Dynamic Scheduling Scheme for Satellite Data Latency
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Solution Overview
Problem
In dynamic scheduling environments, especially for data collection and processing in weather satellites, ensuring data latency and availability is challenging due to resource unavailability, leading to potential data loss and labor-intensive scheduling efforts, particularly during off-nominal situations.
Innovation Solution
A scheduling scheme that predicts data retention and processing conflicts by determining the status and location of data on a recorder, using the schedule as a predictive measure to identify potential issues with data latency and availability, and providing an automated tool for scheduling adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual scheduling efforts are used to manage data collection and processing, then flexibility in handling off-nominal situations can be achieved, but labor intensity increases and real-time responsiveness decreases
Solution Approach 1:
The scheduling system performs self-service by automatically detecting data latency issues and generating corrective scheduling adjustments without human intervention. The system monitors its own schedule execution, identifies conflicts between data retention requirements and resource availability, and autonomously generates revised schedules to prevent data loss.
Solution Approach 2:
The system implements continuous feedback by tracking the actual status of data collection, processing, and downlink operations against the planned schedule. When deviations are detected (such as data approaching latency thresholds or resource unavailability), the system uses this feedback to dynamically adjust the schedule and prevent data loss.
2Ease of operation
If automated scheduling tools are implemented to reduce labor intensity, then operational ease improves, but system complexity increases
Solution Approach 1:
The system merges multiple functions into a unified scheduling framework that simultaneously handles data collection scheduling, processing task allocation, downlink timing coordination, and conflict detection. This integration reduces the need for separate manual coordination systems while managing complexity through a centralized approach.
Solution Approach 2:
The system performs preliminary actions by proactively identifying potential data loss risks before they occur. It calculates predicted data latency and availability issues in advance, generates preventive scheduling adjustments, and executes these adjustments before actual conflicts arise, thereby simplifying operational response.
3Reliability
If predictive scheduling is used to prevent data loss, then data reliability improves, but computational requirements increase
Solution Approach 1:
The system applies partial action by focusing computational resources on predicting and preventing only the critical data loss scenarios that would occur under the current schedule. Rather than exhaustively analyzing all possible scheduling outcomes, it identifies and addresses the specific latency and availability conflicts that threaten data retention, thereby reducing overall computational burden while maintaining reliability.
Data Source
AI summary
Generally discussed herein are devices, systems, and methods for determining if a master schedule will allow information to be recorded and downloaded from a node. A method can include receiving a plurality of schedules including a first schedule, a second schedule, and a list, determining, at the scheduler circuitry and based on the first schedule and the list, whether there is a threshold latency between mission data collection and downlink, determining, at the scheduler circuitry and based on the first schedule, the second schedule, and the list, whether any mission data will be overwritten in performing operations of the master schedule, and providing, by a display communicatively coupled to the scheduler circuitry, a first warning in response to determining that there is a threshold latency between mission data collection and downlink and a second warning in response to determining that mission data will be overwritten.