Runoff Data Abnormal Change Detection via First-Order Differential
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Solution Overview
Problem
Current methods for identifying and correcting abnormal abrupt-change data in runoff data rely on manual selection, which is inefficient and inaccurate, especially when dealing with large amounts of data.
Innovation Solution
A method involving first-order differential processing, threshold settings, and linear interpolation to identify and correct abnormal abrupt-change data in runoff sequences, utilizing a fold-change threshold and abrupt-change window threshold to recognize abrupt-change points and perform data correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual selection is used to identify abnormal abrupt-change data, then the method is simple to implement, but the efficiency and accuracy deteriorate when dealing with large amounts of runoff data
Solution Approach 1:
The patent replaces manual mechanical selection with an automated computational system that uses first-order differential processing and threshold-based algorithms to identify abnormal abrupt-change data, thereby maintaining ease of implementation while dramatically improving processing efficiency for large datasets
Solution Approach 2:
The system enables self-service automation where the runoff data processing system automatically identifies and corrects abnormal abrupt-change data through built-in differential calculation and threshold comparison mechanisms, eliminating the need for manual intervention while improving both efficiency and consistency
2Ease of manufacture
If manual selection is used to identify abnormal abrupt-change data, then the implementation is straightforward, but the accuracy deteriorates when the amount of runoff sequence data is large
Solution Approach 1:
The patent replaces manual visual inspection and judgment with an automated computational system that performs first-order differential processing and threshold-based identification, thereby maintaining ease of implementation while dramatically improving identification accuracy through consistent algorithmic application across large datasets
Solution Approach 2:
The system transforms the identification approach by changing from direct value comparison to first-order differential analysis, using the rate of change as a new parameter to more accurately detect abnormal abrupt-changes regardless of the absolute magnitude of runoff values
3Measurement precision
If first-order differential processing and threshold-based identification are used, then the identification accuracy and efficiency are improved, but the device complexity increases
Solution Approach 1:
The patent segments the data processing into distinct modular steps: first-order differential processing, threshold comparison, and correction operations, making the complex algorithm easier to implement and maintain while preserving high identification accuracy
Solution Approach 2:
The system uses parameter transformation (calculating first-order differences) to convert the original runoff data into a form where abnormal abrupt-changes are more easily detectable through simple threshold comparison, reducing the apparent complexity of the identification process
4Ease of manufacture
If manual correction methods are used, then the process is simple, but the time consumption increases significantly for large amounts of data
Solution Approach 1:
The patent replaces manual correction operations with automated computational processing that applies correction algorithms to identified abnormal data points, maintaining process simplicity while reducing correction time from hours or days to minutes for large datasets
Solution Approach 2:
The system implements continuous automated processing where identification and correction operations flow continuously through the data sequence without manual interruption, eliminating the time losses associated with repeated manual intervention while keeping the correction logic simple and straightforward
Data Source
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AI summary
Embodiments of the present invention relate to a method and an apparatus for processing runoff abnormal abrupt-change data. The method includes: acquiring runoff observation data of a target research region within a preset historical time period, and sorting the runoff observation data by time to obtain a first runoff observation data sequence; performing first-order differential processing on the first runoff observation data sequence, to obtain a first first-order differential runoff observation data sequence; determining runoff abnormal abrupt-change data in the first first-order differential runoff observation data sequence based on a preset runoff abnormal abrupt-change data filtering condition; and correcting the runoff abnormal abrupt-change data. In this way, an abnormal abrupt-change point is recognized by setting an abrupt-change point window threshold and a fold-change threshold of a first-order differential sequence, and data correction is performed by using a linear interpolation method, to implement efficient and precise recognition and correction of runoff sequence abnormal abrupt-change data, thereby improving the cleaning and quality control efficiency of runoff abnormal abrupt-change data.