Research On The Autonomous Decision System Of A Certain Type Of Shipborne Radar Equipment Support

Equipment support has been a hot topic in recent years.This article takes a certain type of shipborne radar as the object,and studies related technologies for the preventive management of spare parts in the comprehensive support of equipment,from the classification of spare parts to the prediction of spare parts demand to the reasonable allocation of spare parts resources and resources.For the selection of suppliers when ordering in shortage,a set of spare parts supply guarantee support system has been designed.The main work of this thesis are as follows:1.This article first selects shipborne radar transmitters as the object,uses the collected spare parts information to distinguish the degree of importance,adds the Analytic Hierarchy Process(AHP)on the basis of the original ABC classification method,and analyzes the spare parts related to the radar transmitter.Categorize and select the most important spare parts for subsequent spare parts demand forecasting and spare parts inventory control strategy research.2.Research on forecasting methods of spare parts demand.First,in the case of insufficient data,the gray model method can be used to predict.An improved method combined with exponential smoothing is proposed,and its effectiveness and accuracy are analyzed through examples.Then,the neural network algorithm is used to establish a support vector machine(SVM)prediction model optimized by the Artificial Fish Swarm Algorithm(AFSA),and the different types of data are normalized and placed into the established model Complete the corresponding training,and further forecast the demand for spare parts,and finally conduct a comparative analysis.The results show that the relative error between the predicted value and the actual value is small,and the algorithm has obvious accuracy and performance.Spare parts demand forecasting is for the follow-up and accurate formulation of the spare parts demand plan list,which affects the follow-up procurement and distribution work.Therefore,it is very important to accurately forecast the demand for radar spare parts.3.On the basis of the research on the classification and demand forecast of maintenance spare parts,the improved genetic algorithm is adopted,combined with the status quo of inventory management,the spare parts inventory optimization method,supplier evaluation and optimization plan research are carried out.First of all,within the scope of inventory capacity,with the goal of minimizing the related costs of spare parts supply,establish a centralized ordering inventory model.Then,considering that inter-regional allocation can save time in emergency situations,establish an adjustable inventory model.And through two cases to verify the comparison of the above two models,the results show that the benefits of the adjustable model is good.Finally,the AHP and GRAP methods are selected to establish the index weight calculation model,and a set of supplier evaluation and selection method processes based on the index system are given through actual cases.4.On the basis of theoretical research,using the AFSA-SVM spare parts prediction algorithm,design and development of equipment integrated support system software,processing the historical data of spare parts to generate the required spare parts supply list.This article uses the historical data of spare parts,from the classification forecast of spare parts,the demand of spare parts to the reasonable allocation of spare parts resources,the generation of spare parts supply list,and the selection of suppliers when ordering due to shortage of resources,to complete the design of a set of equipment support support system.