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ObjectivesIn order to evaluate the operational effectiveness of surface warships more reasonably, this article proposes improving the existing evaluation methods to better suit complex battlefield environments. Methods First, according to operational test items and test scenarios planning, the content of the warship platform test items are analyzed, and classification standards for complex battlefield environments' operational intensity and electromagnetic environment are designed. An improved method for evaluating combat effectiveness based on the influence factors of complex battlefield environments is then proposed, and the operational effectiveness of a certain type of surface warship in an anti-air warfare environment is evaluated. ResultsThe results show that the proposed method is effective. Conclusions The proposed method fully considers the influence of complex battlefield environments on the combat capability of different operational units, and solves the problem of existing operational effectiveness evaluation methods failing to fit the actual situation of complex battlefield environments. As such, this study can provide valuable references for the operational testing of naval vessels.

In the production process of aerospace structural parts, there coexist batch production tasks and research and development (R&D) tasks. Personalized small-batch R&D and production tasks lead to frequent emergency insertion orders. In order to ensure that the task is completed on schedule and to solve the flexible job shop dynamic scheduling problem, this paper takes minimization of equipment average load and total completion time as optimization goals, and proposes a dual-loop deep Q network (DL-DQN) method driven by a perception-cognition dual system. Based on the knowledge graph, the perception system realizes the representation of workshop knowledge and the generation of multi-dimensional information matrix. The cognitive system abstracts the scheduling process into two stages: resource allocation agent and process sequencing agent, corresponding to two optimization goals respectively. The workshop status matrix is designed to describe the problems and constraints. In scheduling decision, action instructions are introduced step by step. Finally, the reward function is designed to realize the evaluation of resource allocation decision and process sequence decision. Application of the proposed method in the aerospace shell processing of an aerospace institute and comparative analysis of different algorithms verify the superiority of the proposed method.