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

Abstract

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

CLC Number:

TP391.9

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.

Figures/Tables 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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Situation mathematical expression method of situational awareness in wargame

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