Dynamic modeling of military variables based on structured reconfiguration: an optimal analysis of the relationship between strength, resources and morale in a complex battlefield environment （Findings）

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In light of the complex battlefield environment where military decisions are required, this study seeks, through optimising military variable models using structured reconstruction methods, to enhance the prediction accuracy and dynamic adaptability of the key variables modulating strength, resources, and morale, hence arriving at an optimisation of resource allocation. In this respect, the study applies multilevel decomposition analysis, causal chain analysis, and genetic algorithms to simulate variable dynamics in various tactical scenarios and undertakes real-time monitoring and adjustment in strength, resources, and morale by iteratively optimizing models. In this connection, the experimental results confirm that, after reconstruction, the prediction accuracy will increase from 70% to 85%, variable inter-correlation from 0.5 to 0.8, resource allocation efficiency from 60% to 90%, dynamically reflects the variations in key variables of battlefield environment, and assigns a scientific basis for tactic adjustment and resource optimization in the Israel-Iran conflicts. It also infers that the structure reconstruction method can improve the applicability of military models and predictive ability by optimizing the relationship among variables in the model, reinforcing feedback mechanisms of the model, and providing scientific tools for decision making under complex battlefield situations and resource allocation. It further discussed the potentiality of a genetic algorithm in combination with deep learning techniques to further enhance the applicability of the model.