Advanced warning method for aircraft landing risk under air–ground data real-time transmission conditions
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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。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.
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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 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 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 PA PB PC PD Mean time/s T=10 s,Sii=0.1 0.235 0.237 0.152 0.376 10.00 T=10 s,Sii=0.9 0.190 0.083 0.015 0.712 9.79 T=30 s,Sii=0.1 0.251 0.250 0.129 0.370 30.00 T=30 s,Sii=0.9 0.114 0.036 0.001 0.849 16.04 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(Vref?2.5 m?s?1); M(Vref , m?s?1); H(Vref+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:Vref means reference landing speed,m?s?1. 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)} 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 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 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 259luxu-164 -
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