A method and system for production control of degradable plastic bags

By using a closed-loop control system covering the entire process and leveraging multi-scale spatiotemporal feature extraction networks and process knowledge graphs, the problems of quality fluctuations and high energy consumption in the production of biodegradable plastic bags have been solved, achieving efficient and flexible production control and improving product quality and energy efficiency.

CN122194894APending Publication Date: 2026-06-12XIONGXIAN YIFENG HONGYAO PAPER PLASTIC PACKAGING MATERIAL CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XIONGXIAN YIFENG HONGYAO PAPER PLASTIC PACKAGING MATERIAL CO LTD
Filing Date
2026-03-18
Publication Date
2026-06-12

AI Technical Summary

Technical Problem

The current production process of biodegradable plastic bags suffers from large fluctuations in product quality, high energy consumption, high scrap rate, and insufficient flexible production capacity. It also lacks the ability to systematically model and dynamically control the coupling relationships of multiple processes and variables.

Method used

A closed-loop control system for perception, decision-making, and execution across the entire process is constructed. Through a multi-scale spatiotemporal feature extraction network and a process knowledge graph, the system achieves unified acquisition, deep fusion, and dynamic optimization of multi-source heterogeneous process parameters, identifies nonlinear coupling relationships between parameters across processes, and generates adaptive control strategies.

Benefits of technology

It significantly improves product thickness uniformity and energy efficiency, reduces scrap rate, supports rapid switching between different product specifications, and enhances production quality stability and flexible manufacturing capabilities.

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Abstract

The application discloses a production control method and system for degradable plastic bags, and relates to the technical field of polymer material processing and intelligent control. The method comprises the following steps: collecting multi-source heterogeneous time sequence data of four processes of raw material mixing, melt extrusion, film blowing and heat sealing and cutting; inputting the preprocessed data into a multi-scale space-time feature extraction network to generate a high-dimensional process state embedding vector; combining a process knowledge graph to perform semantic reasoning, identifying a dominant deviation source, and calling a high-confidence adaptive control strategy library to generate a control instruction; and finally driving an execution mechanism to realize closed-loop optimization. The system comprises full-process data acquisition, data preprocessing, feature extraction, graph reasoning, strategy matching, signal generation and closed-loop execution modules. Through full-process collaborative sensing and intelligent decision-making, the application significantly improves product consistency, energy efficiency and flexible production capacity, and reduces the waste rate.
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