Smart Gas IoT System for Abnormal User Detection via Clustering
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Solution Overview
Problem
There is a need for a method and system to quickly and accurately identify anomalous gas users engaged in theft or misuse, as existing technologies fail to effectively detect and warn such users, leading to economic losses and potential safety risks in gas distribution systems.
Innovation Solution
A smart gas IoT system that analyzes user features and pipeline network transportation features to cluster gas users, determine potential abnormal users based on device use and metering data, and send early warnings to target users, utilizing clustering results and data analysis to identify first and second abnormal users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gas monitoring methods are used, then the system is simple to operate, but the detection precision and ability to identify abnormal users is insufficient
Solution Approach 1:
The patent segments the gas user population into different clusters based on usage patterns, device types, and consumption behaviors. By dividing users into distinct groups, the system can identify abnormal users within each cluster more effectively, improving detection precision without overwhelming the system with undifferentiated data.
Solution Approach 2:
The patent introduces multiple dimensions for analyzing gas user data, including temporal patterns, spatial distribution, device characteristics, and consumption behavior. By adding these additional dimensions, the system achieves higher detection accuracy through multi-faceted analysis rather than relying on single-parameter monitoring.
2Measurement precision
If comprehensive data analysis is performed on all gas users, then the detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary clustering of gas users based on historical data and established patterns before conducting detailed abnormality analysis. By pre-grouping users into clusters with similar characteristics, the system reduces the scope of subsequent analysis and can quickly identify potential abnormalities without processing every user's complete data set in detail.
Solution Approach 2:
The patent focuses analysis efforts on specific clusters or groups of users who exhibit higher risk characteristics or abnormal patterns, rather than uniformly analyzing all users. This selective approach allows the system to maintain high detection accuracy while reducing overall processing time and computational resources by concentrating efforts where they are most needed.
3Reliability
If multiple clustering methods are applied to gas users, then the identification of abnormal users becomes more accurate, but the device complexity and computational requirements increase
Solution Approach 1:
The patent combines multiple clustering methods and analysis approaches into a unified system that leverages the strengths of each method. By merging different clustering techniques (such as hierarchical clustering, k-means, and density-based clustering), the system achieves more reliable abnormal user identification through complementary approaches rather than relying on a single method's limitations.
Solution Approach 2:
The patent develops a multi-functional clustering system that can adapt to different types of gas users, usage patterns, and abnormal behaviors. The system is designed to perform multiple functions including initial clustering, re-clustering based on new data, and cross-validation of abnormal user identification, making the complexity worthwhile by providing universal applicability across diverse scenarios.
Data Source
AI summary
A method, an Internet of Things (IoT) system, and a storage medium for smart gas abnormal data analysis are provided. The method may include: obtaining a user feature and a pipeline network transportation feature of each of a plurality of gas users; obtaining a first clustering result and a second clustering result by clustering the gas user based on the user feature and the pipeline network transportation feature respectively, the first clustering result and the second clustering result including one or more gas user clusters, respectively; for any one of the gas user clusters: determining, based on device use data and/or gas metering data of the gas user in the gas user cluster, a potential abnormal gas user; determining a target abnormal user based on the first abnormal user and the second abnormal user; and sending an early warning message to the target abnormal user.