Sensor Data Visualization Using Interpolation and Heatmaps
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Solution Overview
Problem
Existing methods for monitoring environmental variations such as temperature and humidity in large industrial facilities are limited in identifying specific areas of concern and do not provide adequate density of measurements, making it difficult to create comprehensive visualizations.
Innovation Solution
A sensor data visualization system that includes a computing device connected to a wireless sensor network, using modules for sensor data reception, interpolation, image generation, and alerting, which generates heatmaps and alerts based on sensor data from multiple devices, providing a detailed visualization of surveyed areas and detecting anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If spot measurements are used, then device complexity is reduced, but measurement precision and comprehensive survey capability deteriorate
Solution Approach 1:
The system divides the surveyed area into multiple zones with different measurement densities. High-density measurement zones are placed in areas of concern where detailed analysis is needed, while low-density zones cover broader areas. This segmentation allows the system to achieve comprehensive survey capability without uniformly high device complexity throughout the entire facility.
Solution Approach 2:
The system implements variable measurement density across different spatial locations. Rather than uniform spot measurements or uniform continuous monitoring, the system adapts measurement density to local requirements - using higher density in critical areas and lower density in less critical areas. This resolves the contradiction by improving measurement precision where needed while maintaining overall system simplicity.
2Measurement precision
If continuous monitoring is implemented, then measurement precision improves, but energy consumption increases
Solution Approach 1:
The system implements periodic measurements rather than continuous monitoring. Sensors take measurements at predetermined time intervals, which reduces energy consumption compared to continuous operation while still providing sufficient temporal resolution for detecting environmental variations. The periodic action is adjusted based on the specific monitoring requirements of different zones.
Solution Approach 2:
The system maintains continuous survey coverage through coordinated periodic measurements from multiple sensors rather than requiring each sensor to operate continuously. The useful action of environmental monitoring is maintained continuously across the facility by having different sensors active at different times, reducing individual sensor energy consumption while preserving overall monitoring continuity.
3Measurement precision
If high-density measurements are used, then measurement precision improves, but data processing complexity increases
Solution Approach 1:
The data processing system divides the facility into zones and processes measurement data separately for each zone. This segmentation allows high-density measurements to be processed in manageable portions rather than as one large dataset, reducing overall processing complexity while maintaining high measurement precision within each zone.
Solution Approach 2:
The system applies different processing algorithms and densities to different zones based on local requirements. Areas of concern receive high-density processing with detailed analysis, while other areas use lower-density processing. This local differentiation reduces total data processing complexity while maintaining high precision where it is most needed.
4Loss of information
If comprehensive survey coverage is achieved, then information completeness improves, but device complexity increases
Solution Approach 1:
The system combines data from multiple sensors and measurement points to create comprehensive survey coverage. Rather than deploying a single complex sensor system, multiple simpler sensors are coordinated to collectively provide complete facility coverage. This merging approach achieves information completeness while keeping individual device components relatively simple.
Solution Approach 2:
The system uses multi-functional sensors that can measure multiple environmental parameters (temperature, humidity, etc.) simultaneously. This universality allows comprehensive environmental data collection with fewer devices, reducing overall system complexity while maintaining complete information coverage across the facility.
Data Source
AI summary
In embodiments, apparatuses, methods and storage media (transitory and non-transitory) are described that receive a plurality of sensor data values and a plurality of location data values corresponding to the sensor data values, may generate interpolated data values for a surveyed area, and may generate an image corresponding to the surveyed area based at least in part on the sensor data values and the interpolated data values. Other embodiments may be described and/or claimed.