Temperature-Weighted Universal Node Coordination for 3D Facility Mapping
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Solution Overview
Problem
Facilities face inefficiencies in temperature management, leading to energy waste and inconsistent storage conditions due to inadequate temperature monitoring and identification, resulting in items being stored at inappropriate temperatures and increased energy consumption.
Innovation Solution
A system for real-time temperature monitoring using sensors and computer-based modeling to optimize temperature levels and storage conditions, incorporating machine learning for interpolation and sensor placement, ensuring accurate temperature readings across the facility and efficient energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temperature sensors are placed throughout the entire 3D space of the facility to achieve accurate real-time temperature monitoring, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The facility is divided into multiple 3D spatial zones with temperature sensors strategically placed in each segment. This segmentation allows comprehensive temperature monitoring throughout the entire facility while managing sensor placement complexity through systematic zone division rather than random or uniform distribution.
Solution Approach 2:
The patent transitions from traditional 2D floor-plan sensor placement to 3D spatial distribution, incorporating vertical dimension (height) in sensor positioning. This dimensional change enables more accurate temperature mapping throughout the volume of storage locations and improves measurement precision by capturing temperature variations at different elevations.
2Reliability
If storage locations are maintained at lower temperature values to ensure item safety, then reliability is improved, but use of energy increases
Solution Approach 1:
The system dynamically adjusts storage location temperature setpoints based on real-time monitoring data and item characteristics. Instead of maintaining static low temperatures, the system optimizes temperature levels continuously, raising temperatures when safety thresholds are not at risk, thereby reducing energy consumption while maintaining reliability.
Solution Approach 2:
The patent changes the temperature parameter from fixed low values to variable values optimized for each storage location and item type. By using accurate temperature monitoring to inform dynamic parameter adjustments, the system maintains item safety while avoiding excessive energy consumption from unnecessarily low temperatures.
3Measurement precision
If more temperature sensors are deployed to verify temperature readings and eliminate hotspots, then measurement precision is improved, but loss of information increases due to data management complexity
Solution Approach 1:
The system implements feedback loops where temperature sensor data is continuously collected, analyzed, and used to adjust storage location temperature setpoints and sensor placements. This feedback mechanism ensures data quality and relevance, preventing information loss by actively managing and utilizing the data from multiple sensors rather than allowing data management complexity to degrade information quality.
4Productivity
If facility operations are automated based on real-time temperature data to reduce variability, then productivity is improved, but device complexity increases
Solution Approach 1:
The system enables storage locations and facility operations to self-adjust based on real-time temperature monitoring data. Automated controls adjust temperature setpoints and alert operators to hotspots without requiring complex centralized management, allowing the system to serve itself and improve productivity while managing automation complexity through decentralized intelligence.
Data Source
AI summary
Disclosed is a system for interpolating ambient conditions across a facility, the system including: sensors to generate sensor data indicating ambient conditions in a facility and a computer system that can: receive the sensor data from the sensors, determine real-time temperature information for different locations of the facility based on processing the received sensor data, retrieve a machine learning model that was trained using historic facility data to interpolate ambient conditions across a facility using temperature information that corresponds to a portion of the facility, apply the model to the real-time temperature information for the different locations of the facility, determine, based on applying the machine learning model, real-time temperature information for the facility, and return the real-time temperature information for the facility.