The invention relates to a cross-layer optical
network routing optimization method assisted by a large
language model. The method comprises the following steps: constructing a framework of a cross-layer optical network, and collecting network physical node composition, link resource parameters and service request characteristics; designing an auxiliary
graph model, and converting a cross-layer
network routing optimization problem into a
minimum cost path calculation problem in a virtual topology; building an auxiliary
graph model weight optimization framework endowed by the large
language model, and preliminarily generating an edge weight of the auxiliary
graph model by the large
language model; calculating a
minimum cost path based on the edge weight of the initial auxiliary graph, evaluating a
network performance index corresponding to the path, inputting an
evaluation result as feedback data into the large language model, and iteratively adjusting the edge weight until the
performance index is converged to be optimal; and completing routing planning and
wavelength allocation of all service requests in the auxiliary graph model according to the optimal edge weight. According to the method, a large language model and an auxiliary graph model framework are combined, so that the flexibility and the multi-target
adaptation capability of routing optimization are improved, and
global optimization is realized.