Papermaking Quality Evaluation via Feature Extraction and Model Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current papermaking quality evaluation methods are subjective, offline, and lack comprehensive, multi-dimensional online assessment capabilities, requiring high computing power and storage due to image processing demands.
Innovation Solution
A method and system for evaluating papermaking quality by determining evaluation targets, acquiring and preprocessing data, integrating and extracting features, establishing analysis models for anomaly detection, quality level classification, and quality indicator prediction to generate comprehensive quality health evaluation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If image monitoring and image morphological analysis are used for online papermaking quality evaluation, then online evaluation capability is improved, but high computing power and storage capacity are required
Solution Approach 1:
The patent extracts only the essential feature information from papermaking processes and quality data, rather than processing complete images or raw data. By taking out and retaining only the critical features needed for quality evaluation, the system achieves online evaluation capability while significantly reducing computing power and storage capacity requirements.
Solution Approach 2:
Instead of starting with complete images and extracting features (traditional approach), the patent inverts the process by directly acquiring and processing structured process data and quality data. This inversion eliminates the need for complex image processing while maintaining online evaluation capability, thereby reducing computational resource demands.
2Loss of information
If comprehensive multi-dimensional quality evaluation is implemented, then evaluation comprehensiveness is improved, but system complexity increases
Solution Approach 1:
The patent segments the comprehensive quality evaluation into multiple independent evaluation targets, each focusing on specific quality dimensions. By dividing the complex evaluation task into smaller, manageable segments with dedicated analysis models, the system achieves comprehensive multi-dimensional evaluation while keeping individual model complexities low and manageable.
Solution Approach 2:
The patent creates a universal evaluation framework that handles multiple quality dimensions through a standardized process: data acquisition, preprocessing, feature extraction, and analysis. This multi-functional approach allows the system to evaluate various quality aspects comprehensively while reusing the same technical infrastructure, thereby reducing overall system complexity.
Data Source
AI summary
A method for evaluating papermaking quality includes determining an evaluation target related to papermaking quality and for each evaluation target: acquiring target working condition data, target condition monitoring data and target papermaking quality data; preprocessing the acquired data; performing data integration on the preprocessed target working condition data, the preprocessed target condition monitoring data, and the preprocessed target papermaking quality data to obtain an integrated data set; performing feature extraction on data in the integrated data set according to types and characteristics of the data; establishing a paper quality analysis model based on the target feature data set; and evaluating the corresponding evaluation target and generating a quality health evaluation result of the corresponding evaluation target based on the papermaking quality analysis model. Also obtaining a comprehensive papermaking quality evaluation result based on the quality health evaluation result of the at least one evaluation target.