Industrial IoT Storage Configuration With Dynamic Data Placement
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Solution Overview
Problem
The exponential growth of data volume in industrial IoT environments necessitates efficient and intelligent data storage strategies to manage and store data effectively across different production links, as existing systems struggle to optimize storage allocation and prevent data loss while minimizing resource waste.
Innovation Solution
A system and method for storage configuration in an industrial IoT data center, involving an industrial IoT management platform that determines data upload parameters based on data attributes, historical records, and acquisition frequency, generating configuration instructions to allocate storage space dynamically across cache and sub-databases, ensuring timely and efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in all industrial IoT sensing network platforms to ensure data availability, then data reliability is improved, but storage resource waste increases due to redundant storage across multiple platforms
Solution Approach 1:
The patent segments the storage architecture into multiple levels: local sensing network platform storage, regional data center storage, and cloud storage. Data is divided and stored across these segments based on access frequency and importance, with hot data at local platforms and cold data at cloud storage, eliminating redundant storage while maintaining availability
Solution Approach 2:
The patent introduces a data placement service as an intermediary that manages data distribution across storage locations. This service uses data labels and policies to determine optimal storage locations, acting as a mediator between data generation and storage resources to prevent redundant storage while ensuring data accessibility
2Quantity of substance
If storage space is allocated statically to all industrial IoT sensing network platforms to ensure sufficient capacity, then storage capacity adequacy is improved, but storage utilization efficiency deteriorates due to idle space in some platforms and overflow in others
Solution Approach 1:
The patent implements dynamic storage allocation where data placement policies and storage capacity are adjusted in real-time based on data access patterns, data volume changes, and platform capabilities. The data placement service continuously monitors and repositions data between storage locations to optimize utilization, transforming static allocation into a dynamic adaptive system
Solution Approach 2:
The patent changes storage allocation parameters dynamically by modifying data placement policies based on data labels (access frequency, importance, size) and platform parameters (storage capacity, computational power). This allows the system to adapt storage distribution to changing conditions, improving both capacity adequacy and utilization efficiency
3Device complexity
If manual data management strategies are used in industrial IoT systems to simplify system complexity, then device complexity is reduced, but productivity deteriorates due to inability to optimize storage dynamically for different data types
Solution Approach 1:
The patent implements self-service data management where the data placement service automatically analyzes data labels, determines optimal storage locations, and executes data movement without manual intervention. The system self-configures storage policies based on data characteristics and platform capabilities, reducing manual complexity while maintaining high storage efficiency
Solution Approach 2:
The patent incorporates feedback mechanisms where the data placement service monitors data access patterns, storage utilization, and system performance, then uses this feedback to continuously optimize data placement decisions. This closed-loop control enables automatic adaptation to changing conditions without increasing system complexity
Data Source
AI summary
The present disclosure relates to a system and method for storage configuration based on an industrial internet of things (IoT) data center. The method includes: determining a data upload parameter based on data attribute information, historical retrieval records, and historical processing records of data to be processed; generating database configuration parameters based on the data upload parameter and a data acquisition frequency; generating, based on the data upload parameter, a data upload instruction, and transmitting the data upload instruction to the industrial IoT sensing network platform; generating operating parameters of a second storage component based on cache database configuration parameters; controlling a second storage controller to allocate storage space to a cache database based on the operating parameters of the second storage component; and generating, based on sub-database configuration parameters, a sub-database configuration instruction, and issuing the sub-database configuration instruction to the industrial IoT sensing network platform.