_{Execution time, reliability, and energy consumption are the three main performance parameters of processor scheduling for real-time embedded systems. It is very meaningful to optimize the energy optimization of processor scheduling to satisfy the requirements on time limit and reliability. This paper tries to optimize the energy consumption of the processor under three constraints: the partial ordering relations between task modules, the time limit, and the reliability. Based on directed acrylic graph (DAG) and quantum particle swarm optimization (QPSO), two scheduling algorithms were developed for the problem, namely, DAG_QPSO_I and DAG_QPSO_II. The two algorithms were compared with each other, and with other intelligent algorithms. The results show that the proposed algorithms are superior in optimization effect and efficiency, DAG_QPSO_I makes energy consumption more efficient than DAG_QPSO_II, and DAG_QPSO_II meets stricter requirements on time limit and reliability than DAG_QPSO_I.}

_{执行时间、可靠性和能耗是实时嵌入式系统处理器调度的三个主要性能参数。优化处理器调度的能量优化以满足时限和可靠性的要求是非常有意义的。本文试图在三个约束条件下优化处理器的能耗：任务模块之间的偏序关系、时间限制和可靠性。基于有向丙烯酸图（DAG）和量子粒子群优化（QPSO），针对该问题开发了两种调度算法，即DAG_QPSO_I和DAG_QPSO_II。将这两种算法相互比较，并与其他智能算法进行比较。结果表明，所提出的算法在优化效果和效率上均优越，}