IIoT Data Management With Distributed Classification And Storage
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Solution Overview
Problem
Existing IIoT data storage and processing are centralized in the management platform, leading to poor data management and high pressure on the system, which can cause collapse, especially with massive data generated in industrial production.
Innovation Solution
Implement a data management system with a sensor network platform and management platform structured as general databases and sub-platforms, performing multiple classifications and storage based on data type and function types to distribute data processing and storage efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data storage and processing are centralized in the management platform, then data management is simplified, but system pressure increases and reliability decreases
Solution Approach 1:
The patent segments the centralized management platform into multiple distributed edge computing nodes. Each node independently processes and stores data locally, eliminating the single-point bottleneck. The segmentation divides the system into hierarchical levels (edge nodes, regional centers, cloud platform), where each level handles appropriate data processing tasks, thereby maintaining operational simplicity while distributing system pressure to enhance reliability.
Solution Approach 2:
The patent introduces a spatial dimension to data management by deploying edge computing nodes across multiple geographic locations and organizational levels. This transforms the traditional single-dimension centralized architecture into a multi-dimensional distributed architecture, allowing data to be processed and stored at multiple spatial points simultaneously, thus reducing pressure on any single point while maintaining manageable access through hierarchical organization.
2Loss of information
If massive amounts of industrial production data are processed centrally, then comprehensive data analysis is achieved, but processing pressure and energy consumption increase
Solution Approach 1:
The patent extracts data processing functions from the centralized cloud platform and places them at the edge computing nodes closest to the data sources. This extraction allows local preprocessing, filtering, and initial analysis to occur at the edge, reducing the volume of data that needs to be transmitted and processed centrally. Comprehensive analysis is maintained through hierarchical aggregation, where only essential and aggregated data moves to higher levels, thereby preserving analytical completeness while significantly reducing energy consumption.
Solution Approach 2:
The patent implements preliminary data processing and filtering at the edge computing nodes before data is transmitted to higher levels. This preliminary action includes local data validation, preprocessing, and aggregation, which reduces the burden on centralized systems. By performing these actions in advance at the source, the system maintains comprehensive analysis capabilities while minimizing the energy required for centralized processing and data transmission.
3Ease of operation
If data is stored in a single centralized database, then data access is simplified, but data retrieval efficiency decreases under high load
Solution Approach 1:
The patent segments the centralized database into multiple distributed data storage nodes organized in a hierarchical structure. Each edge computing node maintains local data storage, and data is distributed across regional centers and cloud platforms based on access patterns and data types. This segmentation allows simultaneous access to different data portions across multiple nodes, maintaining simple access interfaces through unified naming conventions while dramatically improving retrieval efficiency under high load through parallel access capabilities.
Data Source
AI summary
The present disclosure provides a data management method and device based on IIoT, and an electronic device. The data management method comprises: generating a data acquisition instruction and sending the data acquisition instruction to an object platform, transmitting and storing raw data acquired by the object platform to a first general database; performing a first classification on the raw data to obtain a plurality of first data sets; storing the plurality of first data sets into a plurality of sub-databases, and transmitting and storing the plurality of first data sets to a second general database; performing a second classification on the first data sets based on function types of service sub-platforms to obtain a plurality of second data sets; and storing the plurality of second data sets into the sub-databases corresponding to different service sub-platforms based on a prioritization of the plurality of second data sets to realize data management.