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一种实时航班热度评估的多模型混合方案

陶凯1,杨琨1,黄智1,吴涣2,李永2,王超2

(1.欧普杰科技(大连)有限公司,辽宁 大连 116023;2.中国民航信息网络股份有限公司,北京 101318)

栏目:信息化应用 · 第 2026 年第 8 期 · 页码:157-165 · 2096-4706(2026)08-0157-09 · 中图分类号:TP311.5 · 文献标识码:A · DOI:10.19850/j.cnki.2096-4706.2026.08.029

摘要

针对航空旅游零售平台传统缓存策略难以捕捉航线热度动态变化导致的缓存命中率偏低、系统负载过高问题,提出多模型混合推理服务方案。该方案融合离线模型(XGBoost)提供稳定预测,在线模块通过增量训练适配实时流数据;引入ADWIN算法实时检测数据漂移,搭配动态学习率机制加速模型适应。实验在日均航班查询量约百万次的平台测试中表明:模型日均准确率从81.0%提升至87.2%(提升6.2%),缓存命中率提升12.3%,系统响应时间缩短20.4%。此研究为航空平台缓存优化提供可行技术路径,也为电商推荐、交通调度等实时推理场景提供重要借鉴。

关键词

多模型混合;在线机器学习;数据漂移;动态学习率;缓存策略

参考文献

  1. [1] 刘浩霖,赵子玉,谢文飞,等.基于机器学习的民航客运量预测方法研究 [J].应用数学进展,2024,13(8):3814-3825.
  2. [2] 韩金.基于Redis的非均衡访问分布式缓存系统的设计与关键技术实现 [D].成都:电子科技大学,2024.
  3. [3] KÖLKER K,ZENGERLING Z,KÜHLEN M,et al. Assessing the Impact of Contrail Avoidance Through Rescheduling on Airline Network Flows: A Case Study of North Atlantic Flights [J/OL].Transportation Research Part A,2024,187:104155(2024-07-12).https://doi.org/10.1016/j.tra.2024.104155.
  4. [4] 韩萌,孟凡兴,李春鹏,等.基于Kolmogorov不等式的数据流漂移检测方法 [J].计算机工程与应用,2025,61(9):102-115.
  5. [5] ANDREA M,MARINA L D G,ULF N,et al. Studying and Mitigating the Effects of Data Drifts on ML Model Performance at the Example of Chemical Toxicity Data [J/OL].Scientific Reports,2022,12(1): 7244(2022-05-04).https://link.springer.com/article/10.1038/s41598-022-09309-3.
  6. [6] SREEJA A,SANGEETHA E,TANVI P. Remediating Data Drifts and Re-Establishing ML Models [J].Procedia Computer Science,2023,218:799-809.
  7. [7] FERNÁNDEZ-RODRÍGUEZ J D,PALOMO E J,ORTIZ-DE-LAZCANO-LOBATO J M,et al. Dynamic Learning Rates for Continual Unsupervised Learning [J].Integrated Computer-Aided Engineering,2023,30(3):257-273.
  8. [8] ZHANG Q,WU H,TAO J,et al. Fault Diagnosis of Rolling Bearing Based on CNN With Attention Mechanism and Dynamic Learning Rate [C]//2021 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD).Nanjing:IEEE,2021:1-7.
  9. [9] LUO L,WANG Y,PENG H,et al. Training Restricted Boltzmann Machine With Dynamic Learning Rate [C]//2021 International Conference on Sensing,Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD).Nagoya:IEEE,2016:103-107.
  10. [10] 赵海燕,戎小玉,曹健,等. 面向业务过程的概念漂移检测技术综述 [J].计算机集成制造系统,2024,30(10): 3431-3446.
  11. [11] KRAUS A,VAN DER AALST W M P. Machine Learning-Based Detection of Concept Drift in Business Processes [J/OL].Process Science,2025,2(1):5(2025-04-16).https://link.springer.com/article/10.1007/s44311-025-00012-w.
  12. [12] KRZYSZTOF W,PIOTR P,TOMASZ O,et al. A New Concept Drift Detection Method Based on the Ranking of Features in a Data Stream [J].Procedia Computer Science,2023,225:3294-3302.
  13. [13] 王娇娇,马小雨,刘畅,等.基于XGBoost增量实现业务流程执行结果的预测性监控方法 [J].计算机集成制造系统,2024,30(8):2756-2775.
  14. [14] NAVARRO J J M,DOMÍNGUEZ R M J,GARCÍA C M,et al. A Real-Time Machine Learning-Based Methodology for Short-Term Frequency Nadir Prediction in Low-Inertia Power Systems [J/OL].Neurocomputing,2025,626: 129583(2025-04-14).https://doi.org/10.1016/j.neucom.2025.129583.
  15. [15] 成英超.分布式流数据分析与实时机器学习理论与应用研究 [D].广州:广东工业大学,2020.
  16. [16] PITUCKWANICH W,HORMDEE D,BOONKONG A,et al. The Implementation of a Prediction System for Sugarcane's Destruction Rate From Sugarcane Stem Borer via Hybrid Machine Learning [J].IEEE Access,2025,13:45594-45608.
  17. [17] WEI L,MU W,WANG J,et al. Online Incremental Learning Algorithms for Real-Time Fault Diagnosis in Charging Stations [C]//Proceedings of the 7th International Conference on Information Technologies and Electrical Engineering. New York:ACM,2025:201-207.
  18. [18] 林锋.持续训练在机器学习平台中的应用研究 [D].上海:上海交通大学,2021.
  19. [19] JING C,SHENGYI Y,TING G,et al. Multi-Type Concept Drift Detection Under a Dual-Layer Variable Sliding Window in Frequent Pattern Mining With Cloud Computing [J/OL].Journal of Cloud Computing,2024,13(1):25(2024-02-12).https://link.springer.com/article/10.1186/s13677-023-00566-9.
  20. [20] AGRAHARI S,SINGH K A. Comparison Based Analysis of Window Approach for Concept Drift Detection and Adaptation [J/OL].Applied Intelligence,2024,55(1):39(2024-11-27).https://link.springer.com/article/10.1007/s10489-024-05890-4.
  21. [21] 李兆恩,张智海. 基于 Wasserstein 距离的在线机器学习算法研究 [J].中国科学:技术科学,2023,53(7):1031-1042.
  22. [22] RABIU I,SALIM N,NASSER M,et al. Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification [J].Computers, Materials & Continua,2022,74(3):6001-6017.
  23. [23] MOHARRAM H,AWAD A,EL-KAFRAWY P M. Optimizing ADWIN for Steady Streams [C]//Proceedings of the 37th Annual International Conference on Machine Learning. Virtual:PMLR,2022:450-459.
  24. [24] 孙子健,汤健,乔俊飞.面向工业过程难测参数建模的双窗口概念漂移检测 [J].控制理论与应用,2021,38(12):1979-1992.
  25. [25] TU J,YASIN B A,MANSOR B S N. Enhancing Efficiency and Accuracy of Optimization Techniques in Time Series Data Prediction Using Machine Learning: A Systematic Literature Review [J].Journal of Computer Science and Technology Studies,2024,6(5):129-142.

作者简介

陶凯(1977—),男,汉族,湖北武汉人,高级工程师,硕士,研究方向:航空公司数字化转型;杨琨(1986—),男,汉族,河北邯郸人,本科,研究方向:大数据、机器学习;黄智(1991—),男,汉族,辽宁大连人,硕士,研究方向:大模型、机器学习;吴涣(1993—),女,汉族,黑龙江大庆人,助理工程师,本科,研究方向:民航人工智能;李永(1985—),男,汉族,江西景德镇人,高级工程师,硕士,研究方向:民航信息化技术;王超(1988—),男,汉族,黑龙江哈尔滨人,工程师,硕士,研究方向:航空公司电子商务。