System and method for inferring or prompting HVAC actions based on large data standard deviation based metric
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing and presenting large datasets of temperature data from HVAC systems is challenging due to varying alert thresholds and the need to normalize sensor signals from hundreds or thousands of sensors, making it difficult for users to determine meaningful actions and identify uniformity or lack of uniformity in temperature distribution within a data center or building.
Innovation Solution
A computer-based system that includes a processor for communicating with sensors and a graphical user interface (GUI) module to generate real-time graphs indicating sensors within, above, or below predetermined temperature ranges, along with standard deviation information, allowing users to easily visualize and manage temperature conditions and associate sensors with groups for effective resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large datasets of temperature data from hundreds or thousands of sensors are collected and monitored, then comprehensive temperature monitoring coverage is improved, but data analysis complexity and difficulty increase
Solution Approach 1:
The patent segments the large dataset of temperature readings into individual sensor groupings, where each grouping contains readings from multiple sensors. This segmentation allows the system to process and analyze data in manageable units rather than as one overwhelming large dataset, reducing analysis complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent introduces an intermediary processing layer that calculates statistical metrics (mean, standard deviation, coefficient of variation) for each sensor grouping. This intermediary layer transforms raw temperature data into meaningful aggregated metrics, serving as a bridge between the large number of individual sensors and the user interface, thereby simplifying data analysis.
2Measurement precision
If individual sensor values are monitored in detail, then measurement precision is improved, but ease of operation deteriorates as individual values become irrelevant
Solution Approach 1:
The patent merges individual sensor values within each sensor grouping into aggregated statistical metrics (mean temperature, standard deviation, coefficient of variation). This merging process combines the precision of individual measurements into meaningful group-level indicators that are directly actionable, making it easier for users to identify cooling needs without being overwhelmed by individual sensor values.
Solution Approach 2:
The patent transforms the parameter representation from individual temperature values to statistical parameters (mean, standard deviation, coefficient of variation). This parameter change converts precise but overwhelming individual measurements into actionable group-level metrics that directly indicate cooling resource allocation needs, improving ease of operation.
3Measurement precision
If alert thresholds are customized for various temperature sensors, then measurement precision is improved, but device complexity increases due to normalization requirements
Solution Approach 1:
The patent creates a universal analysis framework that works with customized alert thresholds for each sensor. By calculating statistical metrics (coefficient of variation, standard deviation) for each sensor grouping, the system provides a unified method that accommodates diverse threshold requirements without requiring separate analysis procedures, thereby managing normalization complexity.
Solution Approach 2:
The patent applies parameter transformations to normalize data across sensors with different thresholds. By converting individual sensor readings into statistical parameters (z-scores, standard deviations from mean), the system creates a common framework for comparison that respects customized alert thresholds while simplifying the normalization process.
4Loss of information
If real-time temperature data from all sensors is displayed, then information completeness is improved, but ease of operation deteriorates due to information overload
Solution Approach 1:
The patent extracts the most critical information from complete temperature data by calculating and displaying key statistical metrics (mean temperature, standard deviation, coefficient of variation) for each sensor grouping. This extraction provides a condensed summary that maintains information completeness in terms of thermal conditions while eliminating information overload by removing redundant individual sensor readings.
Solution Approach 2:
The patent segments the complete temperature dataset into discrete sensor groupings, each represented by its statistical metrics. This segmentation organizes comprehensive temperature information into manageable, comparable units that can be quickly assessed, maintaining data completeness while improving ease of operation through structured presentation.
Data Source
Figure 1
Figure 2A~2E
Figure 3
AI summary
The present disclosure relates to a computer based system for collecting, analyzing and presenting temperature information from sensors associated with an HVAC system. The system makes use of a processor for communicating with the plurality of sensors and obtaining temperature data reported by each of the sensors. A graphical user interface (GUI) module is provided which is embodied in a non-transient medium and configured to run on a computing device having a display screen. The user GUI module uses the temperature data, in real time, to generate a graph on the display screen. The graph includes a plurality of components each having different indicia to indicate groups of sensors that are below a predetermined lower temperature limit, above a predetermined upper temperature limit, and within a predetermined temperature range. Standard deviation information may be supplied with the graph which relates to the maximum standard deviation of those groups of sensors that are reporting temperatures above and below the predetermined temperature range.