HUANG Tong, GAO Qinghe, LIU Zhihao, et al. Evaluation of invulnerability for launch platform via DA-PSR model and complex network[J]. 2023, 55(12): 35-41.
DOI:
HUANG Tong, GAO Qinghe, LIU Zhihao, et al. Evaluation of invulnerability for launch platform via DA-PSR model and complex network[J]. 2023, 55(12): 35-41.DOI: 10.11918/202209109.
Evaluation of invulnerability for launch platform via DA-PSR model and complex network
In order to improve the problems of strong subjectivity and weak logic correlation in current evaluation research
taking a certain type of launch platform as the object
this research constructed an evaluation system of invulnerability from three aspects: evaluation index system
evaluation method and evaluation grade criterion. PSR framework theory is adopted to construct an evaluation index system with causal logical correlation. In view of the large number of indicators
a screening method of evaluation index system was proposed based on the theoretical node number of complex heterogeneous network and k-means algorithm. Based on the mutual information theory and limit damage quantification
the index weighting method and index quantification method are proposed. In addition
the PSR framework theory is improved through the dimensional analysis
and the invulnerability evaluation method of DA-PSR is constructed. Furthermore the evaluation grade criterion method is proposed based on operational mission requirement. The results show that by selecting a simplified evaluation index system with the median value of the remaining nodes accounting for 52.990% of the total median value and the center value of the remaining cluster accounting for 90.550% of the total cluster center value
the invulnerability value of the launch platform can be evaluated to be 0.737
and the evaluation grades of the launch platform under different combat missions show the difference of "excellent
medium and medium" respectively. The evaluation method of invulnerability DA-PSR has low subjectivity and strong logical relevance
which is helpful to improve the credibility of evaluation results.