Sensor Data Testing System for Monitoring Accuracy
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Solution Overview
Problem
Traditional central monitoring systems for buildings struggle with inaccurate sensor data, leading to potential system control deviations due to the inability to effectively detect and address abnormal sensors in real-time.
Innovation Solution
A testing system and method that compares and analyzes sensor data from multiple sensors of the same category, using a server with data pre-processing, value classification, and comparison modules to identify significant differences, record labels for abnormal sensors, and issue alarms for user notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional central monitoring systems use simple upper and lower limit definitions or regular sensor calibration, then the system structure remains simple and operation is easy, but the accuracy of sensor data cannot be ensured during daily operation due to sensor deviation
Solution Approach 1:
The system performs preliminary data cleaning and preprocessing on sensor data before analysis, including handling missing values, outliers, and data quality issues. This preliminary action ensures that the data is ready for comparison and reduces the need for complex real-time processing, thereby improving measurement precision without significantly increasing device complexity
Solution Approach 2:
The patent introduces an intermediary data processing layer between sensors and the monitoring system, which includes data cleaning, preprocessing, and comparison modules. This intermediary layer filters and prepares sensor data before it reaches the decision-making system, improving data accuracy while maintaining manageable system complexity through modular design
2Reliability
If multiple sensors of the same category are compared and analyzed in real-time to detect abnormal sensors, then the accuracy of sensor data is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the sensor network into groups based on sensor category and location, comparing only sensors within the same group. This segmentation reduces the number of comparisons needed while maintaining reliability, as sensors of the same category should produce similar readings under the same conditions. The segmentation approach divides the complex monitoring task into manageable subsets
Solution Approach 2:
The system changes parameters by considering multiple factors including spatial relationships, temporal patterns, and environmental conditions when comparing sensor data. By adjusting comparison parameters based on sensor category, location, and operational context, the system improves reliability without requiring excessive computational resources for all possible comparisons
3Speed
If sensor data is collected and analyzed continuously to identify abnormal sensors, then the detection speed improves, but the energy consumption and computational load increase
Solution Approach 1:
The system implements periodic data collection and analysis cycles rather than continuous processing. Sensors transmit data at intervals, and the server performs comparison and analysis operations periodically. This periodic action maintains fast detection capability while significantly reducing energy consumption and computational load compared to continuous real-time processing
Solution Approach 2:
The patent implements a multi-stage filtering approach where obvious normal readings are quickly skipped over, and only suspicious or anomalous data points undergo detailed analysis. This skipping mechanism allows the system to maintain high detection speed for critical issues while reducing overall computational load and energy consumption by avoiding unnecessary processing of normal data
Data Source
AI summary
A testing system for sensor data of a monitoring system including a server and multiple sensors is disclosed. The server includes a data pre-process module, a value classifying-computing module, and a value comparing module. The data pre-process module performs a data cleaning procedure to sensor data of the multiple sensors to generate multiple cleaned data. The value classifying-computing module computes a difference-value combination of multiple cleaned data of every two sensors in a designated order-direction. The value comparing module subtracts a measuring-accuracy value from every difference-value of each difference-value combination to obtain multiple second difference-value combinations, computes a first feature value of each second difference-value combination, and records a label to two sensors corresponding to one of the second difference-value combinations when a p-value corresponding to the first feature value of the second difference-value combination is determined to be less than a threshold.