Data analysis method and apparatus, electronic device, and storage medium
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Solution Overview
Problem
The complexity of semiconductor display manufacturing processes and the lack of high-quality data make it difficult to effectively apply big data and AI technologies, necessitating extensive exploration and trial applications to identify suitable links for improvement.
Innovation Solution
A data analysis method and apparatus that includes acquiring a target task, analyzing target data using data analysis models, determining an optimal analysis model, and performing parameter adjustments to obtain analysis results, with features like data standardization, storage in a data warehouse, and real-time partition management using a partitioning tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive exploration and trial applications are conducted to identify suitable links for big data and AI applications, then the reliability of application selection is improved, but the time consumption and productivity are reduced
Solution Approach 1:
The system performs preliminary evaluation of multiple links in the semiconductor display manufacturing process to pre-identify suitable candidates for big data and AI applications. By conducting assessments in advance and maintaining a pool of pre-evaluated links, the system reduces the time required for exploration while ensuring reliable selection of appropriate application points.
2Measurement precision
If multiple data analysis models are used to analyze target data, then the accuracy of analysis results is improved, but the complexity of the analysis system increases
Solution Approach 1:
The system dynamically selects and switches between different data analysis models based on the characteristics of the target data and the specific analysis requirements. Rather than maintaining a static complex system with all models always active, the system adapts its model selection to match the task at hand, reducing unnecessary complexity while maintaining high accuracy when needed.
Solution Approach 2:
The system changes parameters such as model selection, analysis depth, and processing intensity based on the specific characteristics of the data being analyzed. This allows the system to achieve high accuracy for complex analysis tasks while maintaining simplicity for routine analyses, effectively managing the trade-off between accuracy and system complexity.
3Productivity
If real-time data querying and automated partition management are implemented, then the productivity of data analysis is improved, but the device complexity increases
Solution Approach 1:
The system implements automated partition management that operates autonomously without requiring manual intervention. The partitioning tool automatically manages data partitions, updates metadata, and optimizes data storage structures based on query patterns and data characteristics. This self-service capability improves data analysis productivity while minimizing the need for complex manual management procedures.
Data Source
AI summary
A data analysis method and apparatus, an electronic device, and a storage medium are provided, which are applicable to the semiconductor display manufacturing field and the artificial intelligence technology field. The data analysis method is applicable to a data analysis platform including at least one data analysis model. The method includes: acquiring a target task for a target product; analyzing target data corresponding to the target task using the at least one data analysis model, and determining a target analysis model from the at least one data analysis model; and in response to a first user completing a parameter adjustment operation on the target analysis model based on the target data, analyzing the target data using the adjusted target analysis model to obtain a target analysis result corresponding to the target task.