A method, system, and medium for managing construction data during non-stop flight operations in the flight zone.

By using dynamic Bayesian network models and machine learning techniques, unsafe behaviors of construction workers and area intrusions are identified, solving the problems of low efficiency of manual inspections and insufficient static assessment in non-stop construction in the flight zone. This enables real-time dynamic management of construction risks, improving safety and management efficiency.

CN121235476BActive Publication Date: 2026-03-06SOUTHWEST PETROLEUM UNIV
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Patent Information

Application Number
CN202511811623.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-12-04
Publication Date
2026-03-06
Estimated Expiration
2045-12-04

AI Technical Summary

Technical Problem

In existing technologies, risk management for non-stop construction in flight zones relies on manual inspections, resulting in low regulatory efficiency, delayed data feedback, difficulty in timely detection and handling of dynamic risks, and static risk assessment methods cannot reflect the inherent correlation and dynamic characteristics of risk factors.

Method used

A dynamic Bayesian network risk evolution model is adopted to construct temporal dependencies by identifying unsafe behaviors of construction workers and intrusion monitoring in neighboring areas, thereby realizing dynamic risk assessment of the construction process and generating a risk distribution heat map. Parameter optimization is performed by combining machine learning and expert knowledge.

Benefits of technology

It enables real-time and automated perception of risks associated with non-stop construction in the flight zone, improving the accuracy of risk identification and the effectiveness of management measures, transforming into intelligent and dynamic prediction, and ensuring safety during construction.

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Abstract

This invention discloses a method, system, and medium for managing construction data during non-stop operation in the airfield, relating to the field of airport construction management. This invention introduces a dynamic Bayesian network model into airfield construction risk management. By dividing construction time slices and establishing temporal dependencies between risk factor nodes and target risk nodes, it achieves accurate modeling of the dynamic evolution of risks throughout the entire construction process. Utilizing diagnostic reasoning technology, it predicts the probability of occurrence of different risk factors at each construction stage and generates an intuitive risk distribution heatmap. This transforms risk management from traditional manual inspections, static assessments, and post-event responses into a new model of intelligent, dynamic prediction, and precise intervention, significantly improving the accuracy of risk identification, the timeliness of early warnings, and the effectiveness of management measures, fundamentally ensuring the safety and efficiency of airport operations during non-stop construction.
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