Smart Gas IoT System Abnormal Data Visualization
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Solution Overview
Problem
Existing data visualization technologies for smart gas management do not effectively provide visual warnings for abnormalities in gas data processing, leading to potential delays in identifying and addressing issues.
Innovation Solution
A method and IoT system for data visual management of smart gas, which utilizes a machine learning-based pre-analysis model to detect abnormal data and generate visual warnings by analyzing first and second sampling data, data acquisition and summary features, and historical probability data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data visualization methods are used for smart gas management, then the system structure remains simple, but the ability to detect and warn about data abnormalities is insufficient
Solution Approach 1:
The patent applies preliminary action by introducing a pre-analysis model that performs preliminary detection and analysis of data abnormalities before the main data processing occurs. The model pre-processes sampling data, extracts features, and identifies potential anomalies in advance, allowing the system to prepare warning visualizations proactively rather than reactively, thus improving reliability without proportionally increasing complexity.
Solution Approach 2:
The patent uses an intermediary approach by introducing a dedicated pre_analysis_model as a mediator between raw data collection and main data processing systems. This intermediate component handles the complex task of abnormality detection, feature extraction, and probability calculation, isolating the complexity from the core data processing workflow while enhancing the overall system's abnormality detection capability.
2Loss of time
If real-time monitoring and visual warnings are implemented, then the responsiveness to data abnormalities improves, but the data processing complexity increases
Solution Approach 1:
The system performs preliminary feature extraction and abnormality analysis on sampling data before full data processing occurs. By pre-identifying potential abnormalities and calculating their probabilities in advance, the system reduces the time needed for real-time detection and response, as the heavy analytical work is already completed on smaller sample sets rather than entire data streams.
Solution Approach 2:
The patent applies partial action by focusing computational resources on analyzing only sampling data and extracting specific abnormality-related features rather than processing complete datasets in real-time. This selective approach to data analysis reduces overall processing complexity while maintaining effective monitoring coverage through strategic sampling and feature selection.
Data Source
AI summary
The embodiment of the present disclosure provides a method and an Internet of Things (IoT) system for data visual management of smart gas. The IoT system includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform. The smart gas management platform transmits visualization data to a target platform for display. The method includes: determining abnormal data and an abnormal probability corresponding to the abnormal data through a pre-analysis model based on first sampling data, second sampling data, a data acquisition feature, and a data summary feature, the pre-analysis model being a machine learning model, and the pre-analysis model including a pre-processing layer and an analysis layer; generating second visualization data based on the abnormal data and the abnormal probability; and controlling the target platform to display the second visualization data.