Heliostat surface shape detection system and method based on multi-view image recognition
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Solution Overview
Problem
Current heliostat surface shape detection methods, both contact and non-contact, face challenges in achieving high precision and efficiency due to interference from stray light and high reflectivity surfaces, and require complex adjustments for accurate measurements.
Innovation Solution
A non-contact heliostat surface shape detection system utilizing a multi-view image recognition method with a multi-view image collector array and a computer, where image collectors are aligned parallel to each other and capture images of the heliostat surface, allowing for 3D surface shape reconstruction through feature matching and distance measurement, avoiding direct interaction with the surface and minimizing interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stripe projection method is used for non-contact detection, then detection can be completed without contact, but the high reflectivity of heliostat surface causes stray light interference that affects stripe image contrast and detection accuracy
Solution Approach 1:
The patent introduces a screen as an intermediary carrier to display the projected stripe pattern. Instead of directly capturing stripes on the highly reflective heliostat surface, the system projects stripes onto the screen, captures the screen image, and then uses geometric relationships to calculate surface shape. This intermediary approach eliminates stray light interference from the measurement process while maintaining non-contact detection capability.
Solution Approach 2:
The system creates a copy of the measurement process by projecting the stripe pattern onto a screen rather than directly onto the heliostat surface. The screen serves as a copy that captures the projection pattern without the interference of high reflectivity, allowing accurate image capture and subsequent calculation of the actual surface shape through geometric relationships.
2Measurement precision
If relative position adjustment between image collector, heliostat and screen is performed for each detection, then complete stripe image can be obtained, but detection efficiency is reduced due to complex adjustments
Solution Approach 1:
The system performs preliminary calibration to establish the geometric relationships between the image collector, screen, and heliostat mounting positions before actual detection. Once calibrated, the relative positions are fixed, and the system can directly detect multiple heliostats without requiring re-adjustment for each measurement, significantly improving detection efficiency while maintaining measurement accuracy.
3Measurement precision
If contact detection method with displacement sensor or probe is used, then detection can be performed, but the force exerted on mirror surface affects detection accuracy
Solution Approach 1:
The patent replaces the mechanical contact detection system (displacement sensors or probes that physically touch the surface) with an optical non-contact detection system. By using projected stripe patterns and image capture, the system obtains surface shape information without any physical contact, thereby eliminating the harmful forces that would deform the mirror surface and affect measurement accuracy.
4Productivity
If multi-view image collector array is used for batch detection, then detection efficiency is improved, but system complexity increases
Solution Approach 1:
The system divides the detection task into multiple independent view channels by using an array of image collectors, each capturing the screen from a different angle. This segmentation allows parallel processing of multiple heliostat surfaces simultaneously. The complexity is managed by maintaining identical hardware configurations for each view channel and using standardized calibration procedures.
Solution Approach 2:
The patent designs the image collector array with universal, identical components that can detect multiple types of surfaces (continuous and discrete) using the same hardware platform. The system achieves multi-functionality by processing different surface types through the same optical path and calculation algorithms, reducing overall system complexity while enabling batch detection of various heliostat configurations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables high-precision, efficient detection of heliostat surface shapes with improved resistance to stray light and high reflectivity, suitable for both continuous and discrete surfaces, and allows for batch detection with reduced calibration needs, enhancing detection efficiency and accuracy.
Implementation Method 1
a heliostat reflects and gathers the sunlight irradiated onto its surface
Data Source
AI summary
A heliostat surface shape detection system and a method based on multi-view image recognition are described. The system includes a multi-view image collector array, a bracket and a computer. The multi-view image collector array is arranged on the bracket so that the main optical axes of image collectors are parallel to each other and point to the heliostat; the multi-view image collector array is connected with the computer via data lines, and transmits the collected image data to the computer for heliostat surface shape calculation.