Function-based equipment decision behavior simulation modeling method and system

Abstract

The invention discloses a function-based equipment decision behavior simulation modeling method and system. The method comprises the steps that S1, a function decision tree is constructed, the function decision tree comprises meta-model root nodes, the meta-model root nodes comprise tree nodes and edges, the tree nodes comprise situation analysis nodes, decision nodes and action nodes, and the edges comprise data edges used for data transmission and decision edges used for decision control; the situation analysis node, the decision node and the action node are associated through a data edge and a decision edge; s2, executing simulation, judging node types, executing a corresponding decision and returning to a next decision node if the node is a decision node, iterating if the next decision node is not empty, and directly executing a corresponding action if the next decision node is an action node; and if the nodes are the situation analysis nodes, topological sorting is carried out on all the nodes, and then each situation analysis node in sorting is circularly executed to obtain a final output value. According to the method, the problem of state space combination explosion caused by excessive input variables can be effectively relieved.

Description

technical field [0001] The invention relates to the technical field of decision-making, and more particularly, to a function-based simulation modeling method and system for equipment decision-making behavior. Background technique [0002] Combat effectiveness assessment is an important part of supporting the whole life cycle of fighter equipment demonstration, development and use. With the maturity of simulation technology and the improvement of effectiveness assessment requirements, combat simulation has become the main technology for combat effectiveness assessment of fighter jets. The fighter combat effectiveness simulation evaluation is inseparable from the support of an efficient and credible fighter combat simulation system, and the pilot's decision modeling is a key part of the construction of the fighter combat simulation system. The existing combat simulation systems for combat effectiveness evaluation of fighter jets generally lack effective air combat decision mod...

AI Technical Summary

Problems solved by technology

Practice shows that the air combat decision-making behavior modeling based on the state diagram has certain limitations when describing the air combat decision-making behavior, and cannot truly reflect the actual air combat combat process. The main manifestations are: First, the state transition relationship is complex and difficult to maintain

The future air combat is a joint and integrated cross-domain operation, which contains many weapons and equipment and complex tactics. Using a state diagram to describe it will inevitably lead to a large number of states and complex transfer relationships between these states, which is not easy to maintain. The scalability is not good, adding a state will often cause changes in the entire state diagram, resulting in a lot of debugging work

Second, the ability to express is limited and cannot describe the uncertainty of expert knowledge

The third is the non-modular modeling method, which is difficult to model

However, the genetic fuzzy tree still has the following problems: First, it does not clearly distinguish between decision-making reasoning and behavioral actions, resulting in model confusion

If the distinction is not made during the modeling process, it will make it difficult to understand and maintain the model

Second, the output of the fuzzy reasoning system is continuous and the expressive ability is limited

When the fuzzy inference system is used for description, the discretized meaning of continuous output values ​​is poorly interpretable

The third is that the model is suitable for specific decision-making problems, and the compatibility and inheritance are poor

If the combat scenario changes and the equipment is innovated, the existing decision-making model cannot be inherited, the difficulty of modeling again will increase, the scalability of the model is not good, it is difficult to be compatible with other methods, and the compatibility is poor

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