Research On Generation And Evaluation Approach For Equipment Operational Test Index Systems Based On Systems Analysis

The operational test index systems are an important basis in the evaluation process for the equipment to be tested.Activities such as the design of the operational test scenario,the specific subjects and content of the evaluation,and the appraisal and evaluation for the equipment to be tested all need to be carried out based on the index systems.Currently,many operational test index systems construction approaches are mostly towards a single dimension,they are highly subjective,not integrated with operational missions and mission scenarios closely,and the systems’ attributes of the confrontational operational test are ignored.In addition,there are still problems such as inconsistent equipment operational test system standards,incomplete data,lack of underlying execution logic,and unclear command systems.In terms of quantitative evaluation of indicators,there are problems such as difficult quantification of indicators under conditions that are affected by multiple factors.Therefore,this article researches the construction and evaluation approach for equipment operational test index systems based on systems analysis.The main research content and innovation points are summarized as follows:Firstly,a framework for the construction and evaluation of the equipment operational test index systems based on systems analysis is designed.Focusing on the core issue of the construction and evaluation of the equipment operational test index systems,the systems analysis method is introduced to realize the analysis of the operational test systems,and then the process for the construction of the operational test index is designed and realized.For the quantitative evaluation of the generated indexes,the design and implementation are based on a neural network.The three aspects of research progressed layer by layer,forming a complete logical chain from the analysis of the operational test systems,the construction of the index,to the quantitative evaluation of indicators.Secondly,the UAF-based equipment operational test architecture model design method is proposed.Because of the problems of inconsistent equipment operational test systems standards,incomplete data,lack of underlying execution logic,and unclear command systems,this article adopts the general steps of architecture development,based on the UAF architecture framework,and adopts a multi-view modeling description method.Carrying out the process of systems architecture data element analysis,metamodel design,systems modeling steps,and implementation method design for the equipment operational test systems.Specifically,it analyzed the composition of the equipment operational test systems from the two dimensions of test systems components and system architecture data elements,analyzed and determined five key data elements including parameters,nodes,capability,desired effects,and personnel,as well as six other important data elements.Using the data elements of the operational test architecture to design the meta-model of the operational test architecture,and analyze the three-element relationship and the four-element relationship respectively,and on this basis,selecting and determining the list of view products under the UAF framework;through construction,The hierarchical relationship of the equipment operational test system and the mapping relationship between it and the UAF modeling domain are analyzed and determined to develop the basic principles of the operational test systems architecture model and the development steps of the view model and its implementation methods.Then,the method of generating and verifying the operational test index system is proposed.According to the characteristics discovered in the analysis process of the operational test systems,combined with the problems that the index construction process is difficult to control,the definition of operational test meta-task is proposed.Based on this,the operational test mission-oriented meta-task decomposition is based on the mission-task decomposition method.Putting forward the process framework of the index systems construction process by the way of meta-task traction operational test indicators.Among them,the analysis and design select multiple view products under the UAF framework to support the mission task decomposition process,and the index construction process is proposed as operational.The construction of test indicators provides a set of standard process methods or paradigms;further,according to the characteristics of operational test indicators,a completeness verification method of the indicator systems is proposed from two dimensions: qualitative and quantitative;in the case study,drones are used for maritime reconnaissance.The index construction process of the operational test systems explains the specific steps of indicator construction and verifies the feasibility of the method.Finally,through the analysis of the characteristics of the bottom-level indicators of the operational test,the bottom-level indicators of the operational test are divided into five categories from the perspective of quantitative evaluation,and the quantitative characteristics of each analogy indicator are pointed out.Aiming at the quantification of the fifth category of indicators,this paper proposes a BP neural network-based quantitative evaluation method for equipment operational test index and uses neural network modeling to solve the quantitative problem of operational test index.Based on the analysis and design of the basic topology structure of the index quantification neural network,the training algorithm framework for the quantification of the operational test index is designed;in the case study,this paper selects the public data set that meets the needs and contains multiple factors and samples.The neural network is trained and fitted,and finally,the fitted neural network model is applied to the index quantification.Results show that only a small number of factors are needed to quantify the test target indicators,that is,factor reduction is achieved at the same time.It provides a feasible solution for operational test personnel to solve the problem of quantitative evaluation of operational test indicators under the influence of multiple factors.