Weather Drone Sensor Thresholding for Corrupt Data Prevention
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Solution Overview
Problem
Weather drones often spend extended periods capturing large volumes of weather data, which can include corrupt data due to sensor malfunctions, leading to significant time and computational resources being wasted in handling and identifying such data.
Innovation Solution
A weather drone equipped with a processor that analyzes data in real-time, preventing further storage if it exceeds or falls below threshold values, thereby minimizing the recording and storage of corrupt data, and optionally communicating with a base station for instructions on handling potential sensor malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the drone continuously records weather data during extended flights, then the quantity of weather data collected increases, but the computational resources and time required to process and identify corrupt data increase significantly
Solution Approach 1:
The processor performs preliminary analysis of weather data immediately as it is being recorded by the sensor, checking for corrupt values against expected ranges before the data is stored in memory. This preliminary action prevents corrupt data from being saved in the first place, eliminating the need for later processing and identification of corrupt data during ground-based analysis.
2Quantity of substance
If the drone stores all recorded weather data in memory, then the complete dataset is preserved for analysis, but unnecessary corrupt data consumes storage space and processing resources
Solution Approach 1:
The system performs preliminary validation of weather data parameters against expected physical ranges and thresholds before storing the data in memory. The processor checks each data point as it is recorded and prevents storage of values that fall outside acceptable ranges, thereby conserving memory space and avoiding the energy expenditure required to later identify and handle corrupt data.
3Loss of energy
If the drone uses real-time data analysis to prevent corrupt data storage, then computational resources are conserved, but the device complexity increases
Solution Approach 1:
The processor monitors weather data parameters and compares them against predefined threshold values that represent acceptable physical ranges for each weather parameter. When a parameter exceeds or falls below its threshold, the system identifies it as corrupt and prevents storage. This approach uses simple threshold comparisons rather than complex algorithms, maintaining low device complexity while effectively conserving computational resources.
Data Source
Figure 1
AI summary
A weather drone (100) comprising: a first sensor (101) configured to repeatedly measure one or more parameters indicative of weather; a memory (103) coupled to the first sensor (101) and configured to store data recorded by the first sensor (101), the data comprising a series of repeatedly measured parameters; and a processor (104) coupled to the first sensor (104) and the memory (103). The processor (104) is configured to analyse the data as it is being recorded by the first sensor (101), and determine if the data exceeds a first threshold value and/or falls below a second threshold value. If the processor (104) determines that the data exceeds the first threshold value and/or falls below the second threshold value on at least one occasion, the processor (104) is configured to prevent the storage of further data from the first sensor (101) in the memory (102).