In safety-critical autonomous systems, data freshness presents a fundamental design challenge. While the Logical Execution Time (LET) paradigm ensures compositional determinism, it often does so at the cost of injected latency, possibly degrading the age of data on high-frequency control loops. Furthermore, heterogeneous, multi-rate, task dependencies is typically guaranteed inefficiently through oversampling. This paper proposes a Task-based scheduling framework extended with data freshness constraints. Unlike traditional models, scheduling decisions are driven by the lifespan of data. We introduce a formal methodology to decompose Data Dependency Graphs into dominant paths by tracing the strictest data freshness constraints backward from the actuators. Based on this decomposition, we propose an offset search algorithm that synchronizes multi-rate, multi-dependencies, task chains. This approach enforces end-to-end data freshness without the artificial latency of LET buffering, a trade-off between data freshness and execution determinism. We formally prove that this offset-based alignment preserves the 100% schedulability capacity of Global EDF while addressing data freshness guarantees.

翻译：在安全关键型自主系统中，数据新鲜度构成了一项基本的设计挑战。逻辑执行时间（LET）范式虽然能确保组合确定性，但通常以注入延迟为代价，可能降低高频控制回路中数据的时效性。此外，异构多速率任务依赖关系通常通过过采样得到低效保证。本文提出一种扩展了数据新鲜度约束的基于任务的调度框架。不同于传统模型，调度决策由数据的生命周期驱动。我们引入形式化方法，通过从执行器反向追溯最严格的数据新鲜度约束，将数据依赖图分解为主导路径。基于该分解，我们提出一种偏移搜索算法，用于同步多速率、多依赖关系的任务链。该方法在强制端到端数据新鲜度的同时，避免了LET缓冲带来的人工延迟——这是在数据新鲜度与执行确定性之间的权衡。我们形式化证明，这种基于偏移的对齐策略在保证数据新鲜度的同时，能保留全局EDF的100%可调度性容量。