Optimization method for yarn strength and raw material ratio of vortex spinning based on big data

By using big data analysis methods, the instability of raw material ratio adjustment in vortex spinning was solved, and the correlation analysis and structured division between raw material ratio and yarn strength were realized. Clear ratio adjustment suggestions were provided, which improved the stability and efficiency of production.

CN122117161APending Publication Date: 2026-05-29WUJIANG XINFENG WEAVING
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Patent Information

Application Number
CN202610150730.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-13
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

In the current eddy spinning process, the adjustment of raw material ratios relies on empirical rules and experimental methods, resulting in long cycles and high costs. It is difficult to balance strength stability and resource utilization. Furthermore, it lacks a joint characterization of the strength response amplitude, fluctuation structure, and sample distribution density during ratio changes, which affects the stability and interpretability of decision-making.

Method used

By using big data-based methods, production records are extracted from enterprise ERP systems to generate raw material ratio sequences and yarn strength sequences. Density analysis and interval division are performed, strength response rate and fluctuation are calculated, a comprehensive structural index is constructed, ratio sensitivity is assessed, and a visualized ratio adjustment suggestion report is generated.

Benefits of technology

It achieves a clear correspondence between raw material ratio data and yarn strength data, with a clear interval structure. The ratio change behavior can be described in layers. In terms of decision-making logic, a continuous derivation chain from data intervals and structural attributes to specific ratios is formed, which improves the traceability and stability of ratio selection.

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Abstract

The application discloses a method for optimizing yarn strength and raw material ratio based on big data, and relates to the technical field of textile engineering.The method, under the authorization of an enterprise, acquires production record data with time stamps and batch numbers from an ERP system by using SQL query technology, and performs data cleaning and standardization processing on the data to construct a raw material ratio sequence p and a yarn strength sequence S.The raw material ratio is divided into several ratio intervals by a kernel density estimation method, interval samples are extracted to calculate the strength response rate RQX and the yarn strength fluctuation VQD of each ratio interval, a comprehensive structure index ZJG is jointly constructed to evaluate the ratio sensitivity, the response interval that meets the conditions is selected in combination with the target strength constraint, and the recommended ratio scheme is output by sorting through the response consistency index CWD.The application can provide ratio adjustment suggestions based on historical data driving for the enterprise to optimize the spinning process, and improve the stability and target matching degree of the yarn strength.
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