Steel production quality intelligent labeling and analysis method and system based on multi-process space-time mapping

By using a multi-process spatiotemporal mapping and data alignment mechanism, the problems of cross-process data correlation and low efficiency of manual annotation in steel production have been solved, enabling quality analysis and traceability of complex processes and improving data utilization efficiency and analysis automation level.

CN121502200APending Publication Date: 2026-02-10SHANGHAI BAOSIGHT SOFTWARE CO LTD
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Patent Information

Application Number
CN202511583001.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-31
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

Existing technologies struggle to effectively capture high-frequency noise and fluctuation trends in steel production. They lack frequency domain analysis methods, cross-process data lack correlation, and manual annotation suffers from subjectivity and low efficiency, making it difficult to achieve quality analysis and traceability of complex processes.

Method used

By establishing a mechanism for converting processing time and location through multi-process spatiotemporal mapping, a cross-process location mapping model is constructed. Combined with data alignment and differential analysis strategies, automatic annotation and feature extraction are achieved to conduct cross-process quality analysis and problem tracing.

Benefits of technology

It achieves unified integration and precise alignment of production data from various processes, improves the efficiency and accuracy of anomaly tracing and location, supports rapid identification and root cause analysis, and enhances the utilization efficiency of manufacturing data and the systematization and automation level of analysis.

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Abstract

The invention provides an intelligent marking and analyzing method for steel production quality based on multi-process space-time mapping. The method comprises the following steps: acquiring a time sequence curve of characteristic parameters, material tracking data and material quality data of a target material in each process in production; according to the material tracking data, establishing a mapping model of the processing position, the process processing time and the cross-process processing position; standardized marking is conducted on the machining position and the machining time interval based on the mapping model; extracting corresponding time sequence curve features based on the annotation interval; analyzing the difference between the target material and the reference material based on feature comparison; and generating a diagnosis result according to the analysis result. The technical problems that in the prior art, the process curve feature extraction mode is single, cross-process data are difficult to associate, and the quality problem tracing efficiency is low are solved.
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