Risk early warning model of cruise ship construction material logistics collection and distribution based on RS-RBFNN

doi:10.16265/j.cnki.issn1003-3033.2023.06.1033

Abstract

Abstract:

An ensemble risk early warning model based on RS and RBFNN fusion was proposed. After constructing the risk early warning index system, the ANP was used to analyze the coupling relationship between indicators and determine the weight values. The efficacy coefficient method was used to determine the historical data alarm, which was used as the output of the neural network. The RS-RBFNN model was used to conduct a risk early warning research on the logistics of mechanical and electrical materials during the construction of a cruise ship, and the performance was compared with RBFNN, BPNN and RS-BPNN. The research shows that the RS-RBFNN model effectively simplifies the neural network structure, improves efficiency and accuracy, and overcomes the shortcomings of BPNN of training time long, poor stability and easy to fall into local minima.

Key words: rough set (RS), radial basis function neural network (RBFNN), cruise ship construction material, logistics collection and distribution, risk early warning, back propagation neural network (BPNN)

XIE Luqiang, XU Jing, WANG Haiyan. Risk early warning model of cruise ship construction material logistics collection and distribution based on RS-RBFNN[J]. China Safety Science Journal, 2023, 33(6): 114-121.

Figures/Tables 11

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Tab.1

Tab.2

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Tab.4

Tab.5

Tab.6

Performance comparison of early warning models

预警模型	网络结构	训练时间/s	$α$	$β$	$δ$	$R 2$
BPNN	18×11×1	3.716 1	0.184 2	0.210 6	0.268 1	0.979 7
RS-BPNN	6×10×1	2.913 6	0.108 2	0.124 3	0.093 4	0.993 9
RBFNN	18×12×1	2.380 3	0.138 2	0.157 3	0.194 0	0.985 1
RS-RBFNN	6×10×1	0.989 3	0.071 0	0.081 5	0.036 0	0.997 3

Tab.6

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Fig.4

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一级指标	权重	二级指标	指标依存关系	局部权重	综合权重
B₁	0.332 8	C₁	C₃,C₄,C₆,C₇, C₈,C₁₀,C₁₁,C₁₂	0.243 9	0.081 2
		C₂	C3,C₄,C₁₀, C₁₁,C₁₂,C₁₃	0.194 6	0.064 8
		C₃	C₄,C₁₀,C₁₁,C₁₂	0.181 1	0.060 3
		C₄	C₁,C₃,C₇, C₁₀,C₁₁,C₁₂	0.380 4	0.126 6
B₂	0.194 3	C₅	C₆,C₇,C₈,C₉	0.282 6	0.054 9
		C₆	C5,C₇,C₈, C₉	0.161 7	0.031 4
		C₇	C₃,C₅, C₆,C₈,C₉	0.190 8	0.037 1
		C₈	C₅, C₆,C₇,C₉	0.082 4	0.01 6
		C₉	C₁₀,C₁₁,C₁₂,C₁₃	0.282 5	0.054 9
B₃	0.127 7	C₁₀	C₁₁,C₁₂	0.191 3	0.024 4
		C₁₁	C₁₂	0.132 3	0.016 9
		C₁₂	C₁₃	0.136 5	0.017 4
		C₁₃	C₇,C₈	0.539 8	0.068 9

一级指标	权重	二级指标	指标依存关系	局部权重	综合权重
B₄	0.345 2	C₁₄	C₁,C₃,C₄,C₅, C₆,C₇,C₈,C₉, C₁₀,C₁₁,C₁₂	0.340 0	0.117 4
		C₁₅	C₉,C₁₄, C₁₆,C₁₈	0.215 9	0.074 5
		C₁₆	C₂, C₉,C₁₀,C₁₁, C₁₂,C₁₅,C₁₈	0.200 8	0.069 3
		C₁₇	C₁,C₂, C₃, C₄,C₅,C₈,C₉, C₁₆,C₁₈	0.173 5	0.059 9
		C₁₈	C₃,C₉,C₁₀, C₁₁,C₁₂,C₁₆	0.069 8	0.024 1

警度	区间	描述
无警	[90,100]	风险可忽略,物流集配整体状况良好
轻警	[85,90)	较小风险,存在个别指标不达标状况,但整体物流集配风险可控可接受
中警	[80,85)	中度风险,部分预警指标出现异常,有必要给予足够重视
重警	[0,80]	重度风险,大部分指标偏离预警线,物流集配整体趋于恶化,有必要立即采取措施管控

时间	综合风险指数	警度	时间	综合风险指数	警度
2020-07	83.1	中警	2021-03	82.4	中警
2020-08	84.2	中警	2021-04	81.7	中警
2020-09	86.1	轻警	2021-05	85.8	轻警
2020-10	84.0	中警	2021-06	86.3	轻警
2020-11	85.0	轻警	2021-07	90.2	无警
2020-12	81.8	中警	2021-08	87.4	轻警
2021-01	77.9	重警	2021-09	88.60	轻警
2021-02	78.8	重警	2021-10	87.4	轻警

U	时期T																		J
U	T₁	T₂	T₃	T₄	T₅	T₆	T₇	T₈	T₉	T₁₀	T₁₁	T₁₂	T₁₃	T₁₄	T₁₅	T₁₆	T₁₇	T₁₈	J
1	3	2	4	2	1	1	2	1	1	1	1	1	2	1	1	1	4	1	3
2	2	3	3	1	1	2	4	2	2	1	2	1	1	1	1	1	4	2	3
3	3	2	4	1	2	2	3	1	2	2	4	3	2	1	2	1	3	2	2
4	4	2	3	2	2	2	3	1	2	2	3	2	3	1	2	1	3	1	3
︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙	︙
16	3	4	3	1	3	2	2	2	1	3	2	2	1	4	4	4	4	4	2