Pixel-Variation Frame Stabilization for Automated Equipment Adjustment
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Solution Overview
Problem
The operation of complex measuring and process equipment, such as scanning electron microscopes, requires significant manual intervention due to instability issues, as software robots like Robotic Process Automation (RPA) struggle to recognize screen stability and calibrate equipment parameters effectively.
Innovation Solution
An automatic adjusting method and device that analyzes pixel variations to recognize frame stability, using a template frame to calculate offsets and adjust equipment parameters remotely, thereby stabilizing the frame without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If software robots (RPA) are used to operate measuring equipment, then automation level increases, but ability to recognize frame stability deteriorates
Solution Approach 1:
The patent introduces an intermediary image processing system between the software robot and the equipment interface. This intermediary analyzes pixel variations and frame stability metrics, translating visual information into actionable data that the RPA system can use for automated calibration decisions, thereby bridging the gap between automation capability and stability recognition accuracy
Solution Approach 2:
The patent replaces the mechanical/manual process of visual inspection with an automated image processing algorithm. By substituting human visual analysis with computational pixel variation analysis, the system achieves both high automation and accurate frame stability recognition through mathematical processing of image data
2Measurement precision
If manual monitoring is performed to ensure equipment parameter calibration, then measurement precision improves, but device complexity and manpower requirements increase
Solution Approach 1:
The patent implements a self-service calibration system where the equipment automatically monitors its own parameter stability through continuous image capture and pixel variation analysis. The system self-diagnoses calibration issues and triggers automatic recalibration without requiring external human intervention, thereby maintaining high measurement precision while reducing operational complexity
Solution Approach 2:
The patent establishes a closed-loop feedback mechanism where frame stability metrics are continuously measured and fed back to the control system. This feedback drives automatic parameter adjustments, eliminating the need for complex manual monitoring procedures while ensuring accurate calibration through real-time corrective actions
3Measurement precision
If multiple clear frames with pixel variation are analyzed, then frame stability recognition accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by analyzing only the necessary subset of frame data required for stability assessment. Instead of processing all captured frames equally, the system identifies and focuses on clear frames with significant pixel variations that provide the most informative data for calibration decisions, thereby achieving accurate recognition with reduced processing time
Solution Approach 2:
The patent performs preliminary filtering and preprocessing of captured frames before full analysis. By pre-identifying clear frames with adequate pixel variation and excluding noisy or redundant frames in advance, the system prepares optimized input data that maintains recognition accuracy while significantly reducing the computational burden and processing time
Data Source
AI summary
An automatic adjusting method for equipment and a smart adjusting device using the same are provided. The automatic adjusting method of the equipment includes the following steps. A template frame from the equipment is obtained in an initial period. Several clear frames are obtained in one window period. Each of the template frame and the clear frame has a pixel variation. The pixel variation of the template frame is the largest in the initial period. The pixel variation of each of the clear frame is greater than a threshold. Each of the clear frame is compared with the template frame to obtain an offset. A statistical value of the offsets is calculated. A parameter of the equipment is adjusted to reduce the statistical value.