A data collection system and method for meteorological big data analysis

By using adaptive data calibration and interquartile range outlier removal using the AdaCalib and NeuCalib algorithms, combined with time synchronization and WGS-84 coordinate system integration of meteorological data, the problems of diverse meteorological data sources and inaccurate real-time monitoring data were solved, achieving efficient and accurate meteorological data collection and analysis.

CN122153397APending Publication Date: 2026-06-05ELECTRIC POWER RES INST STATE GRID SHANXI ELECTRIC POWER +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ELECTRIC POWER RES INST STATE GRID SHANXI ELECTRIC POWER
Filing Date
2026-03-12
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

In existing technologies, meteorological data comes from diverse sources and has inconsistent formats, making it difficult to effectively integrate and analyze. Real-time monitoring data is easily affected by sensor performance and the environment, leading to a decline in data accuracy and reliability. Traditional calibration methods are difficult to adapt to environmental changes and equipment aging.

Method used

Adaptive data calibration was performed using the AdaCalib and NeuCalib algorithms, outliers were removed by combining the interquartile range method, and multi-parameter meteorological data were integrated through time synchronization and the WGS-84 coordinate system to construct a multi-dimensional meteorological data system.

Benefits of technology

It improves the integrity and reliability of meteorological data, ensures data consistency and accuracy, adapts to changes in sensor performance and environmental interference, and is suitable for scenarios with high requirements for meteorological data accuracy, such as power transmission lines.

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Abstract

This invention discloses a data collection system and method for meteorological big data analysis, belonging to the field of meteorological data collection technology. It includes a data acquisition unit, a data processing unit, a data calibration unit, a fusion output unit, and a storage unit. By collecting historical meteorological data, real-time monitoring data, geospatial data, and equipment attribute data from multiple sources, and performing standard score conversion and interquartile range outlier removal, data quality and consistency are improved. The AdaCalib and NeuCalib algorithms are employed, combined with piecewise linear regression and neural networks for adaptive calibration, and dynamic adjustment and incremental learning are introduced to eliminate sensor drift and environmental interference, ensuring long-term monitoring accuracy. Time synchronization and WGS-84 coordinate mapping achieve spatiotemporal unification, integrating data into a comprehensive dataset containing meteorological parameters, timestamps, geographic coordinates, and equipment attributes, breaking down data silos, supporting in-depth analysis, and providing high-quality data support for precise meteorological services and decision-making.
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