[1] BRODERICK S. GE ramping up results-driven big data analytics[J]. International Aviation, 2016(5): 74-75. [2] KONG X F, CAI J Q, ZHANG L H, et al. Research status and development trend of big data in aviation system[J]. Acta Aerogautica ET Astronautica Sinica, 2018, 39(12): 8-23. 孔祥芬, 蔡峻青, 张利寒, 等. 大数据在航空系统的研究现状与发展趋势[J]. 航空学报, 2018, 39(12): 8-23. [3] ODARCHENKO R, HASSAN Z, ZAMAN A. Use of big data in aviation: new opportunities, use cases, and solutions[M]//Automated Systems in the Aviation and Aerospace Industries. IGI Global, 2019: 436-452. [4] LI M Z, RYERSON M S. Reviewing the DATAS of aviation research data: diversity, availability, tractability, applicability, and sources[J]. Journal of Air Transport Management, 2019, 75: 111-130. [5] TAN W T. Researches on flight simulation technology via QAR data[D]. Tianjin: Civil Aviation University of China, 2018. 谭文韬. 利用QAR数据实现模拟飞行技术研究[D]. 天津: 中国民航大学, 2018. [6] YANG Q F, REN Z, DONG W F. The decoding and analysis techniques of flight data recorders in an aviation accident investigation[J]. Aviation Maintenance & Engineering, 2004(5): 40-42. 杨全法, 任章, 董伟凡. 飞行事故调查中的飞行数据记录器译码分析技术[J]. 航空维修与工程, 2004(5): 40-42. [7] WAN S P. The cockpit voice signal analysis and security diagnosis based on information fusion[D]. Shanghai: Shanghai Institute of Technology, 2015. 万守鹏. 基于信息融合的舱音信号分析与安全诊断[D]. 上海: 上海应用技术学院, 2015. [8] KURT K, KENT J, DARRY J. Aircraft flight data management system: 7203630[P]. 2007-04-10. [9] LIU F K, LI Q. Development and application of big data technology in aviation industry[J]. Telecommunication Engineering, 2017, 57(7): 849-854. 刘丰恺, 李茜. 航空大数据技术的发展与应用[J]. 电讯技术, 2017, 57(7): 849-854. [10] BURMESTER G, MA H, STEINMETZ D, et al. Big data and data analytics in aviation[M]//Advances in Aeronautical Informatics. Berlin, Heidelberg: Springer, 2018. [11] CHEN H Y, WANG J D, YAN X F. A fuzzy support vector machine with weighted margin for flight delay early warning[C]//Proceedings of the 5th International Conference on Fuzzy Systems & Knowledge Discovery, Jinan, Oct 18-20, 2008. Washington: IEEE Computer Society, 2008: 331-335. [12] ZANIN M. Network analysis reveals patterns behind air safety events[J]. Physica A: Statistical Mechanics & Its Applications, 2014, 401: 201-206. [13] LI L, HANSMAN R J, PALACIOS R, et al. Anomaly detection via a Gaussian mixture model for flight operation and safety monitoring[J]. Transportation Research Part C:Emerging Technologies, 2016, 64: 45-57. [14] LI Y J, ZHANG J, CAO Y Y, et al. Forecasting of aero-engine performance trend based on fuzzy information granulation and optimized SVM[J]. Journal of Aerospace Power, 2017, 32(12): 3022-3030. 李艳军, 张建, 曹愈远, 等. 基于模糊信息粒化和优化SVM的航空发动机性能趋势预测[J]. 航空动力学报, 2017, 32(12): 3022-3030. [15] TIAN D H, HE J M, ZHANG B Q. Research on aviation ammunition consumption prediction based on NRS-SVM model[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2018, 50(5): 94-99. 田德红, 何建敏, 张保强. 基于NRS-SVM模型的航空弹药消耗预测研究[J]. 南京航空航天大学学报, 2018, 50(5): 94-99. [16] ZHANG J, YANG G B, PENG X F, et al. Research on dynamic prediction method of civil aircraft fuel consumption[J]. Control Engineering of China, 2019, 26(4): 682-687. 张军, 杨贵宾, 彭晓峰, 等. 民航客机运营燃油消耗的动态预测方法研究[J]. 控制工程, 2019, 26(4): 682-687. [17] MANNA S, BISWAS S, KUNDU R, et al. A statistical approach to predict flight delay using gradient boosted decision tree[C]//Proceedings of the 2017 International Conference on Computational Intelligence in Data Science, Chennai, Jun 2-3, 2017. Piscataway: IEEE, 2017: 1-5. [18] MANGORTEY E, GILLERON J, DARD G, et al. Development of a data fusion framework to support the analysis of aviation big data[C]//Proceedings of the 2019 AIAA Scitech Forum, San Diego, Jan 7-11, 2019. Menlo Park: AAAI, 2019: 1-18. [19] LU Z L, WANG J D, ZHENG G S. A new method to alarm large scale of flights delay based on machine learning[C]//Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling, Wuhan, Dec 21-22, 2008. Piscataway: IEEE, 2008: 589-592. [20] BALUCH M, BERGSTRA T, EL-HAJJ M. Complex analysis of united states flight data using a data mining approach[C]//Proceedings of the 2017 IEEE 7th Annual Computing and Communication Workshop and Conference, Las Vegas, Jan 9-11, 2017. Piscataway: IEEE, 2017: 1-6. [21] CHRISTOPHER A B A, BALAMURUGAN S A A. Prediction of warning level in aircraft accidents using data mining techniques[J]. The Aeronautical Journal, 2014, 118(1206): 935-952. [22] SKORMIN V A, GORODETSKI V I, POPYACK L J. Data mining technology for failure prognostic of avionics[J]. IEEE Transactions on Aerospace & Electronic Systems, 2002, 38(2): 388-403. [23] SHEN Y W. Airline customer classification and churn forecast[D]. Guiyang: Guizhou University of Finance and Economics, 2019. 申玉伟. 航空公司客户分类及流失预测[D]. 贵阳: 贵州财经大学, 2019. [24] GUI G, LIU F, SUN J, et al. Flight delay prediction based on aviation big data and machine learning[J]. IEEE Trans-actions on Vehicular Technology, 2020, 69(1): 140-150. [25] BELCASTRO L, MAROZZO F, TALIA D, et al. Using scalable data mining for predicting flight delays[J]. ACM Transactions on Intelligent Systems & Technology, 2016, 8(1): 1-20. [26] CHOI S, KIM Y J, BRICENO S, et al. Prediction of weather-induced airline delays based on machine learning algorithms[C]//Proceedings of the 2016 IEEE/AIAA 35th Digital Avionics Systems Conference, Sacramento, Sep 25-29, 2016. Piscataway: IEEE, 2016: 1-6. [27] PAMPLONA D A, LI W G, DE BARROS A G, et al. Supervised neural network with multilevel input layers for predicting of air traffic delays[C]//Proceedings of the 2018 International Joint Conference on Neural Networks, Rio de Janeiro, Jul 8-13, 2018. Piscataway: IEEE, 2018: 1-6. [28] KIM Y J, CHOI S, BRICENO S, et al. A deep learning approach to flight delay prediction[C]//Proceedings of the 2016 IEEE/AIAA 35th Digital Avionics Systems Conference, Sacramento, Sep 25-29, 2016. Piscataway: IEEE, 2016: 7-12. [29] ZHANG J J. Aeroengine performance parameters pre diction using information fusion based on discreted process neural network[D]. Heilongjiang: Harbin Institute of Technology, 2015. 张颉健. 基于离散过程神经网络的航空发动机性能参数融合预测技术研究[D]. 黑龙江: 哈尔滨工业大学, 2015. [30] TIAN D H, HE J M. Aviation ammunition consumption prediction model based on mutated particle swarm optimization and deep neural network[J]. Journal of Nanjing University of Science and Technology, 2018, 42(6): 716-721. 田德红, 何建敏. 基于变异粒子群优化与深度神经网络的航空弹药消耗预测模型[J]. 南京理工大学学报, 2018, 42(6): 716-721. [31] WU Z T, LI X R, DU J. Research on fuel consumption model of a military aircraft in descent stage[J]. Journal of Signal Processing, 2019, 35(12): 2036-2044. 吴祯涛, 李学仁, 杜军. 某型军用飞机下降阶段燃油消耗模型研究[J]. 信号处理, 2019, 35(12): 2036-2044. [32] YUE J C. Research on short trajectory prediction method based on LSTM-ARIMA and visualization system[D]. Tianjin: Civil Aviation University of China, 2020. 岳聚财. 基于LSTM-ARIMA的短期航迹预测方法研究及可视化系统开发[D]. 天津: 中国民航大学, 2020. [33] SMITH L D, EHMKE J F. A mathematical programming technique for matching timestamped records in logistics and transportation systems[J]. Transportation Research Part C: Emerging Technologies, 2016, 69: 375-385. [34] RAVIZZA S, CHEN J, ATKIN J A D, et al. Aircraft taxi time prediction: comparisons and insights[J]. Applied Soft Computing, 2014, 14(1): 397-406. [35] RAVIZZA S, ATKIN J A D, MAATHUIS M H, et al. A combined statistical approach and ground movement model for improving taxi time estimations at airports[J]. Journal of the Operational Research Society, 2013, 64(9): 1347-1360. [36] ZHAO G S, WU S S, RONG H J. A multi-source statistics data-driven method for remaining useful life prediction of aircraft engine[J]. Journal of Xi??an Jiaotong University, 2017, 51(11): 150-155. 赵广社, 吴思思, 荣海军. 多源统计数据驱动的航空发动机剩余寿命预测方法[J]. 西安交通大学学报, 2017, 51(11): 150-155. [37] REBOLLO J J, BALAKRISHNAN H. Characterization and prediction of air traffic delays[J]. Transportation Research Part C: Emerging Technologies, 2014, 44: 231-241. [38] PAGELS D A. Aviation data mining[J]. Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, 2015, 2(1): 1-7. [39] JANAKIRAMAN V M, MATTHEWS B, OZA N. Using ADOPT algorithm and operational data to discover precursors to aviation adverse events[C]//Proceedings of the 2018 AIAA Information Systems-AIAA Infotech @ Aerospace Conference, Kissimmee, Jun 8-12, 2018. New York: AIAA, 2018: 1638-1651. [40] BALAKRISHNA P, GANESAN R, SHERRY L. Accuracy of reinforcement learning algorithms for predicting aircraft taxi-out times: a case-study of Tampa Bay departures[J]. Transportation Research, 2010, 18C(6): 950-962. [41] FU J F, LI H C, FAN D, et al. Modeling and efficiency prediction of aeroengine centrifugal pump integrated loss model based on one-dimensional flow[J]. Journal of Northwestern Polytechnical University, 2018, 36(5): 8-16. 符江锋, 李华聪, 樊丁, 等. 基于一元流动的航空离心泵综合损失模型与效率预测[J]. 西北工业大学学报, 2018, 36(5): 8-16. [42] ZHAO G, LI S X, GUO M, et al. Prediction and experiment of vibration wear of aviation spline[J]. Journal of Aerospace Power, 2018, 33(12): 2958-2964. 赵广, 李盛翔, 郭梅, 等. 航空花键振动磨损预测与实验[J]. 航空动力学报, 2018, 33(12): 2958-2964. [43] TU Y, BALL M O, JANK W S. Estimating flight departure delay distributions—a statistical approach with long-term trend and short-term pattern[J]. Journal of the American Statistical Association, 2008, 103(481): 112-125. [44] CHEN J. Biological inspired optimization algorithms for transparent knowledge extraction allied to engineering materials process[D]. Sheffield: The University of Sheffield, 2009. [45] CUI J G, GAO B, JIANG L Y, et al. Research on the technology of aeroenging condition prediction based on GRVM[J]. Control Engineering of China, 2018, 25(10): 1854-1858. 崔建国, 高波, 蒋丽英, 等. 基于GRVM的航空发动机状态预测技术研究[J]. 控制工程, 2018, 25(10): 1854-1858. [46] CASTILHO I X. Fault prediction in aircraft tires using Bayesian networks[D]. Lisbon: Técnico, 2015. [47] AYHAN S, SAMET H. DICLERGE: divide-cluster-merge framework for clustering aircraft trajectories[C]//Proceedings of the 8th ACM SIGSPATIAL International Workshop on Computational Transportation Science, Washington, Nov 3, 2015. New York: ACM, 2015: 7-14. [48] ZHANG T X, BAO D W, DI Z W, et al. Research on air spatial behavior patterns of passengers based on K-means[J]. Journal of East China Jiaotong University, 2019(5): 59-66. 张天炫, 包丹文, 狄智玮, 等. 基于K-means的航空旅客空间行为模式研究[J]. 华东交通大学学报, 2019(5): 59-66. [49] CHANG S Y, BAI X Z, LIU J. A two-dimensional shock wave pattern recognition algorithm based on cluster analysis[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(8): 162-175. 常思源, 白晓征, 刘君. 一种基于聚类分析的二维激波模式识别算法[J]. 航空学报, 2020, 41(8): 162-175. [50] JIANG P, LI T. Analysis of aviation customer value based on big data[J]. Journal of Civil Aviation, 2019, 3(3): 1-4. 姜朋, 李挺. 基于大数据的航空客户价值分析[J]. 民航学报, 2019, 3(3): 1-4. [51] TANG J, WANG J, LI D M. Flight track data analysis method based on balanced kernel function clustering[J]. Journal of Naval Aeronautical and Astronautical University, 2019, 34(6): 493-498. 唐静, 王婧, 李冬梅. 基于平衡核函数聚类的飞行航迹数据分析方法[J]. 海军航空工程学院学报, 2019, 34(6): 493-498. [52] PISAREK R, AKPINAR M T, HIZIROGLU A. Data mining application in air transportation-the case of Turkish airlines[J]. Logistics and Transport, 2017, 36(4): 79-88. [53] WANG Z L, LI J, SONG Y F. Improved K-means algorithm based on distance and weight[J]. Computer Engineering and Applications, 2020, 56(23): 87-94. 王子龙, 李进, 宋亚飞. 基于距离和权重改进的K-means算法[J]. 计算机工程与应用, 2020, 56(23): 87-94. [54] LIU Z J. Potential high value passenger discovery based on booking behavior analysis[D]. Tianjin: Civil Aviation University of China, 2020. 刘泽君. 基于旅客订票行为分析的潜在高价值旅客发现[D]. 天津: 中国民航大学, 2020. [55] XU T, XIE J W, YANG G Q. Airport noise data mining method based on hierarchical clustering[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2013, 45(5): 715-721. 徐涛, 谢继文, 杨国庆. 一种基于层次聚类的机场噪声数据挖掘方法[J]. 南京航空航天大学学报, 2013, 45(5): 715-721. [56] WANG X, DU Y, ZHAO Y J, et al. Fault detection and identification for a dual-redundant brushless DC motor system using wavelet transform and hierarchical clustering algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(10): 1436-1441. 王欣, 杜阳, 周元钧, 等. 基于小波变换和聚类的BLDCM故障检测与识别[J]. 北京航空航天大学学报, 2014, 40(10): 1436-1441. [57] LI N, JIN H H, QIANG Y G. Analysis of aircraft trajectory clustering based on multi-dimensional feature terminal area[J]. Aeronautical Computing Technique, 2019, 49(2): 19-22. 李楠, 靳辉辉, 强懿耕. 基于多维特征终端区航空器轨迹聚类研究[J]. 航空计算技术, 2019, 49(2): 19-22. [58] LI N, DENG R B, LIU P, et al. Research on departure aircraft tracks spectral clustering in terminal airspace based speed correction[J]. Journal of Wuhan University of Technology, 2017, 60(11): 27-31. 李楠, 邓人博, 刘朋, 等. 基于速度修正的终端区离场航空器的轨迹谱聚类研究[J]. 武汉理工大学学报, 2017, 60(11): 27-31. [59] CONG W, HU M, DONG B, et al. Empirical analysis of airport network and critical airports[J]. Chinese Journal of Aeronautics, 2016, 19(2): 512-519. [60] WU X Y. Spectral clustering algorithm based on Spark and the application on QAR data[D]. Tianjin: Civil Aviation University of China, 2017. 吴稀钰. 基于Spark的谱聚类算法及其在QAR数据中的应用[D]. 天津: 中国民航大学, 2017. [61] DENG R B. Research on abnormal behavior identification of aircraft in terminal area based on monitoring data[D]. Tianjin: Civil Aviation University of China, 2018. 邓人博. 基于监视数据的终端区航空器异常行为识别研究[D]. 天津: 中国民航大学, 2018. [62] GARIEL M, SRIVASTAVA A N, FERON E. Trajectory clustering and an application to airspace monitoring[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1511-1524. [63] ZHANG P, LI J, ZHAO Z Q, et al. Track clustering for route fuel prediction[J]. Aeronautical Computing Technique, 2019, 49(6): 44-47. 张朋, 李杰, 赵志奇, 等. 面向航路燃油预测的航迹聚类[J]. 航空计算技术, 2019, 49(6): 44-47. [64] DONG Y Q. Analysis and research of spatio-temporal trajectory clustering based on improved DBSCAN[D]. Tianjin: Tianjin University, 2019. 董一强. 基于DBSCAN改进算法的时空轨迹聚类分析与研究[D]. 天津: 天津大学, 2019. [65] CAO Y Y, ZHANG B W, LI Y J. AP clustering improved immune algorithm for aeroengine fault diagnosis[J]. Journal of Aerospace Power, 2019, 34(8): 1795-1804. 曹愈远, 张博文, 李艳军. AP聚类改进免疫算法用于航空发动机故障诊断[J]. 航空动力学报, 2019, 34(8): 1795-1804. [66] QI L, XIONG W, HE Y. Anti-bias track-to-track association algorithm for aircraft platforms based on distance hierarchical clustering[J]. Acta Electronica Sinica, 2018, 46(6): 1475-1481. 齐林, 熊伟, 何友. 基于距离分级聚类的机载雷达航迹抗差关联算法[J]. 电子学报, 2018, 46(6): 1475-1481. [67] STERNBERG A, CARVALHO D, MURTA L, et al. An analysis of Brazilian flight delays based on frequent patterns[J]. Transportation Research Part E: Logistics and Transportation Review, 2016, 95E: 282-298. [68] HOU X T. Data mining of civil aviation accidents based on multi-association rules[D]. Tianjin: Civil Aviation University of China, 2016. 侯熙桐. 基于多维关联规则的民航事故数据挖掘研究[D]. 天津: 中国民航大学, 2016. [69] CHEN X X, LIU K, MA S T, et al. Fault correlation analysis of air equipment based on Apriori algorithm[J]. Journal of Shandong Normal University (Natural Science), 2019, 34(1): 52-57. 陈秀秀, 刘凯, 马双涛, 等. 基于Apriori算法的航空设备故障关联分析[J]. 山东师范大学学报(自然科学版), 2019, 34(1): 52-57. [70] GU F. Association analysis of monitoring points value of airport noise[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014. 谷飞. 机场噪声多监测点噪声值关联分析[D]. 南京: 南京航空航天大学, 2014. [71] CAO W D, XU D D, WANG J, et al. NOSHOW prediction and strong factor association analysis in civil aviation[J]. Computer Engineering and Applications, 2019, 55(2): 227-233. 曹卫东, 许代代, 王静, 等. 民航NOSHOW预测及强因子关联分析[J]. 计算机工程与应用, 2019, 55(2): 227-233. [72] WEI J. Research on user activities analysis based on aviation data mining[D]. Nanjing: Southeast University, 2017. 卫锦. 基于航空数据挖掘的用户行为分析研究[D]. 南京: 东南大学, 2017. [73] KUANG D, FU Y M, FANG L Y. Application of big data mining analysis in aircraft engine condition monitoring and fault diagnosis[J]. Journal of Xi??an Aeronautical University, 2017, 35(5): 42-46. 旷典, 付尧明, 房丽瑶. 大数据挖掘分析在航空发动机状态监控与故障诊断中的应用[J]. 西安航空学院学报, 2017, 35(5): 42-46. [74] DAS S, MATTHEWS B L, SRIVASTAVA A N, et al. Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, Jul 25-28, 2010. New York: ACM, 2010: 47-56. [75] PURANIK T G, MAVRIS D N. Anomaly detection in general-aviation operations using energy metrics and flight-data records[J]. Journal of Aerospace Computing, Information, and Communication, 2018, 15(1): 22-35. [76] JIA G, CHENG F, YANG J, et al. Intelligent checking model of Chinese radiotelephony read-backs in civil aviation air traffic control[J]. Chinese Journal of Aeronautics, 2018, 31(12): 99-108. [77] AKERMAN S, HABLER E, SHABTAI A, et al. VizADS-B: analyzing sequences of ADS-B images using explainable convolutional LSTM encoder-decoder to detect cyber attacks[J]. arXiv:1906.07921, 2019. [78] WEN Y, YAN Y H. Aero engine anomaly monitoring based on self-adaptive kernel principal component analysis[J]. Ordnance Industry Automation, 2016, 35(8): 1-4. 文莹, 闫雅慧. 基于自适应核主元分析的航空发动机异常监测[J]. 兵工自动化, 2016, 35(8): 1-4. [79] WU Q, CHU Y X. Abnormal flight states of aircraft identification based on deep learning method[J]. Civil Aircraft Design & Research, 2017(3): 68-79. 吴奇, 储银雪. 基于深度学习的航空器异常飞行状态识别[J]. 民用航空器设计与研究, 2017(3): 68-79. [80] WANG X H, ZOU J, LI L, et al. The anomaly detection based on TBM two-level fusion architecture[J]. Acta Electronica Sinica, 2017, 45(3): 577-583. 王晓华, 邹杰, 李立, 等. 基于TBM双层融合架构的航路属性异常检测[J]. 电子学报, 2017, 45(3): 577-583. [81] BUDALAKOTI S, SRIVASTAVA A N, OTEY M E. Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety[J]. IEEE Transactions on Systems, Man and Cybernetics: Part C, Applications and Reviews, 2009, 39(1): 101-113. [82] LI N, QIANG Y G, SUN Y, et al. Research on identifi- cation of aircraft abnormal trajectory in terminal area[J]. China Safety Science Journal, 2018, 28(11): 25-31. 李楠, 强懿耕, 孙瑜, 等. 终端区航空器异常轨迹识别研究[J]. 中国安全科学学报, 2018, 28(11): 25-31. [83] ZHAO J, QI K, GAO Z X. Study of flight abnormal detection based on QAR data cluster analysis[J]. Aeronautical Computing Technique, 2018, 48(2): 52-56. 赵剑, 齐凯, 高振兴. 基于QAR数据聚类分析的航班异常检测研究[J]. 航空计算技术, 2018, 48(2): 52-56. [84] CHURCHILL A M, BLOEM M. Clustering aircraft trajectories on the airport surface[C]//Proceedings of the 13th USA/Europe Air Traffic Management Research and Development Seminar, Chicago, Jun 7-21, 2019. Washington: Federal Aviation Administration and European Aviation Control Authority, 2019: 10-13. [85] JARRY G, DELAHAYE D, NICOL F, et al. Aircraft atypical approach detection using functional principal component analysis[J]. Journal of Air Transport Management, 2020, 84: 101787. [86] GU Z P, LIU X B, HAN Z D, et al. Aero engine fault diagnosis based on improved density peak clustering[J]. Computer Integrated Manufacturing Systems, 2020, 26(5): 1211-1217. 辜振谱, 刘晓波, 韩子东, 等. 基于改进密度峰值聚类的航空发动机故障诊断[J]. 计算机集成制造系统, 2020, 26(5): 1211-1217. [87] LV C, CHENG G, LIU Y Q. Aeroengine fault data tag using on improved BDPCA algorithm[J]. Journal of Vibration and Shock, 2020, 39(9): 35-41. 吕超, 程弓, 刘云清. 基于BDPCA聚类算法的航空发动机故障数据标记[J]. 振动与冲击, 2020, 39(9): 35-41. [88] BURZLAFF M. Aircraft fuel consumption estimation and visualization Hamburg: aircraft design and systems group (AERO)[D]. Hamburg: Hamburg University of Applied Sciences, 2017. [89] LI M Z, SUH D Y, RYERSON M S. Visualizing aviation impacts: modeling current and future flight trajectories with publicly available flight data[J]. Transportation Research Part D: Transport and Environment, 2018, 63: 769-785. [90] ZHU Z T. Analysis and research of general aviation accident based on flight data visualization[D]. Guanghan: Civil Aviation Flight University of China, 2019. 朱志童. 基于飞行数据可视化的通用航空事故调查技术研究与应用[D]. 广汉: 中国民用航空飞行学院, 2019. [91] WANG B, TANG H L, ZHONG R H, et al. Visualize framework for aero-engine performance simulation[J]. Journal of Aerospace Power, 2009, 24(3): 602-607. 王波, 唐海龙, 仲如浩, 等. 可视化航空发动机性能仿真模型[J]. 航空动力学报, 2009, 24(3): 602-607. [92] CHENG Z Y. Research and realization on visualization of aero-engine machining process execution[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014. 程振阳. 航空发动机机加工艺执行可视化技术研究与实现[D]. 南京: 南京航空航天大学, 2014. [93] OMIDVAR-TEHRANI B, NANDI A, MEYER N, et al. DV8: interactive analysis of aviation data[C]//Proceedings of the 33rd IEEE International Conference on Data Engineering, San Diego, Apr 19-22, 2017. Washington: IEEE Computer Society, 2017: 1411-1412. [94] KARIKAWA D, AOYAMA H, TAKAHASHI M, et al. A visualization tool of en route air traffic control tasks for describing controller??s proactive management of traffic situations[J]. Cognition Technology & Work, 2013, 15: 207-218. [95] HE P. Research of key characteristic??s construct and visualization based on 3D model[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2014. 贺鹏. 基于三维数模的关键特性构建及可视化研究[D]. 南京: 南京航空航天大学, 2014. [96] WEIBEL N, FOUSE A, EMMENEGGER C, et al. Let??s look at the cockpit: exploring mobile eye-tracking for observational research on the flight deck[C]//Proceedings of the 2012 Symposium on Eye-Tracking Research and Applications, Santa Barbara, Mar 28-30, 2012. New York: ACM, 2012: 107-114. [97] KHOURY H M, KAMAT V R, IOANNOU P G. Evaluation of general-purpose construction simulation and visualization tools for modeling and animating airside airport operations[J]. Simulation, 2007, 83(9): 663-679. [98] DU Y L. Visualizable flight simulation based on MATLAB and FLIGHTGEAR[J]. Aeronautical Science & Technology, 2015, 26(5): 93-98. 杜永良. 基于MATLAB和FLIGHTGEAR的可视化飞行仿真[J]. 航空科学技术, 2015, 26(5): 93-98. [99] XU Z. Research on flight data visualization simulation for Cessna 172 airplane[D]. Tianjin: Civil Aviation Flight University of China, 2015. 许志. 塞斯纳172飞机飞行数据可视化仿真技术研究[D]. 天津: 中国民用航空飞行学院, 2015. [100] YANG Y Z, GUO Y Q, MAO H T. Visualization co-simulation of fuel regulator based on AMESim and MATLAB[J]. Aeroengine, 2019, 45(3): 26-30. 杨元桢, 郭迎清, 毛皓天. 基于AMESim和MATLAB的燃油调节器可视化联合仿真[J]. 航空发动机, 2019, 45(3): 26-30. [101] SHI Y S, HUANG J. VMTS based rotor servo actuator fault diagnosis process visualization[J]. Computer Measurement & Control, 2016, 24(2): 18-21. 史永胜, 黄杰. 基于VMTS的旋翼伺服作动器故障诊断过程可视化[J]. 计算机测量与控制, 2016, 24(2): 18-21. [102] INS E H, WEIBEL N, EMMENEGGER C, et al. An integrative approach to understanding flight crew activity[J]. Journal of Cognitive Engineering and Decision Making, 2013, 7(4): 353-376. [103] HERNáNDEZ A M, SCARLATTI D, COSTAS P. Real-time estimated time of arrival prediction system using historical surveillance data[C]//Proceeding of the 45th Euromicro Conference on Software Engineering and Advanced Applications, Kallithea-Chalkidiki, Aug 28-30, 2019. Piscataway: IEEE, 2019: 174-177. [104] VLADIMíR S, SCHLENKER J, PETER K, et al. Effect of the change of flight, navigation and motor data visualization on psychophysiological state of pilots[C]//Proceedings of the 13th IEEE International Symposium on Applied Machine Intelligence and Informatics, Indore, Dec 21-23, 2015. Piscataway: IEEE, 2015: 339-344. [105] SONG W Q, XU Y, XU L. Application study of visual interaction techniques in aeronautical CFD software[J]. Aeronautical Science & Technology, 2017, 28(5): 63-66. 宋万强, 徐悦, 徐琳. CFD软件可视化交互技术在航空领域应用研究[J]. 航空科学技术, 2017, 28(5): 63-66. [106] XU H R, CHEN M Y, ZHANG X Y. Big data system of aircraft maintenance based on Flume, Kafka, Storm and HDFS[J]. Journal of Shanghai University of Engineering Science, 2015, 29(4): 303-305. 徐海荣, 陈闵叶, 张兴媛. 基于Flume, Kafka, Storm, HDFS的航空维修大数据系统[J]. 上海工程技术大学学报, 2015, 29(4): 303-305. [107] LEE A S, SEAN B. Big data outcomes in aviation industry[J]. Aviation Maintenance & Engineering, 2019(8): 12-14. LEE A S, SEAN B. 航空业基于大数据的预测维修发展现状[J]. 航空维修与工程, 2019(8): 12-14. [108] RODRIGUES D, LAVORATO P. Maintenance, repair and overhaul (MRO) fundamentals and strategies: an aeronautical industry overview[J]. International Journal of Computer Applications, 2016, 135(12): 21-29. [109] LU L J. Research and implementation of loss model of frequent flyers based on Spark[D]. Guangzhou: South China University of Technology, 2017. 卢理军. 基于Spark的航空常旅客流失模型研究与实现[D]. 广州: 华南理工大学, 2017. [110] WANG L J. Research on the segmentation of air pas-sengers based on customer value[D]. Beijing: Beijing University of Posts and Telecommunications, 2018. 王丽菊. 基于客户价值的航空旅客细分研究[D]. 北京: 北京邮电大学, 2018. [111] XIA N C. Air cargo management system based on big data technology[D]. Tianjin: Tianjin University, 2015. 夏念超. 基于大数据技术的航空货运管理系统[D]. 天津: 天津大学, 2015. [112] LI Q Q. Design and implementation of dalian airport freight management system[D]. Dalian: Dalian University of Technology, 2016. 李蔷嫱. 大连机场货运业务管理系统的设计与实现[D]. 大连: 大连理工大学, 2016. [113] CUI C. Application of Spark-based real-time efficient processing algorithm in internet user[D]. Beijing: Beijing University of Posts and Telecommunications, 2019. 崔辰. 基于Spark的实时高效处理算法在互联网用户行为分析平台中的应用[D]. 北京: 北京邮电大学, 2019. [114] HU Y Z, GAO Y. Research on building an airport big data platform[J]. China Transportation Review, 2015, 37(11): 85-89. 呼延智, 高原. 机场大数据平台构建研究[J]. 综合运输, 2015, 37(11): 85-89. [115] GUO Z J, JI Y, ZHOU J T, et al. The cutting-edge application of lightning early warning system based on big data in intelligent airport construction[J]. Civil Aviation Management, 2019, 14(9): 40-43. 郭知静, 吉岩, 周建涛, 等. 基于大数据的雷电预警系统在智慧机场建设中的前沿应用[J]. 民航管理, 2019, 14(9): 40-43. [116] LIU F. The key technology of ADS-B data organization and analysis based on Hadoop[D]. Tianjin: Civil Aviation University of China, 2018. 刘芳. 基于Hadoop 的ADS-B数据组织与分析关键技术[D]. 天津: 中国民航大学, 2018. [117] WU L F. Research on the application of big data in air traffic control safety management[J]. Science and Technology Innovation Herald, 2018(27): 188-189. 武利峰. 大数据在空管安全管理中的应用探究[J]. 科技创新导报, 2018(27): 188-189. [118] XU T, FENG X. Development of intelligence in civil aviation[J]. Science & Technology Review, 2019, 37(6): 60-65. 徐涛, 冯霞. 民航智能化的发展[J]. 科技导报, 2019, 37(6): 60-65. [119] SUN Y J, ZHANG Y, LI J. Study on fault and defect cause analysis model for aircraft design and manufacturing organization[J]. Aeronautical Manufacturing Technology, 2015, 58(15): 148-152. 孙奕捷, 张元, 李敬. 航空器设计/制造单位故障、缺陷原因分析模型研究[J]. 航空制造技术, 2015, 58(15): 148-152.