Neural Network Temperature Mapping for Non-Uniform Room Distribution
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Solution Overview
Problem
Current temperature control systems in buildings rely on single temperature sensors, which provide inaccurate representations of temperature distribution due to non-uniform temperature distribution, leading to overestimation or underestimation based on sensor placement relative to windows or heating/cooling sources.
Innovation Solution
A computing device equipped with a neural network inference engine uses predictive models to infer a two-dimensional temperature mapping by receiving temperature measurements from multiple sensors located around the area's periphery, processing these inputs to provide accurate temperature values across a grid of zones within the area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single temperature sensor is used to measure temperature in an area, then the device complexity is reduced, but the measurement precision deteriorates due to non-uniform temperature distribution
Solution Approach 1:
The area is divided into multiple zones with temperature sensors strategically placed in each zone to capture local temperature variations. This segmentation allows the system to measure temperature distribution across different regions rather than relying on a single sensor, thereby improving measurement precision while maintaining reasonable device complexity
Solution Approach 2:
The patent transitions from single-point temperature measurement to two-dimensional temperature mapping by arranging sensors in a spatial grid pattern. This dimensional expansion enables the system to capture temperature distribution across the area, converting a one-dimensional single-value measurement into a two-dimensional temperature field representation
2Measurement precision
If multiple temperature sensors are deployed around the periphery of the area, then the measurement precision improves through better temperature distribution capture, but the device complexity increases
Solution Approach 1:
Temperature sensors are pre-positioned at fixed locations around the periphery of the area before measurement begins. This preliminary placement optimizes the spatial distribution of sensors to capture temperature patterns effectively, allowing the system to achieve high measurement precision with a predetermined sensor configuration rather than requiring dynamic sensor adjustment
Solution Approach 2:
The patent introduces a computing device as an intermediary that receives temperature measurements from multiple sensors, applies predictive modeling algorithms, and generates a synthesized two-dimensional temperature map. This intermediary processing layer consolidates data from multiple sensors and compensates for measurement uncertainties, improving overall measurement precision while reducing the need for an equally large number of physical sensors
Data Source
AI summary
Computing device and method for inferring via a neural network a two-dimensional temperature mapping of an area. A predictive model is stored by the computing device. The computing device receives a plurality of temperature measurements transmitted by a corresponding plurality of temperature sensors located at a corresponding plurality of locations on a periphery of the area. The computing device executes a neural network inference engine, using the predictive model for inferring outputs based on inputs. The inputs comprise the plurality of temperature measurements. The outputs consist of a plurality of temperature values at a corresponding plurality of zones, the plurality of zones being comprised in a two-dimensional grid mapped on a plane within the area. For instance, the area is a room of a building, the periphery is an interface of a ceiling and walls of the room, and the plane is a horizontal plane within the room.