基于因果推理的航班地面保障事件时间约束优化方法

By constructing a time constraint optimization method for flight ground support events using causal reasoning technology, this method solves the problem of insufficient causal relationship identification in existing technologies, realizes the globally optimal time constraint strategy, and improves the on-time departure rate of flights and the efficiency of airport operation management.

CN122175019BActive Publication Date: 2026-07-17CIVIL AVIATION FLIGHT UNIV OF CHINA

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CIVIL AVIATION FLIGHT UNIV OF CHINA
Filing Date
2026-05-13
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies cannot accurately depict the true causal relationship between factors affecting flight ground support and support events, cannot quantify the real impact of different control behaviors on support efficiency, and are difficult to achieve globally optimal time-constrained decision-making throughout the entire support process, resulting in serious flight departure delays.

Method used

A causal reasoning-based approach is adopted. By constructing a hierarchical analysis system for flight ground support, a standardized quantitative reference dataset is obtained, a causal structure feasible region matrix is ​​generated, a sparse causal adjacency matrix is ​​learned, the causal strength of the intervention interval is calculated, a Markov decision process model is constructed, the globally optimal time constraint strategy is solved, and robustness verification is performed.

Benefits of technology

It has achieved accurate identification of the core driving factors of flight ground support efficiency, quantified the real impact of control behaviors, generated globally optimal time constraint strategies, improved flight punctuality and airport management level, and has robustness and generalization ability.

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Abstract

本发明提供了基于因果推理的航班地面保障事件时间约束优化方法,包括以下步骤:步骤一,分层分析体系构建与数据标准化处理;步骤二,民航先验约束集与因果可行域矩阵生成;步骤三,约束型NOTEARS‑MCP算法因果结构学习;步骤四,影响因素异质性区间划分与有效干预集生成;步骤五,熵平衡加权因果效应量化与强度排序;步骤六,保障流程马尔可夫决策过程建模与空间压缩;步骤七,因果双约束初始行为策略集构建;步骤八,路径积分控制算法全局最优策略求解;步骤九,最优策略鲁棒性扰动校验;步骤十,航班地面保障事件时间管控要求输出与落地映射。本发明有效降低了航班离港延误风险,助力机场实现保障环节的精细化、智能化管控。
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