Real-time dynamic correction method and system for heliostat based on multi-source data fusion

The heliostat calibration method, which integrates multi-source data fusion and dynamic optimization, solves the problems of insufficient accuracy and poor real-time performance in traditional heliostat calibration methods. It achieves high-precision, real-time prediction and active compensation calibration of mirror deformation, thereby improving the performance of solar thermal power generation systems.

CN122172865APending Publication Date: 2026-06-09SHANGHAI BOILER WORKS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI BOILER WORKS CO LTD
Filing Date
2026-01-28
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Traditional heliostat calibration methods rely on optical image analysis, which fails to effectively integrate real-time environmental data, resulting in insufficient calibration accuracy, poor real-time performance, inability to dynamically and adaptively adjust zoning strategies, and inability to actively predict mirror deformation trends, thus affecting system stability and efficiency.

Method used

By synchronously collecting real-time environmental data and camera image data from the heliostat, data fusion is performed using a fusion algorithm model to dynamically optimize the mirror surface zoning adjustment strategy. Furthermore, Kalman filtering or machine learning models are used to predict the mirror surface deformation trend, enabling real-time active compensation and correction.

Benefits of technology

This improved the accuracy and real-time performance of heliostat calibration, reduced calibration lag, and enhanced the stability and efficiency of the system.

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Abstract

This invention relates to a real-time dynamic correction method and system for heliostats based on multi-source data fusion. The method includes: simultaneously acquiring real-time environmental data and camera image data of the heliostat, calculating correction parameters and heliostat world coordinates from the camera image data to obtain final camera image data; fusing the real-time environmental data and the final camera image data to generate fused data; dynamically optimizing the mirror partitioning adjustment strategy to obtain a target mirror partitioning adjustment strategy; predicting the heliostat deformation trend using Kalman filtering or a machine learning model based on the target mirror partitioning adjustment strategy to obtain a prediction result; and actively compensating for and correcting the heliostat attitude in real-time based on the prediction result. By fully utilizing multi-source data and using Kalman filtering or a machine learning model to predict the mirror deformation trend, active compensation and correction can be achieved, improving the accuracy and real-time performance of heliostat correction.
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