Identification and coupling study of risk factors for aviation safety occurrences based on active learning

doi:10.16265/j.cnki.issn1003-3033.2026.08.1066

Abstract

Abstract:

To explore the coupling relationship between risk factors of aviation safety occurrences, AL strategies were introduced into risk factor identification based on 22 439 occurrence text records, and a risk factor identification model combining Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Gated Recurrent Unit (BiGRU) was constructed to achieve automatic identification of risk factors. On this basis, the N-K model was applied to perform a coupling analysis of aviation safety occurrence risk factors. The results show that with 18 000 training samples, the AL strategy reduces manual annotation costs by 70% (12 600 samples), while the model performance (F₁=0.907 5) only decreases by 1% compared with the fully data-trained BERT-BiGRU model. The results of the risk factor coupling analysis show that in combinations with high coupling risk values, human, aircraft, and the environment are the key risk factors that cause aviation safety occurrences. As the number of factors involved in the coupling increases, the risk coupling value generally shows an upward trend, with the coupling value for the six factors being the highest at 0.371. In civil aviation safety management, it is necessary to avoid the combined effects of multiple factors.

Key words: active learning(AL), aviation safety occurrence, risk factor identification, risk coupling, N-K model

CLC Number:

X949

Li Li, Xing Ruijie, Bao Suiyi. Identification and coupling study of risk factors for aviation safety occurrences based on active learning[J]. China Safety Science Journal, 2026, 36(8): 29-36.

Figures/Tables 10

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类别	关键参数	参数值
模型架构	嵌入维度	768
	GRU隐藏层维度	256×2
	随机失活	0.2
	优化器	AdamW
超参数	最大序列长度	512
	批大小	128
	学习率	3×10^-5
焦点损失函数	聚焦参数γ	6
焦点损失函数	类别平衡因子α	0.25, 0.44, 0.87, 0.41, 2.12, 1.92

风险因素	P	R	F₁	宏F₁值
人	0.99	0.98	0.98	0.916 7
机	0.96	0.91	0.94
设	0.80	0.92	0.86
环	0.96	0.94	0.95
技	0.98	0.89	0.93
规	0.85	0.82	0.84

风险因素	P	R	F₁	宏F₁值
人	0.99	0.97	0.98	0.907 5
机	0.95	0.91	0.93
设	0.77	0.90	0.83
环	0.91	0.97	0.94
技	0.96	0.96	0.96
规	0.78	0.82	0.80

事件代码	概率	事件代码	概率	事件代码	概率	事件代码	概率
000000	0.013 5	100100	0.233 5	000101	8.91×10^-5	100101	0.001 2
100000	0.402 0	100010	0.000 8	000011	4.46×10^-5	100011	0.000 2
010000	0.145 0	100001	0.004 0	111000	0.000 8	011100	8.91×10^-5
001000	0.000 9	011000	0.001 4	110100	0.006 6	011010	4.46×10^-5
000100	0.032 0	010100	0.010 1	110010	0.000 3	010011	8.91×10^-5
000010	0.000 3	010010	0.001 6	110001	0.000 7	111100	8.91×10^-5
000001	8.91×10^-5	010001	0.000 9	101100	0.010 3	111001	4.46×10^-5
110000	0.076 3	001100	0.000 5	101001	0.000 1	110101	0.000 1
101000	0.055 8	000110	4.46×10^-5	100110	0.000 1	100111	4.46×10^-5

类型	变化风险概率
单因素	P_0.....=0.206 8	P_1.....=0.793 2	P_.0....=0.755 6	P_.1....=0.244 4	P_..0...=0.929 9
	P_..1...=0.070 1	P_...0..=0.705 2	P_...1..=0.294 8	P_....0.=0.996 3	P_....1.=0.003 7
	P_.....0=0.992 3	P_.....1=0.007 8	—	—	—
双因素	P_00....=0.047 5	P_01....=0.159 3	P_10....=0.708 1	P_11....=0.085 1	P_0.0...=0.203 8
双因素	P_0.1...=0.002 9	P_1.0...=0.726 1	P_1.1...=0.067 2	P_0..0..=0.164	…
3因素	P_000...=0.046 1	P_001...=0.001 4	P_010...=0.157 8	P_011...=0.001 6	P_100...=0.641 9
3因素	P_101...=0.066 2	P_110...=0.084 1	P_111...=0.000 9	P_00.0..=0.014 9	…
4因素	P_0000..=0.014	P_0001..=0.032 1	P_0010..=0.000 9	P_0011..=0.000 5	P_0100..=0.147 7
4因素	P_0101..=0.010 1	P_0110..=0.001 5	P_0111..=8.91×10^-5	P_1000..=0.407	…
5因素	P_00000.=0.013 6	P_00001.=0.000 4	P_00010.=0.032	P_00011.=4.46×10^-5	P_00100.=0.000 9
5因素	P_00101.=0	P_00110.=0.000 5	P_00111.=0	P_01000.=0.146	…
6因素	P₀₀₀₀₀₀=0.013 5	P₀₀₀₀₀₁=8.91×10^-5	P₀₀₀₀₁₀=0.000 3	P₀₀₀₀₁₁=4.46×10^-5	P₀₀₀₁₀₀=0.032
6因素	P₀₀₀₁₀₁=8.91×10^-5	P₀₀₀₁₁₀=4.46×10^-5	P₀₀₀₁₁₁=0	P₀₀₁₀₀₀=0.000 9	…