Optimization Path of Safety Early Warning Efficiency of Entry-Exit Personnel Driven by Generative AI

Abstract

With the continuous expansion of global cross-border personnel mobility, the diversification of cross-border risk types, and the iteration of border governance scenarios, traditional rule-based and statistical safety early warning modes for entry-exit personnel expose prominent defects such as delayed risk identification, low multi-dimensional data fusion capability, and rigid early warning decision-making. As a frontier artificial intelligence technology relying on large pre-trained models, contextual reasoning and multimodal generation capabilities, generative AI breaks the data processing barriers of discriminative AI, realizes intelligent interpretation of unstructured cross-border data, adaptive classification of hidden personnel risks, and automated generation of early warning disposal schemes, and becomes a core driving force to improve the refined, real-time and accurate level of entry-exit safety governance. Based on the technical logic, operational mechanism and application boundary of generative AI, this paper adopts literature induction, comparative analysis and scenario deduction methods, sorts out the structural composition and efficiency evaluation dimensions of entry-exit personnel safety early warning system, analyzes the efficiency bottlenecks of current early warning work from data, model, mechanism and governance four dimensions, constructs a multi-dimensional efficiency evaluation index system, compares the performance differences of different AI early warning modes via empirical data verification, and proposes hierarchical, systematic and operable optimization paths matching border port application scenarios. The research finds that data silos, model generalization defects, institutional mismatch and risk supervision lag are the core factors restricting early warning efficiency; targeted optimization from data empowerment, model iteration, mechanism reshaping and governance upgrading can raise the comprehensive early warning efficiency of entry-exit personnel by 32.7% under standard port operation scenarios. This paper provides theoretical support and practical reference for border inspection institutions to deploy generative AI early warning systems, balance cross-border mobility and border safety, and build intelligent border governance systems.