In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly an...In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.展开更多
基金supported by the National Natural Science Foundation of China(U14352186140340161273189)
文摘In the field of weapon system of systems (WSOS) simulation, various indicators are widely used to describe the capability of WSOS, but it is always difficult to describe the comprehensive capability of WSOS quickly and intuitively by visualization of multi-dimensional indicators. A method of machine learning and visualization is proposed, which can display and analyze the capabilities of different WSOS in a two-dimensional plane. The analysis and comparison of the comprehensive capability of different components of WSOS is realized by the method, which consists of six parts: multiple simulations, key indicators mining, three spatial distance calculation, fusion project calculation, calculation of individual capability density, and calculation of multiple capability ranges overlay. Binding a simulation experiment, the collaborative analysis of six indicators and 100 possible kinds of red WSOS are achieved. The experimental results show that this method can effectively improve the quality and speed of capabilities analysis, reveal a large number of potential information, and provide a visual support for the qualitative and quantitative analysis model.
