Sensor Data Assimilation with Uncertainty Quantification
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Solution Overview
Problem
Current data assimilation methods face challenges in accurately combining observational data with numerical models to estimate the state of complex physical systems, particularly due to limited observations and noise in sensor data, which affects predictive accuracy and model optimization.
Innovation Solution
A system that integrates sensors with a calibrated model and a routing node, utilizing local and core data assimilation analytics to perform uncertainty quantification and combine sensor data with model predictions, enabling real-time filtering and parameter tuning to improve data quality and predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are integrated into the data assimilation framework, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the data assimilation system into separate functional modules: sensors for data collection, routing nodes for data transmission, and processing units for uncertainty quantification and state estimation. This segmentation allows each component to be optimized independently while working together to improve measurement precision without overwhelming complexity
Solution Approach 2:
The patent introduces routing nodes as intermediary components between sensors and the central processing system. These routing nodes handle data aggregation, routing, and preliminary processing, reducing the complexity burden on individual sensors and enabling scalable deployment while maintaining high measurement precision
2Reliability
If uncertainty quantification is performed in real-time, then predictive accuracy is improved, but computational resources increase
Solution Approach 1:
The patent implements local uncertainty quantification at individual sensor nodes rather than requiring centralized processing of all data. Each sensor or sensor group performs local state estimation and uncertainty analysis, which reduces the computational burden on central systems while maintaining high predictive accuracy through distributed intelligence
Solution Approach 2:
The patent applies partial uncertainty quantification by focusing computational resources on the most critical sensors and parameters rather than uniformly processing all sensor data. This selective approach maintains predictive accuracy for key system states while significantly reducing overall computational resource consumption
3Measurement precision
If sensor data is filtered to remove noise, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The patent dynamically adjusts filtering parameters based on the operational context and sensor characteristics. By changing filter strength, cutoff frequencies, and smoothing parameters in response to real-time conditions, the system optimizes the balance between noise reduction and information preservation, preventing excessive information loss while maintaining measurement precision
Data Source
AI summary
A method and system for outputting a state of a physical system using a calibrated model of the physical system, where the calibrated model is used to generate a model prediction. The system includes a plurality of sensors connected to a routing node are used to monitor measured data of the physical system. A first sensor of the plurality of sensors includes a logic module configured to determine an uncertainty quantification, and to combine the uncertainty quantification with the model prediction to output the state of the physical system.