Selective Degradation Recalculation Using Summary Data
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Solution Overview
Problem
Conventional data analysis methods require re-execution of the entire analytical process whenever parameters change, leading to inefficiencies due to the large volume of raw data involved.
Innovation Solution
A data analysis apparatus that uses summary information to extract and recalculate only the necessary raw data for facilities requiring re-execution, leveraging summary information to determine which facilities need recalculating their degradation levels when parameters change, thereby reducing the need for full re-execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire analytical process is re-executed when parameters change, then the accuracy and completeness of the analysis results are maintained, but the time consumption and computational resources increase significantly
Solution Approach 1:
The patent segments the raw data into multiple groups based on summary information (interim results and final results). When parameters change, only the relevant segments need to be re-executed rather than the entire dataset. This segmentation allows the system to maintain accuracy for changed parameters while avoiding unnecessary re-processing of unchanged data, thus resolving the contradiction between result reliability and time consumption.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing summary information (interim results and final results) for all data groups before parameter changes occur. This preliminary processing creates a foundation that enables rapid identification of which data groups require re-execution when parameters change, eliminating the need to re-process entire datasets and significantly reducing time consumption while maintaining accuracy.
2Measurement precision
If parameter adjustment is performed to optimize analytical schemes, then the quality of analysis results improves, but the frequency of re-execution increases
Solution Approach 1:
The patent divides the data into segmented groups with pre-calculated summary information. When parameter optimization is needed, the system identifies and re-executes only the specific segments affected by parameter changes rather than re-processing all data. This segmentation enables frequent parameter adjustments for quality improvement without proportionally increasing re-execution frequency, thus maintaining productivity.
Solution Approach 2:
The patent implements a feedback mechanism where summary information (interim and final results) is stored and used to determine whether re-execution is necessary after parameter changes. This feedback system allows the analytical process to adapt to parameter changes intelligently, re-executing only when and where needed, thereby maintaining high result quality while avoiding unnecessary re-executions that would reduce productivity.
3Loss of time
If summary information is used to identify facilities requiring recalculation, then the computational overhead for identification increases, but the overall re-execution time is reduced
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing summary information (interim results and final results) for all data groups before parameter changes occur. This preliminary processing creates identification criteria that can be quickly applied when parameters change. The computational complexity of creating summary information once is offset by the significant time savings during subsequent parameter change scenarios, where identification becomes a simple comparison operation rather than full re-execution.
Solution Approach 2:
The patent creates copies of essential information by storing summary data (interim results and final results) that represent the state of each data group. These copies serve as identification markers that can be quickly compared against new parameter values to determine re-execution needs. The computational complexity of maintaining these copies is minimal compared to the alternative of full re-execution, thus reducing overall re-execution time while keeping identification complexity manageable.
Data Source
AI summary
A data analysis apparatus (100) includes a group extracting unit (123) and an analysis executing unit (124). When a parameter is changed, by using summary information (121A), the group extracting unit (123) extracts a facility requiring recalculation of a degradation level from respective facilities corresponding to a plurality of degradation levels included in the summary information (121A). The analysis executing unit (124) recalculates a degradation level of the facility extracted by the group extracting unit (123) by using raw data of the extracted facility and the parameter after change.