Feature-Based Data Processing for Resource-Efficient Material Chasing

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Solution Overview

Problem

Existing digital business systems require complex logic design and consume significant data storage and computing resources to accurately and efficiently confirm material chasing in production processes, making them inefficient for timely material delivery.

Innovation Solution

A data processing system and method that builds a structured semantic knowledge base using a processor and storage device to generate summary data, perform feature index calculation, and build a classification model, reducing the need for complex logic and resource consumption.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If traditional digital business systems are used to automate material chasing in production processes, then the prediction function can be realized, but complex logic design and large data storage space and computing resources are consumed

Engineering Contradiction:

Improvematerial chasing automationVSAvoidlogic design complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical logic design with a neural network-based intelligent system. The material chasing prediction is achieved through machine learning models that automatically learn patterns from historical data, eliminating the need for complex manual logic design while maintaining automation capability

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the approach by changing from fixed logic rules to dynamic parameter-based prediction. The system uses multiple parameters (production progress, material delivery status, lead times) that are continuously updated and processed by neural networks to adapt to changing production conditions without requiring complex logic restructuring

Inventive Principle:

Principle #35Parameter changes

2Extent of automation

If traditional digital business systems are used to automate material chasing in production processes, then the prediction function can be realized, but large data storage space and computing resources are consumed

Engineering Contradiction:

Improvematerial chasing predictionVSAvoiddata storage space

Core Design Contradiction:

Extent of automationVSQuantity of substance

Solution Approach 1:

The patent extracts only the essential features and parameters needed for material chasing prediction from the complete dataset. By identifying and extracting key variables (delivery status, lead time, production progress) rather than processing all available data, the system reduces storage requirements while maintaining prediction accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of storing and processing all raw data, the system inverts the approach by storing pre-processed features and using generative models to recreate detailed information only when needed for prediction, significantly reducing continuous storage requirements

Inventive Principle:

Principle #13The other way round (Inversion)

3Productivity

If traditional digital business systems are used to automate material chasing, then business processes can be automated, but the system cannot accurately and efficiently confirm material chasing by human judgment

Engineering Contradiction:

Improvematerial chasing efficiencyVSAvoidmaterial chasing accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements continuous feedback loops where prediction results are compared with actual material delivery outcomes. This feedback is used to retrain and refine the neural network models, progressively improving prediction accuracy while maintaining high processing efficiency through automated iterations

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis and prediction before material chasing is actually needed. By pre-processing data and generating predictions in advance, the system has sufficient time to analyze multiple scenarios and provide accurate recommendations without compromising real-time decision-making speed

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12405964B2Data processing system and data processing method

Publication Date: 2025.09.02 DIGIWIN CO LTD

AI summary

A data processing system and a data processing method are provided. The data processing system includes a processor and a storage device. The storage device stores a data processing module, a feature processing module and a classification conversion module. The processor is electrically connected to the storage device, and executes the data processing module, the feature processing module and the classification conversion module. The data processing module generates summary data according to multiple form data. The data processing module performs feature index calculation on the summary data to generate feature index data. The data processing module preprocesses the feature index data to generate a sample label data set. The feature processing module generates a training data set based on the sample label data set. The classification conversion module builds a classification model based on the training data set, and builds a structured semantic knowledge base based on the classification model.