IoT Gas Data Redundancy Across Sub-Data Centers
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Solution Overview
Problem
The increasing volume of gas data in gas management systems poses challenges in processing efficiency and stability, with data center failures leading to system unavailability.
Innovation Solution
An IoT system comprising a smart gas user platform, service platform, management platform, sensor network platform, and object platform, utilizing machine learning models to determine data redundancy levels and store gas data in sub-data centers with redundant blocks, ensuring efficient and stable data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the amount of gas data is increased to improve data completeness, then data management efficiency deteriorates due to longer processing time
Solution Approach 1:
The patent segments gas data into different levels (first level gas data and second level gas data) based on importance and access frequency. First level data includes critical data requiring high redundancy and fast access, while second level data includes less critical data with lower redundancy requirements. This segmentation enables differentiated storage strategies that improve overall data management efficiency while maintaining data completeness.
Solution Approach 2:
The patent applies local quality by assigning different storage characteristics to different data levels. Critical first level data receives higher redundancy ratios and faster access paths, while less critical second level data receives lower redundancy and standard access. This localized optimization ensures that data management efficiency is improved without compromising the quality and availability of critical data.
2Reliability
If data redundancy is increased to improve data availability, then storage space consumption increases
Solution Approach 1:
The patent segments data into different redundancy levels corresponding to different data importance levels. First level critical data receives higher redundancy ratios to ensure availability, while second level data receives lower redundancy. This segmentation allows the system to maintain data availability for critical data while reducing overall storage space consumption by applying lower redundancy to less critical data.
Solution Approach 2:
The patent dynamically adjusts the redundancy ratio parameter based on data level and access patterns. By changing the redundancy parameter from high for first level data to low for second level data, the system optimizes the balance between data availability and storage space consumption, ensuring critical data remains available while minimizing unnecessary storage overhead.
3Loss of time
If data processing time is reduced to improve system response speed, then data management accuracy deteriorates
Solution Approach 1:
The patent segments data processing into different pathways based on data level. First level data undergoes rapid processing with optimized queries and caching for immediate response, while second level data can undergo more comprehensive processing when time permits. This segmentation enables the system to reduce processing time for critical data without sacrificing accuracy, as the most time-sensitive operations are performed on the most important data first.
4Quantity of substance
If data center capacity is increased to handle more data, then system stability deteriorates due to higher failure risk
Solution Approach 1:
The patent segments the data center into multiple independent sub-data centers that can operate autonomously. Each sub-data center handles specific portions of data, and the system can continue operating even if one sub-data center fails. This segmentation isolates failures to specific zones, preventing system-wide instability while maintaining the capacity to handle large volumes of data across multiple centers.
Solution Approach 2:
The patent implements beforehand cushioning by creating redundant backup sub-data centers and implementing data replication strategies. Before any failure occurs, the system maintains backup capacity and redundancy buffers that can immediately take over if a data center fails, thus preventing system instability while supporting increased data center capacity through prepared redundancy mechanisms.
Data Source
AI summary
Disclosed is a method for managing gas data, which is implemented by at least one processor of an Internet of Things (IoT) system for managing the gas data. The method comprises: obtaining to-be-stored-gas data and downstream user features; determining a user importance level based on the downstream user features; determining accessing frequency distribution features of the to-be-stored-gas data; determining a risk degree of data through a second prediction model; constructing query feature vectors based on pipeline data, and determining a risk degree of the gas pipeline based on the query feature vectors; determining a gas data level; determining a data redundancy level; generating redundant data blocks of the to-be-stored-gas data; and storing the to-be-stored gas data and the redundant data blocks in the at least one storage node of the at least one sub-data center based on loading distribution features.