As a precision strike weapon, the strategic position of missile equipment is increasing. Therefore, the reliability of the missile equipment has put forward higher requirements. However, the traditional periodic maintenance methods are increasingly unable to meet the maintenance and security needs of missile equipment, which has resulted in missile weapon systems facing low operational readiness and high security costs. In order to maintain the missile equipment in a good performance state for a long time, and reduce the investment in the maintenance cost of missile equipment during storage, a reinforcement learning-based method to optimize the maintenance decision of missile equipment is proposed. First, the future health state of the missile is predicted by the degradation pattern of missile components. Second, a Markov maintenance decision model of the missile is developed based on the degradation mechanism of missile components. Finally, with the maintenance cost of missile components during storage as the optimization objective, the Q-learning method is used to optimize the maintenance decision of missile components during storage. Through experimental simulations, the optimal maintenance cost of the missile single component—engine during storage and the optimal maintenance decision during storage are obtained.

Equivalent continuum models provide an efficient alternative to explicitly resolving the internal architecture of lattice structures, but their accuracy in finite components depends strongly on structural scale, boundary effects, constitutive behavior, and numerical discretization. This study quantitatively evaluates the finite-size accuracy and engineering applicability of a first-order equivalent continuum model for body-centered cubic (BCC) lattice structures. A periodic representative unit cell is first used to determine the effective elastic stiffness of a BCC lattice with a unit-cell size of 5 mm, a strut radius of 0.3 mm, and a relative density of 6.99%. The resulting stiffness matrix exhibits the expected cubic symmetry and pronounced directional anisotropy, while the maximum component-wise difference between the 0.050 and 0.025 mm RVE meshes is only 0.0584%. Explicit lattice structures and corresponding homogeneous continua containing N = 1–5 cells along each direction are then compared under lateral loading and quasi-static compression. The lateral displacement deviation decreases overall from 18.69% at N = 1 to 10.84% at N = 5. Under consistent linear-elastic compression, the reaction-force deviation decreases monotonically from 74.52% to 22.62%, confirming that substantial finite-size and boundary-related effects remain even after constitutive inconsistency is removed. Comparison with elastoplastic simulations further shows that plastic softening can partially mask the underlying lattice–continuum discrepancy. The explicit solid-element modeling strategy is independently verified against a published 7 × 7 × 7 BCC compression experiment, predicting an effective modulus of 0.565 GPa compared with the reported 0.54 GPa, a difference of approximately 4.6%. A dedicated macro-mesh study further demonstrates that the homogeneous model can be substantially coarsened while retaining accurate global response. These results provide practical guidance for selecting between explicit and equivalent-continuum representations according to structural scale, response quantity, constitutive regime, and required engineering accuracy.