针对传统弹药毁伤效能评估方法在现代战场中的不足,提出了一种基于人工智能（AI）技术的智能评估体系,以解决评估效率低、精度不足和实时性差等问题。首先分析了靶场试验、理论模型和经验公式等传统方法的局限性,指出了它们难以适应现代战争中复杂目标、多样场景和动态环境的需求。针对这些挑战,引入机器学习、深度学习和强化学习等AI核心技术,构建了一个数据驱动的评估体系,通过智能建模和自动化优化,提升弹药毁伤效能的预测精度与实时性。还详细阐述了智能评估平台的系统架构,涵盖数据采集、多源数据融合、AI建模与效果反馈等模块,强调了模型的泛化性和实战适应性。同时,还展望了多模态数据融合、小样本学习、虚拟与现实结合以及可信AI等未来研究方向。研究表明,基于AI的弹药毁伤效能评估为现代武器系统的精准评估提供了创新的理论与技术支持,推动了评估方法向智能化、高效化发展,具有重要的军事应用前景。
Abstract
To address the limitations of traditional ammunition damage effectiveness evaluation methods on modern battlefields, the work aims to propose an intelligent evaluation system based on artificial intelligence (AI) technology to solve issues such as low efficiency, insufficient accuracy, and poor real-time performance. Firstly, the limitations of traditional methods, including field tests, theoretical models, and empirical formulas were analyzed and it was pointed out that these methods were inadequate for meeting the demands of modern warfare, such as complex targets, diverse scenarios, and dynamic environments. To tackle these challenges, core AI technologies, including machine learning, deep learning, and reinforcement learning, were introduced to build a data-driven assessment system aimed at improving prediction accuracy and real-time performance through intelligent modeling and automation. The system architecture of the intelligent evaluation platform was also elaborated, covering modules for data acquisition, multi-source data fusion, AI modeling, and feedback of results, emphasizing the generalizability and adaptability of the model to real-world combat scenarios. Furthermore, future research directions were discussed, including multi-modal data fusion, small-sample learning, virtual-real integration, and trustworthy AI. The study demonstrates that AI-based ammunition damage effectiveness evaluation provides innovative theoretical and technical support for the precise evaluation of modern weapon systems, driving the development of evaluation methods toward intelligence and efficiency, with significant military application prospects.
关键词
弹药 /
毁伤效能 /
人工智能 /
机器学习 /
评估方法 /
数据融合
Key words
ammunition /
damage effectiveness /
artificial intelligence /
machine learning /
evaluation method /
data fusion
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