Bridge crane design method and system fusing knowledge graph and AI technology

By integrating knowledge graphs and AI technologies into the design methodology for bridge cranes, the problem of inefficiency in existing design methods has been solved, enabling rapid and reliable design optimization and ensuring the compliance and reliability of the design solutions.

CN121744539APending Publication Date: 2026-03-27TAIYUAN HEAVY IND
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Patent Information

Application Number
CN202511926864.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing bridge crane design methods rely on manual consultation of scattered specifications and experience manuals, which is inefficient and prone to errors. Simulation-driven design has a long cycle and high cost, and optimization methods lack guidance from physical laws, making it difficult to quickly respond to complex and customized needs.

Method used

A design methodology integrating knowledge graphs and AI technologies is adopted. Initial design schemes are generated through a standard AI expert database and a historical experience design database. AI proxy models are used for targeted optimization and closed-loop verification, replacing traditional CAE simulation, and enabling intelligent retrieval and reuse of design knowledge.

Benefits of technology

It greatly shortens the design iteration cycle, improves design efficiency and quality, ensures the compliance and reliability of design solutions, and enhances adaptive evolution capabilities.

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Abstract

The invention discloses a bridge crane design method fusing a knowledge graph and an AI technology, and relates to the technical field of crane design, and the bridge crane design method comprises the following steps: inputting a design demand file into a standard AI expert database for analysis and quantification, and extracting design target parameters; inputting the design target parameters into a historical experience design library for reasoning to generate an initial design scheme; based on the initial design scheme, generating a CAE simulation model and performing multi-physics field coupling simulation to obtain simulation data, and constructing a mixed training data set; training and updating the pre-trained AI proxy model fusing the design knowledge and the physical knowledge by using the mixed training data set; taking the initial design scheme as a starting point, carrying out directional optimization search based on an AI proxy model, obtaining an optimization design scheme, and carrying out closed-loop verification; and if the closed-loop verification is not passed, performing directional optimization search and closed-loop verification again based on the AI proxy model. According to the method, the design efficiency, quality and self-adaptive evolution capability of the bridge crane can be improved.
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