A self-adaptive adjusting method for a hot-dip galvanized steel structure modular hanger

By constructing a three-dimensional dynamic digital twin model and a hybrid intelligent optimization algorithm, adaptive scheduling of the rack cluster is achieved, solving the problem of rigid rack scheduling in the hot-dip galvanizing production line and improving production efficiency and system reliability.

CN122114499APending Publication Date: 2026-05-29TAIZHOU YONGBANG HEAVY IND CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TAIZHOU YONGBANG HEAVY IND CO LTD
Filing Date
2026-02-14
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

The existing hot-dip galvanizing production line has a rigid rack scheduling method that cannot adapt to multiple specifications of components, resulting in low turnover efficiency, long changeover and adjustment time, low space utilization, and reliance on manual intervention, which leads to poor production flexibility.

Method used

A virtual-real integrated three-dimensional dynamic digital twin model is constructed, and rack status data is collected in real time through wireless communication. A hybrid intelligent optimization algorithm is used for path planning to achieve adaptive matching and collaborative operation of rack clusters. Combined with fault early warning and fault-tolerant scheduling, intelligent decision-making is achieved throughout the entire process.

Benefits of technology

It significantly shortens the single-batch turnaround time of racks, reduces changeover and adjustment time, improves workshop space utilization, enhances the reliability and continuity of the production system, and provides a flexible and efficient production solution.

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Abstract

The present application relates to the technical field of hanger scheduling, in particular to a steel structure hot-dip galvanizing modular hanger self-adaptive adjustment method, which first collects workshop physical layout and track topology data, constructs a three-dimensional static model, and obtains the position, vibration and load information of each hanger in real time; then fuses dynamic and static data to generate a dynamic digital twin model; according to the specifications of the components to be processed, the adaptive hanger module is automatically matched and distributed, and an intelligent algorithm combining fusion genetic algorithm and reinforcement learning is used to plan a globally optimal conflict-free path for multiple hangers in three-dimensional space; during the execution of the hanger, local real-time obstacle avoidance is realized through the vehicle-mounted sensor, and the model is updated; at the same time, the vibration data is analyzed, the AI model is used for fault warning, and fault-tolerant scheduling is triggered. The present application realizes the self-adaptation, collaboration and intelligence of hanger scheduling, and improves the production efficiency, space utilization and system reliability.
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