Weather Data Dissemination via Event-Triggered Downlinks
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Solution Overview
Problem
Current weather data dissemination systems face inefficiencies, including the lack of real-time critical weather event detection and sharing, reliance on request-response protocols, and significant processing latency, which can lead to delayed and incomplete data delivery to aircraft, affecting safety and accuracy.
Innovation Solution
A method that automatically identifies and triggers critical weather alerts to consumer aircraft by continuously downlinking weather data from producer aircraft to a ground data center, using geo-spatial grids for data processing and fusion, and configuring weather thresholds based on consumer needs, allowing for dynamic reconfiguration and auto-uplinking of critical weather events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If continuous downlink streaming is used to ensure real-time weather data delivery, then data timeliness is improved, but data-link costs and bandwidth usage increase significantly
Solution Approach 1:
The system uses periodic event-triggered downlinks where producer aircraft transmit weather data only when critical weather events are detected or at scheduled intervals, rather than continuous streaming. This reduces bandwidth usage and data-link costs while maintaining timely delivery of critical weather information to consumers.
Solution Approach 2:
The system extracts and transmits only the critical weather event data that meets predefined thresholds, rather than transmitting all weather data continuously. This selective extraction reduces the volume of data transmitted, lowering bandwidth consumption and costs while ensuring timely delivery of the most important weather information.
2Loss of energy
If request-response protocol is used to reduce bandwidth usage, then data-link costs are reduced, but critical weather events may be missed due to processing latency
Solution Approach 1:
Producer aircraft continuously monitor weather conditions and pre-identify critical weather events based on predefined thresholds before ground requests are made. This preliminary detection ensures that critical events are ready for immediate transmission, reducing the risk of missing events due to request-response latency while maintaining cost-effective bandwidth usage.
Solution Approach 2:
The system implements a hybrid protocol where producer aircraft automatically trigger downlinks when critical weather events are detected, and ground centers can also initiate requests. This feedback mechanism ensures reliable detection of critical events while optimizing bandwidth usage by avoiding unnecessary transmissions during normal conditions.
3Measurement precision
If ground data center processes all weather data requests, then data accuracy is improved through fusion and prediction algorithms, but processing time and latency increase
Solution Approach 1:
The system segments processing tasks between producer aircraft and ground data center. Producer aircraft perform preliminary weather event detection and filtering based on predefined thresholds, while the ground data center focuses on fusing data from multiple sources and running prediction algorithms. This segmentation reduces ground processing latency while maintaining data accuracy through collaborative processing.
4Loss of energy
If request-response protocol is used, then bandwidth usage is reduced, but only requested parameters are transmitted leaving critical data unshared
Solution Approach 1:
Producer aircraft autonomously determine which weather data to transmit by continuously monitoring for critical events and automatically triggering downlinks when thresholds are exceeded, without waiting for ground requests. This self-service mechanism ensures that all critical weather events are shared regardless of whether ground centers have specific requests, while still maintaining cost-effective bandwidth usage by avoiding unnecessary transmissions.
Data Source
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AI summary
A method of gathering and distributing critical weather event information is provided. The method includes monitoring for critical weather events based on defined critical weather event thresholds with an onboard producer weather detection unit of at least one producer vehicle. The critical weather event thresholds are dynamically reconfigured based at least in part on consumption needs of the consumer vehicle. The detected critical weather event and associated time stamp and geolocation information are communicated to a base station. The detected critical weather event is fused with other weather data at the base station to produce a unified weather event image that includes the detected critical weather event. Prediction algorithms are applied to the unified weather event image and are parsed into predefined grid cells of a geo-spatial grid. The predicted weather data is up-linked to a consumer vehicle anticipated to travel within at least one grid cell of the geo-spatial grid.