Method for calibrating sensors in chiller plant system based on logic self-consistency
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Solution Overview
Problem
Existing sensor calibration methods in chiller plant systems, which rely on static calibration or mathematical models, fail to accurately account for dynamic changes in complex operating environments, leading to significant deviations in sensor measurements.
Innovation Solution
A method for calibrating sensors in a chiller plant system based on logic self-consistency, involving the establishment of a sensor true value logic related constraint system, construction of a system degree of logic non-consistency calculation function, and optimization of a sensor correction function using steady-state measurement data to minimize logic non-consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static calibration using standard sensors is used, then measurement precision is improved at specific conditions, but adaptability to dynamic operating environments deteriorates
Solution Approach 1:
The patent applies dynamics by transitioning from static calibration to dynamic calibration. The calibration method uses real-time operational data from the chiller plant system to continuously update and optimize the calibration parameters, allowing the system to adapt to changing operating conditions while maintaining measurement precision.
Solution Approach 2:
The patent implements parameter changes by adjusting calibration parameters based on operating conditions. The system monitors multiple parameters (temperature, pressure, flow rates) and dynamically modifies calibration factors to compensate for environmental variations, thereby improving both precision and adaptability.
2Adaptability or versatility
If mathematical model estimation is used, then calibration coverage is improved, but reliability deteriorates due to model errors
Solution Approach 1:
The patent applies feedback by using actual sensor measurements and operational data to validate and refine the mathematical model. The system continuously compares model predictions with real-world measurements and adjusts calibration parameters accordingly, reducing model errors and improving reliability while maintaining broad coverage.
Solution Approach 2:
The patent implements preliminary action by performing initial calibration using mathematical models to establish baseline parameters, then refining these parameters through iterative optimization using operational data. This approach ensures comprehensive coverage while progressively improving reliability through data-driven adjustments.
3Ease of manufacture
If static calibration is performed, then calibration simplicity is improved, but measurement accuracy in changing environments deteriorates
Solution Approach 1:
The patent applies self-service by enabling the calibration system to automatically adjust its own parameters using operational data from the chiller plant. The system performs self-calibration by identifying patterns in measurement deviations and autonomously updating calibration factors, maintaining simplicity while improving accuracy in dynamic conditions.
Data Source
AI summary
A method for calibrating sensors in a chiller plant system based on logic self-consistency, comprising the following steps: step 10: establishing a sensor true value logic related constraint system, and constructing a system degree of logic non-consistency calculation function, based on a sensor deployment structure in the chiller plant system; step 20: constructing a sensor correction function; step 30: collecting measurement data of sensors in the chiller plant system within a preset time period, and constructing a steady-state measurement data set; step 40: optimizing the sensor correction function based on the steady-state measurement data set, with a system degree of logic non-consistency as an optimization objective, and stopping optimization until an optimization cut-off condition is met, to obtain an optimized sensor correction function; step 50: using a correction value outputted by the optimized sensor correction function as calibrated data.