Intelligent textile workshop management and control platform and method based on big data drive

By constructing a multi-dimensional feature space and a weighted heuristic genetic algorithm to optimize the production scheduling sequence, and combining it with a load fragment library to predict risks, the problem of unbalanced equipment load in the textile workshop was solved, equipment load balancing and energy consumption optimization were achieved, and the stability and continuity of production were improved.

CN120525296BActive Publication Date: 2025-10-03DONGHUA UNIV +1
1 Cites 0 Cited by

Patent Information

Application Number
CN202511013569.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-07-23
Publication Date
2025-10-03
Estimated Expiration
2045-07-23

AI Technical Summary

Technical Problem

In the modern textile industry, there is a lack of dynamic prediction and adjustment mechanisms for the problems of high load and high energy consumption vicious cycle, frequent equipment overload and production interruption caused by unbalanced equipment load.

Method used

A big data-driven intelligent textile workshop management and control method extracts the operating data of historical production tasks, constructs a multi-dimensional feature space, uses a weighted heuristic genetic algorithm to optimize the production scheduling, combines the load fragment library with the pattern mapping mechanism, predicts the risk of load accumulation and inserts buffer tasks to avoid equipment overload.

Benefits of technology

Achieve equipment load balancing, reduce energy consumption, improve production continuity, reduce equipment failures, and ensure production process stability.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention belongs to the technical field of intelligent textile workshop management and control, and discloses an intelligent textile workshop management and control platform and method driven by big data. The method includes: matching historical tasks with the sequence of production tasks to be scheduled and extracting the operation data of the historical tasks; preprocessing the operation data to construct a multi-dimensional information feature space containing basic features and derived features; based on the production tasks to be scheduled and the feature space, dynamically optimizing the production task scheduling sequence through a weighted heuristic genetic algorithm to generate a new scheduling sequence. The present invention achieves equipment load balancing, energy consumption reduction and production continuity improvement through big data integration and intelligent algorithm optimization, thereby improving the effectiveness of intelligent textile workshop management and control.
Need to check novelty before this filing date? Find Prior Art