Multi-Source Data Aggregation for Satellite Target Identification
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Solution Overview
Problem
Current systems face challenges in efficiently analyzing and processing vast amounts of asynchronous data from sources like social media, weather reports, and satellite imagery to identify relevant geographic areas for targeted intelligence collection and satellite image acquisition, due to the high volume, variety, velocity, and veracity of this data, which requires manual correlation and processing that is time-consuming and inefficient.
Innovation Solution
A system and method that aggregates data from satellite imagery, weather conditions, and social media, detects persistent first-time changes, qualifies asynchronous data packets, correlates events, and predicts geographic progression to identify target areas for future satellite imagery acquisition, thereby providing analyst-ready information for directing intelligence assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual correlation and processing of asynchronous data from multiple sources is performed, then data analysis accuracy can be maintained, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical processing with automated computational systems. Machine learning algorithms and automated data processing systems analyze asynchronous data from multiple sources, substituting human analysts and manual correlation processes with algorithmic systems that can process vast quantities of data rapidly while maintaining analytical accuracy.
Solution Approach 2:
The system creates digital replicas and models of data processing workflows. By using automated systems to replicate and execute analysis tasks that were previously performed manually, the patent enables parallel processing of multiple data streams simultaneously, dramatically reducing processing time while preserving the analytical rigor of manual methods.
2Loss of information
If all asynchronous data from multiple sources is collected and analyzed, then comprehensive intelligence information can be obtained, but data volume and processing complexity become unmanageable
Solution Approach 1:
The patent extracts and isolates only the most relevant features and patterns from the vast asynchronous data streams. By applying filtering algorithms and relevance scoring mechanisms, the system extracts critical intelligence information while discarding redundant or low-value data, reducing processing complexity while maintaining comprehensive coverage of important information.
Solution Approach 2:
The system segments the massive asynchronous data stream into manageable components and categories. By dividing data from different sources (social media, weather reports, satellite imagery) into separate processing streams and applying specialized analysis methods to each segment, the patent reduces overall system complexity while maintaining the ability to synthesize comprehensive intelligence information.
3Speed
If real-time analysis of vast data volumes is performed, then timely intelligence can be provided, but computational resources and processing power requirements increase
Solution Approach 1:
The patent implements periodic batch processing and incremental analysis of asynchronous data streams. Instead of continuously processing all data in real-time, the system analyzes data at optimized intervals and updates intelligence products incrementally, reducing peak computational resource requirements while maintaining timely delivery of intelligence information.
Solution Approach 2:
The system performs preliminary filtering, aggregation, and preprocessing of asynchronous data before main analysis. By preparing and organizing data in advance using automated ingestion pipelines and preliminary processing steps, the patent reduces the computational burden during critical analysis phases, enabling faster processing with fewer resources.
Data Source
AI summary
Disclosed is a system and method for collecting, processing and aggregating satellite imagery with large volumes of other digitized data for analysis by a human user in order to identify geographic areas for further data collection and/or analysis. More particularly, the invention relates to identifying geographic subjects for satellite image acquisition by aggregating and analyzing first-time changes detected by satellite imagery as well as weather report data, social media streams and newswire feeds. An analytics engine uses rules to qualify, flag and correlate asynchronous data from a plurality of sources with changes on the earth's surface, and catalogs and stores the qualified and correlated data where it may be queried and used to prepare reports or recommendations for future satellite image acquisition targets.