Intelligent Recommendation System for Factory Worker Skill Training
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Solution Overview
Problem
Current learning methods for factory workers, such as the master-apprentice system and social media-based learning, are inefficient and costly, as they rely on face-to-face communication, are time-consuming, and fail to accurately provide relevant training content, leading to skill gaps and reduced productivity.
Innovation Solution
A method and device for intelligently providing recommendation information using user attribute parameters and a scoring model, combined with a knowledge graph, to identify skill gaps and recommend relevant training content based on user skills and similarity, improving learning efficiency and reducing manual training costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the master-apprentice system is used for worker training, then knowledge transfer occurs through face-to-face communication, but the training period becomes long and the master's production capacity is reduced
Solution Approach 1:
The system creates a digital copy of the master's knowledge and skills by capturing operation data, process data, and sensor data during actual work. This digital replica is then used to train apprentices through virtual simulation, eliminating the need for prolonged face-to-face training while preserving the master's production capacity.
Solution Approach 2:
The patent introduces a digital twin as an intermediary between the master and apprentice. The digital twin captures the master's operational patterns and serves as a virtual teaching assistant, enabling knowledge transfer without requiring the master's direct time and attention, thus resolving the conflict between effective knowledge transfer and training duration.
2Ease of operation
If social media-based learning is used, then learning accessibility is improved, but the learning content becomes cumbersome and workers cannot accurately find what they need
Solution Approach 1:
The system provides personalized learning content tailored to each worker's specific skill level, current task requirements, and identified knowledge gaps. Rather than offering generic social media content, the system curates localized, relevant training materials that directly address the individual worker's needs, ensuring high relevance while maintaining accessibility.
Solution Approach 2:
The system continuously monitors worker performance through sensors and operational data, identifying specific skill deficiencies. This feedback loop enables the system to dynamically adjust and recommend targeted learning content, ensuring workers receive precisely the information they need rather than overwhelming them with unrelated social media content.
3Reliability
If traditional training methods are used, then workers receive hands-on training, but the cost and time investment increase significantly
Solution Approach 1:
The system creates virtual copies of training scenarios by capturing real-world operational data and reconstructing it in a digital environment. Apprentices can practice skills repeatedly in this virtual space without consuming physical resources or requiring actual equipment, dramatically reducing training costs while maintaining skill acquisition effectiveness.
Solution Approach 2:
The system performs preliminary analysis of worker skill gaps and pre-prevents personalized training paths before actual training begins. By identifying exactly what skills need development and preparing targeted content in advance, the system eliminates wasteful training activities and reduces overall training investment while ensuring effective skill acquisition.
Data Source
AI summary
Various embodiments include methods for intelligently providing recommendation information. For example, the method may include: determining user attribute parameters corresponding to a user identifier; determining a score value corresponding to the user identifier by inputting the user attribute parameters into a scoring model; determining recommendation information corresponding to the user identifier based on the score value and a knowledge graph related to the user; and providing the recommended information.