空地數據實時傳輸下的飛機著陸風險預警方法

王巖韜; 趙昕頤

doi:10.13374/j.issn2095-9389.2022.11.29.002

空地數據實時傳輸下的飛機著陸風險預警方法

doi: 10.13374/j.issn2095-9389.2022.11.29.002

王巖韜^,,
趙昕頤

中國民航大學國家空管運行安全技術重點實驗室，天津 300300

基金項目: 國家重點研發課題（2022YFC3002502）；國家自然基金資助項目（U1933103）

詳細信息

通訊作者:
E-mail:CAUCwyt@126.com

中圖分類號: N945.24;U8;V328.3
計量
- 文章訪問數: 171
- HTML全文瀏覽量: 34
- PDF下載量: 27
- 被引次數: 0
出版歷程
- 收稿日期: 2022-11-29
- 網絡出版日期: 2023-02-07
- 刊出日期: 2023-10-25

Advanced warning method for aircraft landing risk under air–ground data real-time transmission conditions

WANG Yantao^,,
ZHAO Xinyi

National Key Laboratory of ATM Operation Safety Management, Civil Aviation University of China, Tianjin 300300, China

More Information

Corresponding author: E-mail: CAUCwyt@126.com

摘要

摘要: 面向未來5G和衛星網構成的空地高通量互聯場景，為實現飛機著陸風險提前預警。首先基于統計與模型，建立了一套以多源運行實時數據為主，融合歷史統計和專家知識的著陸預警體系；然后，針對現有研究計算結果滯后問題，先通過對ARJ21飛機著陸過程快速存取記錄器（QAR）數據的聚類分析，將飛行員著陸操作模式分為4類，進而構建基于決策場理論的飛行員著陸操作模式預測模型，計算并討論不同場景下、不同個性飛行員的著陸模式選擇；在上述基礎上，針對著陸過程的復雜性和不確定性，提出一種分層計算的置信規則庫推理方法，融合定性與定量信息實現著陸動態風險評估和預警。最后，通過對“2020.10.16攀枝花跑道外接地事件”和“2010.8.2伊春空難”著陸過程的風險推理驗證了預警方法的有效性，其中攀枝花事件提前預警時間可達13 s。
- 多源運行數據 /
- 著陸風險 /
- 風險預警 /
- 決策場理論 /
- 分層置信規則庫
Abstract: At present, the flight safety work of civil aviation in China mainly investigates the probable causes of accidents and analyzes flight data after air accidents, causing numerous problems such as passive safety management and delayed risk control. To realize the early warning of flight risk during flight, a dynamic method for the evaluation of landing risk and early warning under the condition of future air–ground data real-time transmission was proposed. The landing stage, which has the most complex operation program and the highest accident rate during a flight, was taken as the research object, and future air-to-ground high-throughput interconnection scenarios comprising 5G and satellite networks were considered to solve the problem of advanced intelligent warnings and aircraft alarms in abnormal flights. First, according to the accident causation theory, the human factor reliability model, the system model, and other theories or models, a landing warning index system based on multisource real-time operation data and the integration of historical statistics and expert knowledge was established. Then, a grounding parameter prediction model was established to solve the problem of lag in the acquisition of four grounding parameters, namely ground pitch angle, ground speed, ground vertical rate, and 50 ft-ground horizontal flight distance in actual flight. This model classified the pilot’s landing operation mode by clustering ARJ21 historical landing data and determined the attribute mean value of the four parameters for each type of operation mode. Furthermore, according to decision field theory, the model discussed the landing mode selection of pilots with different personalities in different scenarios and calculated the selection probability of the pilot’s landing operation mode, thereby obtaining the predicted values of the four above-mentioned indicators. According to the above, aiming at the complexity and uncertainty of the landing risk early warning system, a reasoning method of the multilayer confidence rule base was proposed. This method improved the traditional reasoning method of the single-layer confidence rule base and adopted the bottom-up hierarchical reasoning method considering the complexity characteristics of the landing process, effectively integrating different sources and forms of qualitative or quantitative data. Thus, the dynamic assessment and reasoning of the landing risk were realized. Finally, using the reasoning-based calculation of the landing process for the “2020.10.16 Panzhihua runway grounding event” and “2010.8.2 Yichun air disaster,” the results verified the effectiveness of the method. It was found that the early warning time of the Panzhihua event can reach 13 s.
- multisource operation data /
- landing risk /
- advanced risk warning /
- decision field theory /
- hierarchical belief rule base

HTML全文

圖 1 機組信息處理模型

Figure 1. Information processing model of the crew

下載: 全尺寸圖片幻燈片

圖 2 著陸風險指標體系

Figure 2. Risk index system for the landing

下載: 全尺寸圖片幻燈片

圖 3 飛行員著陸操作模式. (a) A類; (b) B類; (c) C類; (d) D類

Figure 3. Pilot landing operation modes: (a) Class A; (b) Class B; (c) Class C; (d) Class D

下載: 全尺寸圖片幻燈片

圖 4 飛行員著陸操作模式預測模型

Figure 4. Prediction model of the pilot landing operation modes

下載: 全尺寸圖片幻燈片

圖 5 著陸屬性正態擬合圖. (a)接地俯仰角;(b)接地速度;(c)接地垂直速率;(d)15 m-接地水平距離

Figure 5. Normal distribution fitting of landing attributes: (a) grounding pitch angle ;(b) grounding speed;(c) grounding vertical rate; (d) 15 m-ground horizontal distance

下載: 全尺寸圖片幻燈片

圖 6 飛行員著陸操作偏好分布圖. (a) T=10 s, S_ii=0.1; (b) T=10 s, S_ii=0.9; (c) T=30 s, S_ii=0.1; (d) T=30 s, S_ii=0.9

Figure 6. Preference distribution of the pilot landing operations: (a) T=10 s, S_ii=0.1; (b) T=10 s, S_ii=0.9; (c) T=30 s, S_ii=0.1; (d) T=30 s, S_ii=0.9

下載: 全尺寸圖片幻燈片

圖 7 基于分層BRB的著陸風險評估系統

Figure 7. Landing risk assessment system based on layered BRB

下載: 全尺寸圖片幻燈片

圖 8 B-8667風險變化情況

Figure 8. Risk changes in B-8667

下載: 全尺寸圖片幻燈片

表 1 屬性矩陣與屬性參考值

Table 1. Attribute matrix and attribute reference values

Alternative model	Grounding pitch angle/(°)	Grounding speed/（m·s^-1）	Grounding vertical rate/(m·s^?1)	15 mground horizontal distance /m
A	2.17	67.32	?0.31	598.27
B	1.52	66.26	?0.43	661.29
C	2.02	68.60	?0.43	757.64
D	1.73	67.26	?0.36	566.38
Mean value	1.89	67.45	?0.43	641.26
Standard deviation	0.94	4.06	0.28	123.44

下載: 導出CSV

表 2 歸一化后屬性矩陣

Table 2. Normalized attribute matrix

Alternative plan	Grounding pitch angle	Grounding speed	Grounding vertical rate	15 m-ground horizontal distance
A	0.51	1.00	0.00	0.84
B	0.00	0.00	0.95	1.00
C	1.00	0.22	0.12	0.00
D	0.69	0.94	1.00	0.51

下載: 導出CSV

表 3 不同參數設置下的著陸模式選擇概率

Table 3. Landing mode selection probability under different parameter settings

Parameter settings	P_A	P_B	P_C	P_D	Mean time/s
T=10 s，S_ii=0.1	0.235	0.237	0.152	0.376	10.00
T=10 s，S_ii=0.9	0.190	0.083	0.015	0.712	9.79
T=30 s，S_ii=0.1	0.251	0.250	0.129	0.370	30.00
T=30 s，S_ii=0.9	0.114	0.036	0.001	0.849	16.04

下載: 導出CSV

表 4 指標參考值

Table 4. Index reference values

Index	Reference values	Index	Reference values
X₁	L(800 m); M(1600 m); H(5000 m)	X₁₉	L(0); M(0.5); H(1)
X₂	L(60 m); M(300 m); H(600 m)	X₂₀	L(3.05 m?s^?1); M(4.07 m?s^?1); H(5.59 m?s^?1)
X₃	N(0); Y(1)	···	···
X₄	L(0 m?s^?1); M(3.6 m?s^?1); H(10 m?s^?1)	Y₁₀	L(1); M(5); H(10)
···	···	Y₁₁	N(0); Y(1)
X₁₇	L(V_ref?2.5 m?s^?1); M(V_ref , m?s^?1); H(V_ref+10 m?s^?1)	Y₁₂	L(1); M(5); H(10)
X₁₈	L(0); M(0.5); H(1)	Y₁₃	L(1); M(5); H(10)
Notes:V_ref means reference landing speed，m?s^?1.

下載: 導出CSV

表 5 著陸風險評估置信規則庫

Table 5. Landing risk assessment belief rule base

Rule number	Rule base number	Rule weight	Premise attribute	Evaluation result
1	1	1	(X₃ is Y)	Y₁ is {(L,1)}
2	1	1	(X₄ is H)	Y₁ is {(H,1)}
3	1	1	(X₃ is N∧X₄ is M∧Y₅ is M∧X₆ is M∧X₇ is M)	Y₁ is {(M,1)}
4	1	1	(X₃ is N∧X₄ is L∧Y₅ is L∧X₆ is L∧X₇ is M)	Y₁ is {(L,0.75), (M,0.25)}
5	1	1	(X₃ is N∧X₄ is M∧Y₅ is M∧X₆ is L∧X₇ is L)	Y₁ is {(L,0.5), (M,0.5)}
6	1	1	(X₃ is N∧X₄ is L∧Y₅ is L∧X₆ is L∧X₇ is L)	Y₁ is {(L,1)}
···		···	···	···
115	13	1	(X₃₅ is L)	Y₁₃ is {(L,0.2), (M,0.3), (H,0.5)}
116	13	1	(X₃₅ is H)	Y₁₃ is {(L,0.2), (M,0.3), (H,0.5)}
117	13	1	(X₃₂ is M∧X₃₃ is M∧Y₃₄ is M∧X₃₅ is M)	Y₁₃ is {(L,0.8), (M,0.2)}

下載: 導出CSV

表 6 B-8667著陸風險指標值

Table 6. Landing risk index values of B-8667

Time before grounding/s	X₁/m	X₂/m	X₃	···	X₂₀/(m?s^?1)	X₂₁/(°)	···	X₃₀	X₃₁
20	2500	1000	0		3.46	2.8		0.95	0.95
19	2465	1000	0		3.73	3		0.95	0.95
18	2430	1000	0		4.00	3.3		0.95	0.95
···		···		···			···
3	1310	1000	0		4.28	3.9		0.95	0.95
2	1275	1000	0		4.05	3.6		0.95	0.95
1	1200	1000	0		3.90	3.6		0.95	0.95

下載: 導出CSV

表 7 B-8667實時風險評估值

Table 7. Real-time risk assessment value of B-8667

Time before landing/s	20	19	18	···	3	2	1
Risk value	4.55	4.55	4.56	···	7.29	8.11	8.84

下載: 導出CSV

表 8 B-3130著陸風險指標值

Table 8. Landing risk index values of B-3130

Index	X₁	X₂	X₃	···	X₂₀	X₂₁	···	X₃₀	X₃₁
Value	2800	600	0	···	872	3	···	0.95	0.95

下載: 導出CSV

259luxu-164

參考文獻(25)

[1]	CAAC. The 14th five-year civil aviation development planning [J/OL]. The State Council The People’s Republic Of China (2021-12-14) [2022-11-29]. https://www.gov.cn/zhengce/zhengceku/2022-01/07/content_5667003.htm 中國民用航空局. “十四五”民用航空發展規劃 [J/OL]. 中華人民共和國中央人民政府(2021-12-14) [2022-11-29]. https://www.gov.cn/zhengce/zhengceku/2022-01/07/content_5667003.htm
[2]	CAAC. Notice of the civil aviation administration of China on issuing road map for smart civil aviation [J/OL]. The State Council The People’s Republic Of China (2022-01-21) [2022-11-29]. https://www.gov.cn/xinwen/2022-01/21/content_5669771.htm 中國民用航空局. 中國民用航空局關于印發智慧民航建設路線圖的通知 [J/OL]. 中華人民共和國中央人民政府 (2022-01-21) [2022-11-29]. https://www.gov.cn/xinwen/2022-01/21/content_5669771.htm
[3]	Reed S. ALAR approach and landing accident reduction [J/OL]. Flight Safety Digest (2019-05-09) [2022-11-29]. https://flightsafety.org/fsd_aug-nov00/
[4]	FAA. Flight risk assessment tools [J/OL]. FAA Safety Briefing (2022-02-04) [2022-11-29]. https://www.faa.gov/newsroom/safety-briefing/flight-risk-assessment-tools
[5]	Feng C, Zhao T D. Case knowledge-based system safety risk model. Acta Aeronaut Sin, 2010, 31(4): 724 馮暢, 趙廷弟. 基于案例知識的系統安全風險模型. 航空學報, 2010, 31(4):724
[6]	Wang H W, Zuo H F. Evaluation of airline safety. Syst Eng, 2006, 24(2): 46 doi: 10.3969/j.issn.1001-4098.2006.02.010 王華偉, 左洪福. 航空公司安全評估研究. 系統工程, 2006, 24(2):46 doi: 10.3969/j.issn.1001-4098.2006.02.010
[7]	Wang Y T, Li R, Lu F, et al. Risk assessment system of flight operation based on multiple factor analysis. J Tianjin Polytech Univ, 2014, 33(3): 84 王巖韜, 李蕊, 盧飛, 等. 基于多因素分析的航班運行風險評估體系. 天津工業大學學報, 2014, 33(3):84
[8]	Wang Y T, Chen G M, Liu Y, et al. Flight operations risk improvement prediction based on Adaptive Lasso and RF. J Transp Syst Eng Inf Technol, 2018, 18(4): 194 王巖韜, 陳冠銘, 劉毓, 等. 基于Adaptive Lasso與RF的航班運行風險預測改進研究. 交通運輸系統工程與信息, 2018, 18(4):194
[9]	Wang L, Zhang J Y, Dong C T, et al. A method of applying flight data to evaluate landing operation performance. Ergonomics, 2019, 62(2): 171 doi: 10.1080/00140139.2018.1502806
[10]	Barry D J. Estimating runway veer-off risk using a Bayesian network with flight data. Transp Res C, 2021, 128: 103180 doi: 10.1016/j.trc.2021.103180
[11]	Cai J, Cai K, Huang S J. Early warning method for heavy landing of civil aircraft based on real-time monitoring parameters. J Traffic Transp Eng, 2022, 22(2): 298 doi: 10.19818/j.cnki.1671-1637.2022.02.024 蔡景, 蔡坤燁, 黃世杰. 基于實時監測參數的民用飛機重著陸預警方法. 交通運輸工程學報, 2022, 22(2):298 doi: 10.19818/j.cnki.1671-1637.2022.02.024
[12]	Wang Y H, Pang Y T, Chen O, et al. Uncertainty quantification and reduction in aircraft trajectory prediction using Bayesian-Entropy information fusion. Reliab Eng Syst Saf, 2021, 212: 107650 doi: 10.1016/j.ress.2021.107650
[13]	Havlikova M, Jirgl M, Bradac Z. Human reliability in man-machine systems. Procedia Eng, 2015, 100: 1207 doi: 10.1016/j.proeng.2015.01.485
[14]	Degrauwe M G R, Nys O, Dijkstra E, et al. IDAC: An interactive design tool for analog CMOS circuits. IEEE J Solid State Circuits, 1987, 22(6): 1106 doi: 10.1109/JSSC.1987.1052861
[15]	Busemeyer J R, Townsend J T. Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychol Rev, 1993, 100(3): 432 doi: 10.1037/0033-295X.100.3.432
[16]	Gao F, Wang M Z. Process-oriented dynamic route choice model. J Transp Syst Eng and Inf Technol, 2009, 9(5): 96 doi: 10.3969/j.issn.1009-6744.2009.05.015 高峰, 王明哲. 面向決策過程的動態路徑選擇模型. 交通運輸系統工程與信息, 2009, 9(5):96 doi: 10.3969/j.issn.1009-6744.2009.05.015
[17]	Zheng L, Chi H, Shao X Y. Pattern recognition and risk analysis for flight operations. Chin J Manag Sci, 2017, 25(10): 109 鄭磊, 池宏, 邵雪焱. 基于QAR數據的飛行操作模式及其風險分析. 中國管理科學, 2017, 25(10):109
[18]	Wang L, Ren Y, Wu C X. Effects of flare operation on landing safety: A study based on ANOVA of real flight data. Saf Sci, 2018, 102: 14 doi: 10.1016/j.ssci.2017.09.027
[19]	Jiang J, Li X, Xing L N, et al. System risk analysis and evaluation approach based on fuzzy evidential reasoning. Syst Eng Theory Pract, 2013, 33(2): 529 姜江, 李璇, 邢立寧等. 基于模糊證據推理的系統風險分析與評價. 系統工程理論與實踐, 2013, 33(2):529
[20]	Samson S, Reneke J A, Wiecek M M. A review of different perspectives on uncertainty and risk and an alternative modeling paradigm. Reliab Eng Syst Saf, 2009, 94(2): 558 doi: 10.1016/j.ress.2008.06.004
[21]	Yazdi M, Kabir S. Fuzzy evidence theory and Bayesian networks for process systems risk analysis. Hum Ecol Risk Assess, 2020, 26(1): 57 doi: 10.1080/10807039.2018.1493679
[22]	Yang J B, Liu J, Wang J, et al. Belief rule-base inference methodology using the evidential reasoning Approach-RIMER. IEEE Trans Syst Man Cybern A, 2006, 36(2): 266 doi: 10.1109/TSMCA.2005.851270
[23]	Yang J B, Liu J, Xu D L, et al. Optimization models for training belief-rule-based systems. IEEE Trans Syst Man Cybern A, 2007, 37(4): 569 doi: 10.1109/TSMCA.2007.897606
[24]	Guo M. A belief-rule-based inference method for modeling systems under uncertainties. Syst Eng Theory Pract, 2016, 36(8): 1975 郭敏. 基于置信規則庫推理的不確定性建模研究. 系統工程理論與實踐, 2016, 36(8):1975
[25]	Zhou Z J, Yang J B, Hu C H. Expert System of Belief Rule Base and Modeling of Complex System. Beijing: Science Press, 2011 周志杰, 楊建波. 置信規則庫專家系統與復雜系統建模. 北京: 科學出版社, 2011