Task Scheduling Method for Unmanned Vehicle Systems
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Solution Overview
Problem
Unmanned vehicle systems face unbalanced resource scheduling due to varying data transmission frequencies among nodes in their complex directed acyclic graph topology, leading to inefficient task execution.
Innovation Solution
A task scheduling method that traverses tasks in a queue, selects tasks with the minimum scheduled frequency for execution, and updates the frequency parameter after each task is executed, ensuring balanced resource allocation and improved stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are scheduled by traversing the queue in conventional order, then tasks with high data transmission frequency can be executed continuously, but this results in unbalanced resource scheduling
Solution Approach 1:
The patent introduces a frequency parameter to track and control the scheduled frequency of each task. By monitoring and adjusting the execution frequency of tasks based on this parameter, the system prevents high-frequency tasks from being scheduled continuously, thereby achieving balanced resource scheduling while maintaining execution efficiency.
2Reliability
If tasks with high data transmission frequency are scheduled uninterruptedly, then their execution completeness is improved, but resource scheduling becomes unbalanced
Solution Approach 1:
The patent implements periodic scheduling control by using the frequency parameter to regulate how often each task is selected for execution. Instead of allowing uninterrupted execution of high-frequency tasks, the system enforces periodic intervals between executions, ensuring both task completeness and balanced resource utilization across all tasks.
3Reliability
If the scheduling algorithm considers the complex DAG topology structure, then task dependencies are properly managed, but the scheduling complexity increases
Solution Approach 1:
The patent applies local quality by treating each task independently with its own frequency parameter, rather than applying a complex global scheduling algorithm across the entire DAG. This localized approach simplifies the scheduling decision-making process while still respecting task dependencies through the queue structure and frequency-based selection.
Data Source
AI summary
Embodiments of the preset disclosure provide a task scheduling method, an apparatus, an electronic device, and a storage medium. The method comprises traversing tasks in a current queue, and obtaining current values of current state information and a frequency parameter for each task that has been traversed, wherein the frequency parameter is a preset parameter for indicating a scheduled frequency of a corresponding task; selecting a task with the minimum scheduled frequency in the tasks having the current state information indicating an executable state as a target task according to the current values of the current state information and the frequency parameter for each task; and executing the selected target task, and updating the current value of the frequency parameter corresponding to the target task after the target task is executed.