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.

In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed. Firstly, considering the different damage grades of ammunition hitting targets, the damage results are approximated as multinomial distribution, and a Bayesian inference model of ammunition demand based on multinomial distribution is established, which provides a theoretical basis for forecasting the ammunition demand of multigrade damage under the condition of small samples. Secondly, the conjugate Dirichlet distribution of multinomial distribution is selected as a prior distribution, and Dempster–Shafer evidence theory (D-S theory) is introduced to fuse multisource previous information. Bayesian inference is made through the Markov chain Monte Carlo method based on Gibbs sampling, and ammunition demand at different damage grades is obtained by referring to cumulative damage probability. The study result shows that the Bayesian inference method based on multinomial distribution is highly maneuverable and can be used to predict ammunition demand of different damage grades under the condition of small samples.

Effective customer classification is a key issue for logistics enterprises to carry out customer relationship management, which has been concerned and studied by relevant institutions. Based on BP and AdaBoost algorithm, a new customer classification method for logistics enterprises is proposed. First of all, the logistics customer value evaluation index system is designed, and the different customer characteristics are analysed. Secondly, in order to solve the problem of low accuracy of the traditional BP network, the AdaBoost algorithm is used to linearly combine the traditional BP network to produce a strong classifier. The simulation results show that the application of the new customer value classification method based on BP-AdaBoost in customer classification improves the classification accuracy.

To address the problems of high cost of new ammunition experiment, few data of field test and low accuracy of consumption prediction, this article proposes a Bayesian estimation method of ammunition consumption based on normal‐inverse gamma distribution, and estimates the hyperparameters in the prior distribution through the prior information from the consumption of ammunition under different damage degrees of point targets, based on the normal distribution phenomenon of ammunition consumption at each damage degree. It is to establish a Bayesian estimation model for ammunition consumption under different damage degrees according to field test data based on Bayesian formula and solve for its posterior distribution. The example proves that the estimation results of ammunition consumption for point target with different damage degrees based on this method is more scientific and reasonable according to various prior information.