Operator Efficiency Classification via Multi-Source Feature Fusion
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Solution Overview
Problem
Existing operator assignment methods in production line management are inefficient as they rely heavily on structured data, leading to inaccurate predictions and require retraining for slight changes, making it difficult to adapt to new scenarios and changes in business conditions.
Innovation Solution
A classification method that extracts feature data from operator images and personal data, converting it into vectors and merging them into a matrix for classification, allowing for improved prediction accuracy and efficient adaptation to changes by integrating features from both structured and unstructured data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning models are used for operator assignment, then prediction capability is provided, but the models require retraining for slight changes in prediction targets, making it impossible to import models efficiently and rapidly
Solution Approach 1:
The patent applies universality by designing a feature extraction framework that can handle multiple types of data (images, text, structured data) and adapt to different prediction targets using the same base model architecture. The model is trained on multi-source features that can be applied across different operator assignment scenarios without requiring complete retraining, thus enabling one model to serve multiple functions and prediction targets.
Solution Approach 2:
The patent implements dynamics through its incremental learning capability and adaptive feature extraction. The system can dynamically adjust to new prediction targets by leveraging the pre-trained multi-source feature extraction capabilities and fine-tuning on specific tasks, rather than requiring static, fixed-purpose models. This allows the system to adapt rapidly to changing business requirements and prediction targets.
2Measurement precision
If only structured data is used for machine learning, then data processing is simplified, but effective features are difficult to find and prediction accuracy is reduced
Solution Approach 1:
The patent applies merging by combining multiple data sources including images, text, and structured data into a unified feature extraction framework. Different types of data are processed through appropriate neural network architectures (CNN for images, BERT for text, and direct processing for structured data) and then merged into a comprehensive feature representation, thereby improving prediction accuracy by leveraging complementary information from diverse sources.
Solution Approach 2:
The patent uses intermediary components in the form of specialized feature extraction modules that act as mediators between raw multi-source data and the prediction model. These intermediaries (CNN layers for images, BERT for text, and feature engineering modules for structured data) transform diverse data types into standardized feature representations that can be effectively processed by the prediction algorithm, thereby bridging the gap between complex data sources and prediction requirements.
3Reliability
If machine learning models are retrained for each new scenario, then prediction accuracy for specific scenarios is improved, but the process becomes inefficient and cannot adapt rapidly to business changes
Solution Approach 1:
The patent applies preliminary action by pre-training a comprehensive feature extraction model on multi-source data (images, text, structured data) that captures general patterns and features applicable across multiple scenarios. This pre-trained model serves as a foundation that can be quickly adapted to specific scenarios through fine-tuning or direct deployment, eliminating the need to train from scratch for each new scenario and thereby improving both reliability and deployment efficiency.
Solution Approach 2:
The patent implements parameter changes by allowing the system to adjust model parameters and configuration based on specific prediction scenarios without retraining the entire model. The pre-trained multi-source feature extraction model can have its parameters adjusted or selectively activated depending on the scenario requirements, enabling rapid adaptation to new scenarios while maintaining prediction reliability through the robust pre-trained features.
Data Source
AI summary
The disclosure provides a classification method and an electronic apparatus. The classification method includes the following steps. First feature data of multiple pictures of assembly is extracted, and each picture of assembly includes an operator at a station. The first feature data is converted into a first feature vector. Second feature data recording personal data of the operator is converted into a second feature vector. The first feature vector and the second feature vector are merged into a first feature matrix. The efficiency of the operator operating at the station is classified according to the first feature matrix to obtain a classification result.