Factory Scheme Recommendation Using Case-Based AI Search
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Solution Overview
Problem
Current digital factories lack the capability to provide optimal scheme recommendations for various technical requests, such as candidate and job recommendations, technical scheme recommendations, and expert solutions, due to the complexity of production tasks and diverse requirements.
Innovation Solution
An intelligent expert recommendation system utilizing a neural network algorithm for deep learning, combined with case-based reasoning, to analyze and extract features from technical requests, and select appropriate searching algorithms from a database of industrial technology, worker characteristics, and expert expertise, integrating and sorting results for optimal scheme recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an intelligent expert recommendation system is established to provide optimal scheme recommendations for technical requests, then the accuracy and quality of recommendations is improved, but the device complexity and system structure increases
Solution Approach 1:
The system segments the recommendation process into distinct functional modules: a neural network algorithm module for deep learning and feature extraction, a case-based reasoning module for selecting searching algorithms, and a recommendation generation module. This segmentation allows each module to specialize in specific tasks, improving overall recommendation accuracy while making the complex system more manageable and maintainable through modular architecture
Solution Approach 2:
The patent introduces an information database as an intermediary component that stores industrial technology information, worker technical characteristics, and expert expertise. This intermediary layer decouples the complex analysis functions from the recommendation delivery, allowing the system to handle complexity internally while presenting simplified interfaces to users, thus improving recommendation quality without proportionally increasing user-facing complexity
2Measurement precision
If multiple searching algorithms are used to search the information database for recommendation schemes, then the comprehensiveness and accuracy of recommendations is improved, but the loss of time and computational resources increases
Solution Approach 1:
The system dynamically selects and adjusts searching algorithms based on the specific characteristics of each technical request. The case-based reasoning module analyzes the request features and chooses appropriate algorithms from multiple options, rather than applying all algorithms uniformly. This dynamic adaptation improves recommendation accuracy for different query types while reducing unnecessary computational overhead and search time
Solution Approach 2:
The patent changes the parameters of the searching process by adjusting algorithm selection, search depth, and matching criteria based on the specific technical request. The neural network algorithm optimizes these parameters by learning from historical data, allowing the system to achieve high recommendation accuracy with reduced search time by avoiding exhaustive searching for all types of queries
3Reliability
If the system integrates and sorts multiple searched items from different algorithms, then the quality and reliability of recommendation schemes is improved, but the device complexity and processing complexity increases
Solution Approach 1:
The patent implements a universal integration mechanism that handles multiple searching algorithms through a single case-based reasoning framework. This universal processor applies consistent integration and sorting rules across different algorithm outputs, improving recommendation reliability through comprehensive evaluation while avoiding the need for separate complex processing logic for each algorithm, thus managing processing complexity efficiently
4Adaptability or versatility
If historical searching cases are used for deep learning to select searching algorithms, then the adaptability and accuracy of recommendations is improved over time, but the use of energy and computational resources increases
Solution Approach 1:
The system performs preliminary deep learning training using historical searching cases during system initialization or off-peak periods, storing the learned patterns and algorithm selection rules in the information database. During actual operation, the pre-trained neural network quickly applies these learned patterns without requiring intensive real-time computation, thus achieving high adaptability and accuracy while reducing operational energy consumption
Data Source
AI summary
Various embodiments of the teachings herein include a method for scheme recommendation. An example includes: obtaining a technical request for a factory production, with a candidate and job recommendation request for a production task, a technical scheme request for a type of work and specific requirement, or a technical expert and solution request for a technical problem; performing analysis and feature extraction on the request and obtaining corresponding current request description information; according to the information, adopting a neural network algorithm for deep learning using historical searching cases in a case database to obtain a searching algorithm from multiple algorithms using case-based reasoning; and obtaining corresponding recommendation scheme after an information database established with industrial technology information, technical characteristics information of workers, and technical expertise information of engineering experts is searched by the at least one searching algorithm.