兵棋推演态势认知的态势数学表达方法

doi:10.12305/j.issn.1001-506X.2025.03.20

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (3): 883-892.doi: 10.12305/j.issn.1001-506X.2025.03.20

• 系统工程 • 上一篇

兵棋推演态势认知的态势数学表达方法

潘晨辉¹^,*, 鲜勇¹, 马培洋¹, 倪晚成², 赵晓楠², 李少朋¹^,³

1. 火箭军工程大学导弹工程学院, 陕西西安 710025
2. 中国科学院自动化研究所, 北京 100190
3. 清华大学自动化系, 北京 100084

收稿日期:2024-02-18 出版日期:2025-03-28 发布日期:2025-04-18
通讯作者: 潘晨辉
作者简介:潘晨辉 (1994—), 女, 博士研究生, 主要研究方向为智能任务规划与作战仿真
鲜勇 (1972—), 男, 教授, 博士, 主要研究方向为智能任务规划与作战仿真、飞行力学与飞行器任务规划
马培洋 (1991—), 男, 博士研究生, 主要研究方向为飞行器总体与作战运用
倪晚成 (1978—), 女, 研究员, 博士, 主要研究方向为数据挖掘与知识发现、群体智能博弈决策平台与评估
赵晓楠 (2002—), 女, 硕士研究生, 主要研究方向为数据挖掘与知识发现、群体智能博弈决策平台与评估
李少朋 (1991—), 男, 副教授, 博士, 主要研究方向为智能任务规划与作战仿真
基金资助:
国家自然科学基金(62103432)

Situation mathematical expression method of situational awareness in wargame

Chenhui PAN¹^,*, Yong XIAN¹, Peiyang MA¹, Wancheng NI², Xiaonan ZHAO², Shaopeng LI¹^,³

1. Sohool of Missile Engineerings Rocket Force University of Engineering, Xi'an 710025, China
2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
3. Department of Automation, Tsinghua University, Beijing 100084, China

Received:2024-02-18 Online:2025-03-28 Published:2025-04-18
Contact: Chenhui PAN

摘要/Abstract

摘要：

为满足态势认知智能化需求, 提出一种面向兵棋推演战场态势认知的机器学习数据集规范化构建方法。以陆军战术兵棋推演为研究对象, 基于特征工程分析其战场态势关键要素, 提出一种分层栅格化的战场态势特征表达模型。采用等间隔时间关联模式对兵棋推演过程数据进行滑动切片、映射变换, 解决兵棋数据结构不统一、特征标签不均衡、数据变换保真困难等问题, 实现样本的自动收集与存储。构建并公开包含26万个样本的数据集兵棋推演态势认知机器学习数据集, 实验表明采用所提方法构建的数据集主观色彩少, 数据保真效果好, 数据集采集过程自动且高效, 数据集一致性好。

关键词: 兵棋推演, 特征表达, 态势认知, 机器学习, 数据集

Abstract:

In order to meet the demand for intelligent situation cognition, this paper proposes a standardized construction method of machine learning dataset for battlefield situation cognition in wargame. Based on feature engineering analysis, this study focuses on army tactical wargame and analyzes key elements of battlefield situation, and proposes a hierarchical and grid based model for expressing the battlefield situation. By using the equal interval time correlation mode to slide slice and map transformation the data of the wargame process, the problems of inconsistent military chess data structure, imbalanced feature labels, and difficulty in data transformation fidelity are solved, and the automatic collection and storage of samples are achieved. A machine learning for situational awareness in military chess deduction dataset containing 260 000 samples is constructed and publicly available. The experiment shows that the dataset constructed using the mapping model in this paper has fewer subjective colors, good data fidelity, automatic and efficient data collection process, and good dataset consistency.

Key words: wargame, feature expression, situation awareness, machine learning, data set

中图分类号:

TP391.9

潘晨辉, 鲜勇, 马培洋, 倪晚成, 赵晓楠, 李少朋. 兵棋推演态势认知的态势数学表达方法[J]. 系统工程与电子技术, 2025, 47(3): 883-892.

Chenhui PAN, Yong XIAN, Peiyang MA, Wancheng NI, Xiaonan ZHAO, Shaopeng LI. Situation mathematical expression method of situational awareness in wargame[J]. Systems Engineering and Electronics, 2025, 47(3): 883-892.

图/表 16

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地形地貌	编码
开阔地	0
丛林地	1
城镇地	2
松软地	3
湖泊	4
河流	5
一级公路	6
二级公路	7

夺控点状态	编码
未被占领	0
红方占领	1
蓝方占领	2

动作	编号
静止	0
机动	1
静止射击	2
机动射击	3
上车	4
下车	5
夺控	6
切换状态	7
间瞄射击	8
引导射击	9

类别	关键特征要素	降维前	降维后
环境	地形地貌	√	√
	高程	√	√
	夺控点位置	√	√
	夺控点状态	√	√
算子	红\蓝算子位置	√	√
	红\蓝算子速度	√	√
	红\蓝算子血量	√	√
	红\蓝算子携带武器类型	√	×
	红\蓝算子携带武器数量	√	×
	红\蓝算子观察距离	√	×
	红\蓝算子当前高度	√	×
	红\蓝算子可被观察的基础距离	√	×
	红\蓝算子装甲类型	√	×
	红\蓝算子武器堆叠情况	√	×
	红\蓝算子武器冷却情况	√	√
	红\蓝算子动作情况	√	√

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兵棋推演态势认知的态势数学表达方法

Situation mathematical expression method of situational awareness in wargame

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