Smart Gas Data Storage Using Time and Reliability Evaluation
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Solution Overview
Problem
Existing gas data processing systems face challenges in evaluating the timeliness and completeness of gas data, leading to inefficient data storage management.
Innovation Solution
A method for smart gas data storage is implemented using an IoT system, which includes a smart gas management platform that obtains raw gas data, calculates collection time characteristics, evaluates time and data reliability, and generates storage instructions for efficient data storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If comprehensive evaluation of gas data timeliness and completeness is implemented, then data storage management efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a data quality evaluation module as an intermediary between data collection and storage management. This module assesses timeliness and completeness of gas data using predefined criteria, generating quality scores that guide storage decisions. The intermediary handles the complexity of comprehensive evaluation centrally, allowing the rest of the system to operate with simplified logic while still achieving improved storage management efficiency.
2Reliability
If multiple evaluation criteria for gas data quality are used, then data reliability assessment is improved, but processing time increases
Solution Approach 1:
The patent establishes predefined evaluation criteria and weighting schemes for assessing data timeliness and completeness before actual data processing begins. These evaluation rules are configured in advance, allowing the system to quickly apply established metrics to incoming gas data without performing complex real-time analysis. This preliminary preparation maintains high data reliability assessment while minimizing processing time overhead.
3Productivity
If differentiated storage strategies are implemented based on data quality, then data storage efficiency is improved, but storage system complexity increases
Solution Approach 1:
The patent applies differentiated storage strategies based on local data quality characteristics. High-quality data (meeting timeliness and completeness thresholds) is routed to primary storage systems for immediate analysis, while lower-quality data is directed to secondary or archival storage. This local quality-based differentiation improves overall storage efficiency by optimizing resource allocation without requiring complete system-wide complexity, as each data stream is handled according to its specific quality profile.
Data Source
AI summary
Embodiments of the present disclosure provide a method for smart gas data storage, an Internet of Things system, and a storage medium. The method includes: obtaining at least one type of raw gas data, calculating collection time characteristics of the raw gas data based on a collection time stamp when the raw gas data is collected; evaluating a time reliability of the raw gas data based on the collection time characteristics; determining at least one set of gas sampling data of the raw gas data based on gas importance of the raw gas data; evaluating a data reliability of the raw gas data based on the at least one set of gas sampling data; and generating at least one storage instruction based on the time reliability and the data reliability to store the raw gas data in a corresponding data storage area.


