A Method for Communication Protocol File Format Conversion Using AI Models

During multiple systems communication, heterogeneous, manually written communication-protocol formats need to be aligned. Manual alignment is time-consuming, whereas software-based solutions demand heavy, error-prone coding and still offer limited functionality. We propose a fully automatic, AI-model-driven converter (from protocol to configuration file) that eliminates heavy human coding workload. Given the protocol document and the configuration-file template supported by the target communication framework, several AI models are invoked in parallel to produce a pool of candidate configuration files; after text embedding, a maximum-consensus algorithm selects the version that is most similar to all others as the final output. Experiments show that the resulting configuration files achieve near-perfect accuracy upon manual inspection, proving that multi-model consensus can reliably generate deployable protocol parsers without hard-coding or protocol-translation middleware. The approach significantly shortens system-integration cycles, improves maintainability, and provides an extensible technical route for future evolution.