Abnormality identification method and equipment for air data and medium

By employing a multi-perspective, multi-scale, and multi-mechanism small model system, combined with statistical, machine learning, and deep learning models, anomalies in air data are identified, solving the problem of frequent anomalies in air quality monitoring data and improving the stability and efficiency of data processing.

CN121963969APending Publication Date: 2026-05-01HEBEI UNIV OF SCI & TECH +1
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Patent Information

Application Number
CN202511947360.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-23
Publication Date
2026-05-01

AI Technical Summary

Technical Problem

Frequent anomalies in air quality monitoring data affect the scientific rigor and accuracy of environmental regulation, pollution source tracing, and assessment. Existing technologies struggle to effectively identify and process various types of data anomalies.

Method used

Employing a multi-perspective, multi-scale, and multi-mechanism small model system, this system identifies anomalies in air data by calculating the frequency of the first digit in air data, conducting time-series analysis, analyzing the correlation of air indicators, and predicting weather data, combined with statistical, machine learning, and deep learning models.

Benefits of technology

It achieves comprehensive coverage of air data, enables rapid screening of structural and meteorological anomalies, improves the stability and efficiency of data processing, and significantly enhances the data cleaning and quality control capabilities of regulatory authorities.

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Abstract

The invention provides an air data anomaly identification method and device and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring air data in a target monitoring period; the occurrence frequency of different first digits in the air data is calculated, and a first anomaly in the air data is recognized based on the Benford's law according to the occurrence frequency; performing time sequence analysis on the air data, and identifying a second anomaly in the air data; correlation among the air indexes is calculated, and a third anomaly in the air data is recognized according to the correlation; and according to the weather data in the target monitoring period, predicting reference air data, and according to the reference air data, identifying a fourth anomaly in the air data. According to the invention, the abnormity in the air data can be comprehensively and accurately identified.
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