A Method for Constructing a Domain-Specific Large Language Model for Carrier Air-Hub Operation Parsing
-
-
Abstract
Objectives Carrier air hub operation parsing is characterized by strong domain specificity and complex spatiotemporal constraints, and is therefore prone to parsing errors, omission of critical information, and structural inconsistency. For aircraft carrier operational scenarios with highly constrained resources, this study investigates how to construct a deployable domain specific large model to improve accuracy, reliability, and consistency in carrier air hub operation parsing tasks. Methods We propose a domain-specific large model construction method for carrier air-hub operation parsing, termed the Carrier Air-Hub Operation Parsing Large Model (CAOP). In the training stage, high dimensional distillation is employed to transfer the parsing capabilities of a cloud based teacher model across six key information dimensions, namely entity recognition, relation recognition, statistical information, predictive information, text and numerical joint semantic parsing, and abnormal event cue extraction, to a local model. In the inference stage, a carrier aviation operation knowledge base and case retrieval are introduced, and collaborative verification is adopted to impose evidence constraints and structural consistency constraints on candidate outputs and to further refine the parsing results. Results Experiments show that CAOP improves the average score of the local model Qwen3-32B on carrier air-hub operation parsing from 13.3 to 18.4, corresponding to a gain of 5.1 points, approximately 38.3%. CAOP outperforms many cloud-based models and achieves performance comparable to human experts. Conclusions The results demonstrate that CAOP significantly enhances the accuracy, reliability, and consistency of carrier air-hub operation parsing under local deployment constraints. It provides a reliable data foundation and trustworthy structured inputs for subsequent tasks including automatic scheduling plan generation, conflict detection, and optimization solving.
-
-
DownLoad: