Blockchain-Triggered Simulation Model Recalibration
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Solution Overview
Problem
Existing simulation models in organizations like oil and gas companies face inefficiencies and reduced quality due to manual updates, which can be neglected or postponed, leading to incorrect results when not capturing up-to-date data.
Innovation Solution
A system and method using a blockchain to automate simulation model updates by incorporating a recalibration module that adjusts operating parameters based on predetermined threshold values, ensuring the simulation model data remains within acceptable performance parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual updates of simulation models are performed, then flexibility in update timing is maintained, but updates are frequently neglected or postponed leading to outdated results
Solution Approach 1:
The system enables automatic self-updating of simulation models through blockchain-triggered recalibration. When new data is added to the blockchain, the system automatically detects this and triggers model recalibration without requiring manual intervention, thus maintaining reliability while eliminating the need for continuous manual monitoring and updates.
Solution Approach 2:
The system implements a feedback mechanism where the blockchain state is continuously monitored for new data entries. When new data is detected on the blockchain, this triggers an automatic recalibration process that adjusts the simulation model parameters, ensuring the model remains synchronized with the latest data without manual intervention.
2Reliability
If continuous calibration of simulation models is performed, then model quality is maintained, but computational resources and time are wasted
Solution Approach 1:
Instead of continuous calibration, the system performs recalibration periodically based on blockchain data additions. Each recalibration is triggered only when new data is added to the blockchain, creating an event-driven periodic update mechanism that maintains model quality without the overhead of continuous calibration cycles.
Solution Approach 2:
The system prepares the recalibration mechanism in advance by monitoring the blockchain for new data entries. When data is added to the blockchain, the system is already positioned to immediately trigger recalibration, ensuring timely updates without requiring continuous active calibration processes.
3Loss of energy
If simulation models are updated manually, then resource consumption is reduced, but the models do not capture up-to-date data leading to incorrect results
Solution Approach 1:
The system uses blockchain data additions as feedback triggers for automatic recalibration. When new data is added to the blockchain, this automatically triggers model updates, ensuring the simulation model captures up-to-date data without requiring continuous manual monitoring or excessive computational resources.
Solution Approach 2:
The simulation model system automatically monitors the blockchain and performs self-updates when new data is detected. This self-service mechanism ensures data accuracy is maintained through automatic recalibration triggered by blockchain events, eliminating the need for manual updates while capturing the latest data without excessive resource consumption.
Data Source
AI summary
A simulation model update system and method receive input data from a data source and receive simulation model data from a simulation model of a component of an organization. The simulation model generates the simulation model data from the input data. The received simulation model data is stored in a blockchain, and a recalibration module updates the simulation model when a metric value of the simulation model data exceeds a predetermined threshold value. A method implements the simulation model update system.