作战方案智能推荐方法综述

doi:10.12305/j.issn.1001-506X.2026.03.15

Abstract

Abstract:

To better promote the research of the operational scheme intelligent recommendation techniques, the main literature on this topic in recent years is reviewed, and a systematic review is conducted. The current mainstream intelligent recommendation methods from the two aspects of the traditional and artificial intelligence methods are analyzed. In terms of the traditional method, the main qualitative and quantitative methods involved are introduced. In terms of the artificial intelligence method, search-based, reasoning-based and learning-based intelligence recommendation methods are reviewed. In addition, the characteristics of the four methods are elaborated, the advantages, disadvantages and applicable scenarios of each method are summarized. Finally, the development trends of intelligent recommendation methods for operational schemes are analyzed and summarized.

Key words: operational scheme, intelligent recommendation, artificial intelligence method

CLC Number:

E 919
TJ 01

Pengbo JIAO, Zhihao LUO, Changjun FAN, Jianmai SHI. Review of intelligent recommendation methods for operational scheme[J]. Systems Engineering and Electronics, 2026, 48(3): 883-893.

Figures/Tables 10

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方法	优点	缺点	适用场景
传统方法	原理与结构简单，便于理解和使用；理论性与客观性较强，推导过程明确可见	过于依赖假设条件和专家判断因素，难以揭示作战方案内部关联关系；数学公式的建立与推导需通过人工完成，复杂的关系会导致公式难以建立	评估指标体系完备，对人为判断因素不敏感，具有确定备选方案；对象间的关系较为明确，易于使用公式描述，静态环境下的问题
基于搜索的方法	具有较强的通用性且速度快，易于理解和实现，可对带有约束限制的潜在方案进行搜索优化	难以将不确定性因素与动态场景考虑在内，效率与问题复杂程度相关，求解结果质量不稳定，只能逼近到全局最优解	考虑作战约束与目标，具有潜在可搜索作战方案，静态环境下的确定性问题
基于推理的方法	灵活性强且执行效率高，能够处理不确定性因素的影响	规则制定需要大量人力与领域知识，且规则合理性影响到结果质量，样本数据不足难以发挥其优势	考虑多属性、多目标的方案特征，规则较为完备且具有足够的样本数量
基于学习的方法	具有灵活的结构以及良好的非线性表达能力，改善了人为假设和主观因素对结果的影响，推荐过程可自动化完成	模型构建与训练过程需要大量时间，对训练样本数据的完备性和充足性要求较高，算法的性能高度依赖算力	作战规则明确、样本数据充足且完备，对时效性要求高的动态问题

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Review of intelligent recommendation methods for operational scheme

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