A machine learning model establishment method for improving turnover rate of large equipment

By automatically matching tunnel boring machines with engineering projects using machine learning models, the problem of accurate matching between large equipment and engineering projects has been solved, improving equipment turnover rate and resource utilization efficiency.

CN115545630BActive Publication Date: 2026-07-03TUNNEL TANG TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TUNNEL TANG TECH
Filing Date
2022-07-01
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Currently, the engineering project market lacks precise matching information products between large equipment such as tunnel boring machines and engineering projects, leading to resource waste and high operating costs.

Method used

A machine learning model is established to automatically seek the highest matching degree between large equipment such as tunnel boring machines and engineering projects by intelligently extracting classification models and data matching models. This includes using BiLSTM, CRF, Self-Attention, TextCNN and support vector machine algorithms for data processing and matching.

Benefits of technology

It enables precise matching of large equipment such as tunnel boring machines with engineering projects, saving resources, reducing project costs, and avoiding equipment idleness and high-cost use.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a machine learning model establishing method for improving turnover rate of large equipment, belongs to the technical field of machine learning model establishing, and solves the problem of lack of information products for automatically and accurately matching between large equipment such as a shield machine and engineering projects on the market; the method comprises the following steps: training an intelligent extraction classification model by using classification training data, extracting, classifying and summarizing project information data and equipment information data, establishing an information database, training a data matching model by using matching training data, classifying and matching the project information data and the equipment information data, checking the practicability of the data matching model according to an evaluation standard after the training of the data matching model is completed, and inputting the information database into the data matching model for practical application after the practicability reaches the standard; and the application realizes automatic seeking of the highest matching degree between large equipment such as a shield machine and engineering projects, thereby improving the turnover rate of the large equipment such as the shield machine.
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