Tensorial Database for Automation Network Data
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Solution Overview
Problem
Current methods for evaluating and operating automation networks are inadequate, particularly in handling complex nonlinear automation data, and existing database systems face challenges in efficiently storing and analyzing multidimensional data.
Innovation Solution
A method involving coupling node devices with edge devices, stipulating a global time, recording and storing address and content elements in a tensorial database structure, and recording operating parameters to facilitate efficient data management and analysis across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multidimensional automation data are stored in relational databases, then data storage is achieved, but data access speed and storage capacity utilization deteriorate
Solution Approach 1:
The patent transitions from traditional relational database structures to a tensor-based multidimensional data structure. This dimensional transformation allows automation data to be organized along multiple axes (time, spatial coordinates, measurement variables), enabling both efficient storage compression and rapid access through tensor operations. The tensor structure inherently captures the multidimensional nature of automation data, improving both storage utilization and access speed simultaneously.
2Adaptability or versatility
If different database systems are used in parallel for analysis and evaluation, then analysis capabilities are improved, but data volume and system complexity increase
Solution Approach 1:
The patent creates a universal tensor-based data structure that can serve multiple analytical purposes simultaneously. The same tensor structure supports various types of analysis operations without requiring separate database systems, thereby maintaining analytical versatility while reducing system complexity. The tensor framework provides a unified interface for different evaluation tasks.
3Device complexity
If linear reduction is applied to automation data, then data complexity is reduced, but handling of complex nonlinear automation data deteriorates
Solution Approach 1:
The patent changes the fundamental parameter representation from linear scalar values to multidimensional tensor structures. This parameter transformation enables the data structure to naturally represent nonlinear relationships and complex patterns in automation data while maintaining manageable complexity through the mathematical properties of tensors.
Data Source
AI summary
A method for operating a network, such as an automation network, for example, has multiple node devices provided that are networked to one another. There is a global time available, and the node devices record their operating parameters. The operating parameters are allocated to a respective address element as content elements in order to be stored in a tensorial database structure. Control or adaptation of the operation of the network with its node devices and couplings is facilitated thereby. The method is suitable particularly for use in supply networks, automated production installations, communication networks, transport networks and logistical networks. The proposed storing allows easy visualisation, depiction and evaluation of operating states of the network and of its node devices.