Aircraft Mission Risk Mapping with Heterogeneous Geospatial Layers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for assessing operational risk in aircraft operations, such as UAVs, are dependent on predefined data sources and lack dynamic, four-dimensional risk evaluations, limiting their effectiveness in managing risks associated with aircraft missions.
Innovation Solution
A method and system that acquire multiple heterogeneous geospatial data sets, derive operational risk metrics using a risk model incorporating aircraft and regulatory information, and store these as risk layers in a database for dynamic, four-dimensional risk assessments, enabling informed decision-making and risk-minimized aircraft operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined data sources are used for risk assessment, then the system is simpler to implement, but the risk assessment lacks dynamic and four-dimensional evaluation capabilities
Solution Approach 1:
The system integrates multiple heterogeneous data sources (geospatial data, aircraft data, regulatory information, weather data, traffic data) into a unified risk assessment framework that serves multiple evaluation purposes simultaneously, enabling dynamic four-dimensional risk assessment across different operational contexts
Solution Approach 2:
The risk assessment system is divided into distinct modular components: data acquisition modules for different data types, risk model modules that process specific data categories, and integration layers that combine results. This segmentation allows each module to be developed and maintained independently while achieving comprehensive dynamic assessment
2Reliability
If multiple heterogeneous geospatial data sets are acquired and processed, then the risk assessment becomes more comprehensive and dynamic, but the data processing complexity increases
Solution Approach 1:
The system introduces intermediary processing layers including standardized data interfaces, normalization modules, and integration algorithms that mediate between heterogeneous data sources and the core risk assessment engine. These intermediaries translate diverse data formats into a unified structure, reducing processing complexity while maintaining comprehensive risk evaluation
Solution Approach 2:
The system dynamically adjusts processing parameters such as data sampling rates, resolution levels, and integration weights based on operational context, aircraft type, and mission characteristics. This allows the system to optimize processing complexity according to specific assessment needs while maintaining high reliability
3Reliability
If real-time risk assessment is performed during aircraft operation, then the operational safety is improved, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary risk assessments during flight planning and pre-flight preparation, establishing baseline risk profiles and identifying potential hazard zones before actual operations begin. This advance preparation reduces the computational burden during real-time operation while maintaining continuous safety monitoring
Solution Approach 2:
The system implements periodic risk assessment cycles that alternate between comprehensive evaluations and rapid updates. During steady-state operations, lighter monitoring is performed, while intensive assessments are triggered by specific events or conditions, optimizing the balance between safety and processing time
Data Source
Figure 1
Figure 2
Figure 3
AI summary
We propose a method of operating an aircraft (AC), in particular UAV, for assessing operational risk at a given position in space, comprising: dissimilarly acquiring (S1.1,...,S1.n) multiple heterogeneous geospatial data sets (DL1,...,DLn); deriving metrics for operational risk from each of said data sets(DL1,...,DLn), thus obtaining multiple corresponding geospatial risk layers (RL1,... RLn), by means of a respective risk model; storing said risk layers (RL1,...RLn) in a risk layer database (DB); accessing the risk layer database (DB) during aircraft operation planning and/or during actual aircraft operation to obtain a mission risk map (RMA); operating the aircraft (AC) based on risk information comprised in said mission risk map (RMA), preferably comprising minimizing a mission risk. We also propose a system for carrying out said method.