Extrapolation Data Set for Synchronized Network Processing
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Solution Overview
Problem
Existing methods for synchronously providing data on distributed devices in networks face challenges in achieving precise, timely data synchronization, particularly in time-critical applications like rapid control prototyping and hardware-in-the-loop simulations, where simultaneous data availability is crucial, and current solutions are limited by high data transmission demands and sensitivity to errors.
Innovation Solution
A method where a master device transmits an extrapolation data set including an update time point to slave devices, allowing both to calculate new data using extrapolation, reducing the need for individual data item transmission and enabling error correction protocols, while maintaining data synchronization across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual data items are transmitted from master device to slave devices for synchronous provision, then data synchronization is achieved, but data transmission volume and channel demands become excessively high
Solution Approach 1:
The patent extracts only the essential parameters needed for data reconstruction (extrapolation data set including update time point) from the complete data items, transmitting only this condensed information over the network. The actual data items are then locally reconstructed at each slave device using the extrapolation function, dramatically reducing network transmission volume while maintaining synchronization.
Solution Approach 2:
Instead of transmitting complete data items to each slave device, the master device transmits a template or model (extrapolation data set) that enables each slave device to locally generate copies of the required data items through extrapolation calculations, reducing redundant data transmission.
2Productivity
If high transmission rates are demanded for reliable synchronous data provision, then data synchronization is improved, but sensitivity to errors and limitations on other data channel uses increase
Solution Approach 1:
The patent extracts and transmits only the minimal necessary information (update time point and extrapolation parameters) rather than complete data streams, reducing the overall data volume subject to transmission errors and allowing more robust error correction protocols to be applied to the smaller data set.
Solution Approach 2:
The master device prepares and transmits the extrapolation data set in advance, including the update time point, before the actual data items are needed at slave devices. This preliminary transmission allows time for error detection and correction without compromising the timing requirements for synchronous data provision.
3Loss of time
If data is transmitted and immediately activated on slave devices, then synchronous data availability is achieved, but any transmission errors directly affect data reliability
Solution Approach 1:
The patent transmits only the essential extrapolation parameters and update time point rather than complete data items, reducing the amount of data that could potentially contain errors. The local extrapolation process at slave devices then generates the actual data items from this error-resistant parameter set.
Solution Approach 2:
The system incorporates error detection and correction mechanisms that operate on the transmitted extrapolation data set before local data generation. This feedback loop ensures that any transmission errors are detected and corrected before the extrapolation process begins, preventing error propagation to the final data items.
Data Source
AI summary
A method for synchronously providing data on distributed devices of a network includes storing, by a master device, an extrapolation data set including at least one update time point. The update time point is in the future and marks the beginning of an extrapolation interval. The master device transmits the extrapolation data set to a slave device. Starting at an update time point, new data is calculated separately on the master device and the slave device by extrapolation using the extrapolation data set available on both the master device and the slave device. The method steps are repeated for subsequent extrapolation intervals.