Unmanned swarm systems are now employed in a wide range of applications. Agriculture, commerce, and the military will all benefit from the advancement of unmanned swarm technology. To support multiple applications, the scenarios should have a standardized description and definition as the system input, allowing for more precise and specific processing and resource planning. In this paper, the Scenario description Language for Unmanned swarm (SL4U) is introduced in order to implement the unmanned swarm scenario. The scenario is divided into environment and task. We describe the environment, including the task space, target and home locations, and the initial settings of the unmanned swarm. In terms of tasks, we have identified four categories of tasks, four temporal relationship constraints, the event handler, as well as other elements.The dual engines of parsing and micro-embedded collaboration we developed will handle the SL4U. We employ a search and rescue scenario in both simulation and physics to test SL4U. Through the processing of the dual engines, the unmanned swarm's search and rescue operations in a predefined environment are accomplished successfully. As scenarios are developed, the benefits of SL4U will become more apparent, as it will drastically reduce personnel costs and experimentation time.

With the increasing scale and complexity of power systems, the Security and Stability Control System (SSCS) plays a vital role in ensuring the safe operation of the grid. However, existing SSCS implementations still face many limitations in cross-regional coordination, control precision, and risk prediction. Establishing the digital simulation model is an effective way to verify the control policy of SSCS. This paper proposes a neural heuristic task scheduling method based on deep reinforcement learning (DRL) to schedule the simulation tasks. It models the task dependencies of SSCS as a directed acyclic graph (DAG) and then dynamically optimizes task priorities and resource allocation through deep reinforcement learning. The method introduces multi-head attention and heterogeneous attention mechanisms to effectively capture complex dependencies among tasks, enabling efficient multi-core task scheduling. Simulation results show that the proposed algorithm significantly outperforms traditional scheduling methods in terms of makespan, load balancing, and resource utilization. It can also adapt to dynamic changes under different task scales and multi-core environments, demonstrating strong robustness and scalability.