Automated Data Processing Model Construction via Server-Side Base Selection
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Solution Overview
Problem
Existing data processing methods require users to have high professional capabilities and manually test multiple models to construct a suitable data processing model, increasing complexity and reducing efficiency.
Innovation Solution
A method where users input model description parameters and sample data, allowing a server to automatically determine a base model and train it to construct a target data processing model, reducing the need for manual testing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually test and analyze multiple models through code programs to determine a suitable base model, then the model selection can be customized according to specific requirements, but the operational difficulty increases and processing efficiency decreases
Solution Approach 1:
The system enables self-service by allowing users to input natural language descriptions of their data processing needs, and the server automatically selects and configures the appropriate base model without requiring users to manually test multiple models or understand complex model selection criteria
Solution Approach 2:
The server acts as an intermediary between the user's natural language description and the base model selection process. The server parses the description, identifies key requirements, and automatically matches them with suitable base models from the database, eliminating the need for users to directly interact with multiple model options
2Manufacturing precision
If users manually perform multiple tests to find a suitable base model, then the model can be optimized for specific requirements, but the processing time increases
Solution Approach 1:
The system performs preliminary action by pre-storing multiple base models with different characteristics and capabilities in a database. When a user submits a natural language description, the server quickly retrieves and evaluates pre-configured base models against the requirements, avoiding the need for users to conduct time-consuming manual tests from scratch
Solution Approach 2:
The system replaces the mechanical process of manual model testing and analysis with an automated server-based system. The server uses natural language processing and automated evaluation algorithms to assess base models against user requirements, significantly reducing the time compared to manual testing while maintaining or improving optimization precision
3Adaptability or versatility
If users require high professional capabilities to perform model testing and data analysis, then the model construction can be highly customized, but the accessibility of the system decreases
Solution Approach 1:
The system enables self-service by automatically handling the complex model selection and configuration process. Users only need to provide natural language descriptions of their needs, and the server autonomously performs data analysis, model evaluation, and configuration, making the system accessible to users without requiring professional machine learning expertise
Solution Approach 2:
The server acts as an intermediary that translates user-friendly natural language descriptions into technical model configuration parameters. This intermediary layer shields users from complex technical details while still enabling highly customized model construction based on their specific requirements
Data Source
AI summary
A method for constructing a data processing model, includes: acquiring a model description parameter and sample data of a target data processing model; determining a base model according to the model description parameter and the sample data; and training the base model according to the sample data to obtain the target data processing model.