Dynamic Grayscale Adjustment for Data Visualization
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Solution Overview
Problem
Existing data visualization methods, such as heat maps, face challenges in accurately representing data distributions with fixed thresholds, leading to overheating or underrepresentation of extreme values, requiring manual and subjective threshold adjustments.
Innovation Solution
A dynamic scale adjustment method that calculates statistical data, determines upper and lower thresholds based on the mean value, and assigns data to levels, allowing for dynamic threshold adjustment in data visualization processing devices to ensure even color distribution across different data collection sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed threshold is used for heat map color representation, then the implementation is simple, but the data distribution becomes unbalanced with extreme values causing overheating or underrepresentation
Solution Approach 1:
The patent applies the dynamics principle by transforming the static fixed threshold into a dynamic adaptive threshold system. The threshold automatically adjusts based on statistical calculations (mean and standard deviation) of the actual data distribution, allowing the heat map to adapt to varying data characteristics across different time intervals and datasets, thereby resolving the contradiction between implementation simplicity and representation accuracy
Solution Approach 2:
The patent implements parameter changes by modifying the threshold parameter from a fixed constant to a dynamically calculated value based on data statistics. By computing the mean and standard deviation and using them to determine adaptive thresholds, the system changes the threshold parameter to match the actual data distribution, improving accuracy while maintaining automated operation
2Manufacturing precision
If the user manually adjusts the threshold to achieve even color distribution, then the data representation accuracy improves, but the operation complexity and time consumption increase
Solution Approach 1:
The patent applies the self-service principle by enabling the threshold adjustment system to automatically calculate and set appropriate thresholds without requiring user intervention. The system performs statistical analysis on the data and autonomously determines the optimal threshold values, eliminating the need for manual adjustment while maintaining accurate data representation
Solution Approach 2:
The patent implements feedback by continuously monitoring the data distribution characteristics and using this information to automatically adjust the threshold. The system calculates statistical parameters from the data and uses this feedback to determine appropriate threshold values, creating a closed-loop system that maintains accuracy without manual operation
3Stability of the object's composition
If a fixed threshold is used across different time intervals, then the implementation is consistent, but the heat map cannot correctly show the relationship of data in varying distributions
Solution Approach 1:
The patent resolves this contradiction by making the threshold dynamic rather than static. The threshold automatically adapts to different time intervals by recalculating based on the statistical characteristics of data in each interval, maintaining consistency in the adjustment mechanism while achieving adaptability to varying data distributions
Solution Approach 2:
The patent applies universality by creating a threshold adjustment mechanism that works universally across different time intervals and data types. The statistical-based threshold calculation method can be applied to any dataset with varying distributions, making the system both consistent in its approach and adaptable to different scenarios
Data Source
AI summary
A dynamic scale adjustment method for data visualization enhancement comprises calculating statistical data for a data collection section according to data collected in the data collection section, calculating a mean value according to the statistical data corresponding to the data collection section, dividing the statistical data into a plurality of levels, determining an upper threshold and a lower threshold according to the mean value, and assigning a first data of the statistical data to the highest level when the first data exceeds the upper threshold and assigning a second data of the statistical data to the lowest level when the second data is under the lower threshold.