Metering Abnormality Analysis Using Multidimensional Cluster Aggregation
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Solution Overview
Problem
Current metering abnormality analysis methods have a limited analysis dimension and simple verification logic, leading to low accuracy in identifying the joint causes of metering issues, resulting in recurrence and spread of problems, and are unable to effectively reduce electric power operation and maintenance costs.
Innovation Solution
A method and apparatus for metering abnormality analysis that involves acquiring preliminary analysis data, determining data filtering rules, filtering monitoring data, comparing it with preconfigured abnormal case data, and performing multidimensional cluster analysis to determine the aggregation level and cause of abnormalities, thereby expanding the analysis dimension and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional indicator calculation method is used to analyze metering abnormality, then the analysis process is simple and fast, but the analysis dimension is small and the accuracy of identifying joint causes is low
Solution Approach 1:
The patent transitions from traditional single-dimension indicator calculation to multidimensional cluster analysis by introducing multiple analysis dimensions including data filtering rules, abnormal case data comparison, and cluster aggregation levels. This dimensional expansion enables comprehensive identification of joint causes while maintaining analysis efficiency through structured processing frameworks.
2Measurement precision
If comprehensive data analysis is performed to improve accuracy of metering abnormality identification, then the analysis precision improves, but the computational complexity and data processing burden increase
Solution Approach 1:
The patent segments the complex data analysis process into distinct modular stages: data filtering rule determination, abnormal case data comparison, multidimensional cluster analysis, and aggregation level calculation. Each module handles specific aspects of the analysis independently, reducing overall computational complexity while maintaining high accuracy through systematic processing of comprehensive data.
3Ease of operation
If traditional analysis methods are used, then the implementation is easy and quick, but the verification logic is simple and cannot effectively prevent recurrence and spread of metering abnormality problems
Solution Approach 1:
The patent implements feedback mechanisms by comparing analyzed metering data against preconfigured abnormal case data and using cluster analysis results to identify patterns and root causes. This feedback loop enables the system to learn from historical abnormalities and improve future detection accuracy, effectively preventing recurrence while maintaining operational ease through automated processing.
Data Source
AI summary
Provided are a metering abnormality analysis method and apparatus, a storage medium, and a computer device. The metering abnormality analysis method includes acquiring preliminary analysis data analyzed by a source-end system and determining at least one data filtering rule based on the preliminary analysis data (101); filtering the monitoring data of the source-end system based on the data filtering rule to obtain target metering abnormality data (102); comparing and analyzing the target metering abnormality data with preconfigured abnormal case data and determining at least one target case data from the abnormal case data (103); and performing multidimensional cluster analysis on the target metering abnormality data to obtain the aggregation level of the target metering abnormality data in multiple data dimensions and analyzing the cause of metering abnormality based on the target case data and the aggregation level (104).