Sensor Parameter Control Using Environment Data Correlation
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Solution Overview
Problem
Current data management systems in industrial environments lack an efficient method to automatically set measurement parameters for sensors based on environment data, relying on operator experience or manual adjustments, which can lead to suboptimal sensor settings and lack of consideration for environmental conditions beyond human sensory detection.
Innovation Solution
A data management system that acquires environment data from IoT sensors and selects relevant measurement sensors to set parameters dynamically based on distance, type of physical quantity measured, and correlation with environment data changes, allowing for automatic adjustment of measurement cycles and sensitivities in response to environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual parameter setting by operators is used, then ease of operation is maintained, but measurement precision and reliability deteriorate due to lack of consideration for environmental conditions
Solution Approach 1:
The sensor system automatically adjusts its own measurement parameters (measurement cycle, sensitivity) based on environmental data without requiring manual intervention. The parameter setting unit automatically receives environment data, determines appropriate parameters, and configures the measurement sensor, enabling the system to serve itself and eliminate dependence on operator expertise.
Solution Approach 2:
The system continuously monitors environmental data and uses this feedback to dynamically adjust measurement parameters. The parameter setting unit receives real-time environment data, processes it to determine optimal parameters, and applies these parameters to the sensor, creating a closed-loop feedback system that continuously optimizes measurement accuracy based on current environmental conditions.
2Measurement precision
If automatic parameter setting based on environment data is implemented, then measurement precision improves, but device complexity increases
Solution Approach 1:
The parameter setting unit serves multiple functions: it receives environment data from IoT sensors, processes this data to determine optimal parameters, stores parameter information in a parameter database, and configures the measurement sensor. By consolidating these diverse functions into a single multi-functional unit, the system reduces overall complexity while maintaining improved measurement precision.
Solution Approach 2:
The system merges the environment data acquisition function, parameter determination function, parameter storage function, and sensor configuration function into an integrated parameter setting system. This consolidation combines multiple previously separate operations into a unified process, reducing system complexity while achieving automatic optimized parameter setting.
3Measurement precision
If frequent measurement is performed to capture environmental changes, then data accuracy improves, but energy consumption increases
Solution Approach 1:
The measurement system dynamically adjusts its operation based on environmental conditions. When environmental data indicates significant changes or anomalies, the system increases measurement frequency to capture detailed data. When conditions are stable, the system reduces measurement frequency, creating a dynamic adaptation that optimizes data accuracy while minimizing unnecessary energy consumption from continuous high-frequency measurements.
Solution Approach 2:
The system changes the measurement parameter (measurement cycle frequency) based on environmental data analysis. The parameter setting unit determines appropriate measurement cycles by analyzing environment data characteristics, adjusting the measurement frequency parameter dynamically. This allows the system to perform frequent measurements only when environmentally justified, reducing overall energy consumption while maintaining data accuracy when needed.
Data Source
AI summary
Provided is a data management system including an environment data acquisition unit configured to acquire, from an environment sensor, environment data obtained by measuring a production environment where a product is produced from a raw material, a sensor selection unit configured to select a specific sensor from among measurement sensors, each of which is configured to measure a production target serving as a target of the production, according to a relevance with respect to the environment sensor, and a parameter setting unit configured to set a measurement parameter in the specific sensor based on the environment data.