Owning to the fact that ammunition can cause varying degrees of damage to its target, this article presents a damage effectiveness calculation method of hitting targets with ammunition based on Bayesian multinomial distribution to solve the problems of complex processes, few trial times and difficult calculations of damage probability in target-hitting tests with high-tech ammunition, according to a calculation index of damage effectiveness about the occurrence probability of different damage. Based on the concept of symmetry, the idea of “divide damage level—determine distribution—integrate information—solve distribution” is adopted. Firstly, this paper describes the damage effectiveness test of ammunition attacking targets with multiple distributions; secondly, this paper integrates the damage effectiveness information of ammunition strike targets with Dempster–Shafer evidence theory (D–S evidence theory) and symmetry advantage; finally, this paper attempts to solve the symmetric posterior distribution of damage effectiveness parameters with Bayesian theory and the Markov chain Monte Carlo (MCMC) method. The result demonstrates that this method is very significant in improving the calculation accuracy of ammunition damage effectiveness, which could describe the damage effectiveness of ammunition in detail by integrating the prior information with multiple types of damage effectiveness.

The development of equipment research projects is replete with uncertainties, complicating the provision of accurate and objective evaluations of their development costs. To enhance the precision of cost predictions for such projects, it is critical to assess and integrate the cost‐risk level. This study introduces an improved two‐dimensional cloud model (TDCM) that effectively integrates qualitative concepts and quantitative data for analyzing equipment projects. First, the principal factors influencing cost are thoroughly evaluated, and an assessment index system is structured with three primary and twelve secondary indicators. The probability of budget overrun and its impact are designated as the primary variables for assessing each indicator, aligning with the definition of cost risk. Second, this research merges the fuzzy analytic hierarchy process (FAHP) with the criteria importance though intercriteria correlation (CRITIC) method, employing coalitional game theory to ascertain the weights of each assessment indicator. Third, a TDCM is developed to derive cloud eigenvalues, and a two‐dimensional cloud diagram is constructed through MATLAB to preliminarily ascertain the risk level, with the degree of nearness subsequently calculated to refine these results. Fourth, each indicator in the assessment system is treated as a node to construct a Bayesian network (BN) based on logical relationships, and a sensitivity analysis is conducted to identify sensitive indicators. Fifth, the development of a specific mine countermeasure (MCM) weapon system is examined as a case study, incorporating relevant existing data into the improved model. The validity and feasibility of the model are corroborated by comparing it with traditional methods. The results affirm that the enhanced TDCM effectively navigates the ambiguity and randomness inherent in cost‐risk assessment data, providing a reference for similar scientific research projects.

The test of new equipment is usually carried out in multiple batches according to the task schedule and test results. Constrained by the test environment, cost, and other factors, the amount of reliability test data in each batch is relatively limited, which brings difficulties to the accurate equipment reliability estimation work. For the reliability simulation tests conducted before each batch tests, it is particularly important to make full use of each batch tests information and simulation tests information to estimate the reliability of the equipment for small sample tests. This study takes the common normal-type life distribution equipment as the research object, and selects the normal-inverse gamma distribution as the equipment life parameters prior distribution based on the Bayesian method. Combined with the system contribution, the fusion weights of each batch tests information are determined and all the batch tests information is fused. Finally, the estimation of equipment reliability based on multiple batch tests is completed. The research results show that this method can integrate the information of each batch test and simulation test, overcome the problem of insufficient information of single batch tests, and provide an effective analytical tool for equipment reliability estimation.

In response to the limitations of traditional static reliability analysis methods in characterizing the reliability changes of the Integrated Power System, this paper proposes a time-varying reliability analysis framework based on a Dynamic Bayesian Network. By embedding a multi-physics coupled degradation model into the conditional probability nodes of the Dynamic Bayesian Network, a joint stochastic differential equation for the degradation process was constructed, and the dynamic correlation between continuous degradation and discrete fault events throughout the entire life cycle was achieved. A modified method for modeling continuous degradation systems was proposed, which effectively solves the numerical stability problem of modeling complex degradation systems. Finally, the applicability and correctness of the model were verified through numerical examples, and the results showed that the analysis framework can be effectively applied to time-varying reliability assessment and dynamic health management of complex equipment systems such as the Integrated Power System.

There are many uncertainties in the process of technology projects “Unveiling and Commanding” system which can easily lead to the risk of failing to achieve the expected effect of technology quality. In order to ensure that the implementation of the system can achieve the expected results, the indicator system for risk assessment of technology projects “Unveiling and Commanding” system is established on the basis of expert interviews and field research. The improved game theory multiple combination weighting method is used to weight the indicators to reduce error risk of the weight results. This paper makes a comprehensive risk assessment of technology projects “Unveiling and Commanding” system from the two dimensions of risk possibility and risk impact degree based on two-dimensional cloud model, so as to reduce the subjectivity, randomness and fuzziness of assessment results. The example verifies that the model is reasonable and effective, and has a certain guiding role in the improvement of technology projects “Unveiling and Commanding” system.