highly dynamic process with many influencing factors and complex interrelationships, which is closely related to the extremely complex working environment of dredging, making the relationship between the control parameters of the operation process and the actual process output parameters unclear, and it is difficult to use Traditional learning methods to analyze verification and prediction

This creates a barrier to real-time capacity forecasting of dredgers

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