Pollutant Permit Anomaly Detection Using Quantile Ratio Modeling
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Solution Overview
Problem
Existing methods for reviewing pollutant discharge permits are prone to errors due to incorrect or missing data entries, unit conversion mistakes, and subjective data fabrication, making accurate judgment challenging.
Innovation Solution
A method using Pearson correlation coefficient matrices and quantile regression to identify abnormal pollutant discharge permit information by calculating key parameter pairs, constructing upper and lower edge value distribution functions, and utilizing these functions to detect anomalies in permit data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used for pollutant discharge permit information, then personnel can exercise judgment, but errors occur due to missing data, unit conversion mistakes, and subjective data fabrication
Solution Approach 1:
The patent replaces manual mechanical review with an automated computational system that calculates Pearson correlation coefficients and performs quantile regression analysis. This substitution eliminates human errors in data review, unit conversion, and judgment consistency, achieving both high accuracy and reliable consistent results through mathematical algorithms.
Solution Approach 2:
The patent introduces statistical intermediaries (Pearson correlation coefficients and quantile regression models) between the raw permit data and the final judgment. These intermediaries objectively measure relationships between parameters and identify anomalies, replacing subjective human judgment with measurable statistical evidence.
2Measurement precision
If automated identification methods are implemented, then review accuracy improves, but system complexity increases due to correlation matrices and quantile regression
Solution Approach 1:
The patent transforms the review problem from qualitative judgment to quantitative parameter analysis by calculating Pearson correlation coefficients and quantile regression parameters. This parameter transformation enables automated identification of abnormal permit information through mathematical metrics rather than subjective assessment.
Solution Approach 2:
The patent creates statistical models (correlation matrices and quantile regression functions) that copy and represent the relationships in permit data. These models serve as simplified representations that capture complex data relationships, enabling automated analysis without requiring the full complexity of raw data processing.
3Measurement precision
If comprehensive parameter analysis is performed, then identification accuracy increases, but processing time increases due to multiple calculation steps
Solution Approach 1:
The patent performs preliminary calculations of Pearson correlation coefficients and quantile regression parameters using historical permit data to establish baseline relationships. This preliminary action creates reference models that enable rapid identification of abnormal information in new permit submissions without repeating all calculation steps.
Solution Approach 2:
The patent implements a dynamic review process where the system adapts its analysis depth based on data characteristics. For routine permits, simpler checks are performed, while abnormal cases trigger more comprehensive correlation and regression analysis, optimizing processing time while maintaining detection accuracy.
Data Source
AI summary
A method for automatically identifying abnormal pollutant discharge permit information, an apparatus, a device, and a storage medium are provided. The method includes: using a Pearson correlation coefficient matrix derived from a dynamic time window to fit statistical distribution patterns, thereby determining key parameter pairs suitable for calculating ratio coefficients; calculating a ratio coefficient matrix within the dynamic time window; constructing an upper and lower edge value distribution function of ratio coefficients based on quantile regression; filtering multiple groups of quantile function parameters and selecting an optimal parameter group to serve as final quantile function parameters during actual use; and, when data anomalies are detected, calculating the ratio coefficients using submitted data, and determining whether the submitted data contains any abnormalities based on whether the calculated ratio coefficients fall within the upper and lower edge values predicted by the quantile distribution function.