边远海域救援船舶与直升机联合搜救优化

林婉妮; 王诺; 高忠印; 吴迪

doi:10.19818/j.cnki.1671-1637.2021.02.016

边远海域救援船舶与直升机联合搜救优化

doi: 10.19818/j.cnki.1671-1637.2021.02.016

林婉妮^1,,
王诺²,
高忠印²,
吴迪^2, ,

1.
军事科学院系统工程研究院后勤科学与技术研究所，北京 100166
2.
大连海事大学交通运输工程学院，辽宁大连 116026

基金项目:

国家自然科学基金项目 42030409

辽宁省社会科学基金青年项目 L19CGJ001

详细信息

作者简介:
林婉妮(1991-)，女，辽宁海城人，军事科学院系统工程研究院工程师，工学博士，从事物流工程与管理研究

通讯作者:
吴迪(1989-)，男，黑龙江大庆人，大连海事大学讲师，工学博士

中图分类号: U676.8
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- 被引次数: 0
出版历程
- 收稿日期: 2020-09-20
- 刊出日期: 2021-04-01

Associated searching and rescuing optimization of salvage vessels and helicopters in remote sea area

LIN Wan-ni^1
,,
WANG Nuo²,
GAO Zhong-yin²,
WU Di^{2
, ,}

1.
Institute of Logistics Science and Technology, Institute of Systems Engineering, Academy of Military Sciences, Beijing 100166, China
2.
College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

Funds:

National Natural Science Foundation of China 42030409

Social Science Foundation Youth Project of Liaoning Province L19CGJ001

More Information

Author Bio:
LIN Wan-ni(1991-), female, engineer, PhD, wn_lin@126.com

Corresponding author: WU Di(1989-), male, assistant professor, PhD, wudidlmu@163.com

摘要

摘要: 以救援船舶行驶路线、释放救援直升机时刻与救援直升机搜索方案为优化内容，以搜救时间最短和发现概率最大为目标，建立了海空联合搜救双目标优化模型，并结合地理信息系统和智能算法设计了模型求解算法; 利用地理信息系统模拟了复杂海洋环境中风、浪因素影响下的救援船舶和遇险船舶运行状态，采用自适应混沌搜索替代随机搜索，改进了传统粒子群算法; 以从南海永兴岛出发前往边远海域执行搜救任务为算例，验证了搜救优化模型。研究结果表明：利用地理信息系统与智能算法结合的海空联合搜救方法得到的搜救行动总时间为4.4~16.9 h，发现概率可达45.12%~99.76%;与传统的粒子群算法相比，改进后的粒子群算法在发现概率分别为85.00%、90.00%与95.00%的情况下，搜救总时间分别减少1.5、1.3与1.1 h，减少幅度分别为18.07%、14.28%与10.57%，改进后的算法在计算速度、计算稳定性与结果优化方面均效果良好; 海空联合搜救方案优化与传统的多目标路径优化问题有所不同，需要建立特定的海空联合搜救模型，结合新的技术手段开展研究; 未来建议发展不同船型、机型参与的海空联合搜救优化方法，以适应不断提高边远海域搜救行动效率的发展要求。
- 航运管理 /
- 海上搜救 /
- 海空联合 /
- GIS /
- 多目标优化 /
- 粒子群算法
Abstract: A bi-objective optimization model of air-sea associated searching and rescuing (SAR) was built, which took the time when the helicopter took off from the salvage vessel and the search plan of helicopter as optimization content, and aimed to minimize the SAR time and maximize the probability of discovery. An improved algorithm was designed based on a the geographic information system (GIS) and intelligent algorithms. The GIS was used to calculate the statuses of salvage vessels and vessels in distress under the influence of wind and wave factors in view of the changeable marine environment. The self-adaptive chaos search was used instead of random search to improve the particle swarm optimization algorithm. An example of the salvage vessel carrying a helicopter from Yongxing Island in the South China Sea to a remote sea area was used to verify the optimization model. Research results show that the total SAR time required for the SAR plan using GIS and intelligence algorithms is 4.4-16.9 h and the discovery probability is 45.12%-99.76%. Compared with the traditional particle swarm algorithm, the total SAR time of the improved particle swarm algorithm reduces by 1.5, 1.3, and 1.1 h, with a decrease rate of 18.07%, 14.28%, and 10.57% when the probability of discovery is 85.00%, 90.00%, and 95.00%, respectively. The improved algorithm shows better effect on calculation speed, calculation stability, and optimization result. The optimization of air-sea associated SAR is different from the traditional multi-objective routing optimization problem, and a new model that combines the improved algorithm is needed. To improve the efficiency of SAR in remote sea areas, it is suggested to further develop the optimization method used for air-sea associated SAR for different types of salvage vessels and helicopters. 6 tabs, 10 figs, 32 refs.
- marine management /
- maritime searching and rescuing /
- air-sea association /
- GIS /
- multi-objective optimization /
- particle swarm algorithm

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图 1 救援船舶和遇险船舶的相对位置

Figure 1. Relative locations of salvage vessel and vessel in distress

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图 2 扩展方形搜索

Figure 2. Extended square searching

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图 3 扫海宽度与航线间隔相对关系

Figure 3. Relative relation of sweeping width and route spacing

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图 4 发现概率与覆盖比关系曲线

Figure 4. Relation curve of detection probability and coverage ratio

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图 5 海空联合搜救

Figure 5. Air-sea associated searching and rescuing

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图 6 PSO算法改进

Figure 6. Improvement of PSO algorithm

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图 7 计算流程

Figure 7. Calculation process

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图 8 非支配解集分布

Figure 8. Distributions of non-dominated solution sets

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图 9 有效浪高分布

Figure 9. Distribution of significant wave height

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图 10 算法改进前后的帕累托前沿

Figure 10. Pareto frontiers before and after algorithm improvement

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表 1 IGD值对比

Table 1. Comparison of IGD values

测试函数	传统粒子群算法			改进粒子群算法
	计算结果			计算结果			改进幅度
	平均值/10^-4	最小值/10^-4	标准差/10^-4	平均值/10^-4	最小值/10^-4	标准差/10^-4	平均值改进幅度/%	最小值改进幅度/%	标准差改进幅度/%
Z₁	11.00	8.90	1.40	9.20	7.80	0.75	16.36	12.36	46.43
Z₂	8.30	7.20	0.86	7.40	6.80	0.54	10.84	5.56	37.21
Z₃	36.00	33.00	2.40	33.00	30.00	1.50	8.33	9.09	37.50
Z₄	33.00	23.00	9.60	23.00	17.00	5.10	30.30	26.09	46.88

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表 2 SPM值对比

Table 2. Comparison of SPM values

测试函数	传统粒子群算法			改进粒子群算法
	计算结果			计算结果			改进幅度
	平均值/10^-4	最小值/10^-4	标准差/10^-4	平均值/10^-4	最小值/10^-4	标准差/10^-4	平均值改进幅度/%	最小值改进幅度/%	标准差改进幅度/%
Z₁	24.00	21.00	2.20	20.00	18.00	1.40	16.67	14.29	36.36
Z₂	21.00	17.00	3.10	18.00	13.00	1.80	14.29	23.53	41.94
Z₃	61.00	31.00	8.20	41.00	29.00	5.40	32.79	6.45	34.15
Z₄	43.00	50.00	24.00	29.00	14.00	17.00	32.56	72.00	29.17

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表 3 海上风、浪数据

Table 3. Data of wind and wave in sea

时间/h	实测风向/(°)	实测风速/(m·s^-1)	基于GIS预测的救援船舶位置浪高/m	基于GIS预测的漂移船舶位置浪高/m
0.0	33.1	22.4	4.1	4.2
0.5	41.2	21.2	5.1	4.5
1.0	61.4	21.3	4.5	3.9
1.5	34.1	22.3	3.7	4.3
2.0	40.6	21.5	4.9	5.1
2.5	40.0	21.8	5.0	4.8
3.0	47.2	21.5	4.0	3.7
3.5	45.5	21.1	4.2	4.7
4.0	46.6	21.9	4.8	4.1
4.5	53.6	22.5	3.6	3.6
5.0	58.8	21.9	5.2	4.1
5.5	53.7	21.1	4.8	4.6
6.0	57.6	22.6	4.5	5.0
6.5	40.0	21.7	3.8	3.9
7.0	33.8	22.1	4.6	4.8
7.5	49.4	22.6	4.8	3.6
8.0	52.6	21.9	4.9	4.2
8.5	46.3	21.6	4.8	3.8
9.0	41.0	22.6	4.9	5.0
9.5	61.1	21.8	5.3	4.4
10.0	38.9	21.6	3.8	5.0
10.5	39.5	21.9	5.1	4.2
11.0	38.9	21.9	5.3	5.0
11.5	35.7	21.4	4.6	3.8

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表 4 救援船舶和遇险船舶数据

Table 4. Data of salvage vessels and vessels in distress

时间/h	风、浪影响下的救援船舶航速/kn	遇险船舶航速/kn	遇险船舶航向/(°)	遇险船舶位置/n mile	救援船舶位置/n mile
0.0	19.967	4.342	33.1	(200.00, 0.00)	(0.00, 0.00)
0.5	19.969	4.647	41.2	(201.85, 1.49)	(9.99, 0.07)
1.0	19.968	4.522	61.4	(202.94, 2.62)	(19.99, 0.21)
1.5	19.966	4.446	34.1	(204.10, 4.54)	(29.99, 0.44)
2.0	19.969	3.414	40.6	(205.23, 5.71)	(39.99, 0.74)
2.5	19.969	5.023	40.0	(206.66, 7.03)	(49.98, 1.12)
3.0	19.967	4.940	47.2	(208.45, 8.38)	(59.97, 1.58)
3.5	19.967	4.672	45.5	(209.62, 9.61)	(69.96, 2.11)
4.0	19.969	4.369	46.6	(210.94, 11.48)	(79.94, 2.78)
4.5	19.966	4.355	53.6	(212.18, 12.52)	(89.91, 3.51)
5.0	19.970	4.465	58.8	(213.91, 14.26)	(99.87, 4.37)
5.5	19.969	4.436	53.7	(215.82, 15.78)	(109.83, 5.35)
6.0	19.968	3.897	57.6	(217.63, 17.40)	(119.76, 6.46)
6.5	19.966	3.352	40.0	(218.99, 18.62)	(129.68, 7.69)
7.0	19.968	4.722	33.8	(220.34, 20.52)	(139.59, 9.08)
7.5	19.969	3.528	49.4	(221.91, 22.17)	(149.47, 10.65)
8.0	19.969	3.541	52.6	(223.84, 23.76)	(159.31, 12.39)
8.5	19.969	3.773	46.3	(225.08, 25.49)	(169.12, 14.34)
9.0	19.969	3.870	41.0	(226.54, 27.39)	(178.87, 16.56)
9.5	19.970	4.371	61.1	(227.83, 28.94)	(188.57, 19.01)
10.0	19.966	5.112	38.9	(229.82, 30.80)	(198.18, 21.76)
10.5	19.969	4.663	39.5	(230.85, 31.99)	(207.73, 24.75)
11.0	19.970	5.028	38.9	(232.54, 33.09)	(217.20, 27.94)
11.5	19.968	3.630	35.7	(234.30, 34.80)	(226.48, 31.66)
12.0				(236.20, 36.78)	(235.33, 36.32)

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表 5 改进算法运算结果

Table 5. Improved algorithm's computational result

约束概率/%	起飞时刻/h	起飞时与遇险船舶之间的距离/n mile	飞机搜索航线间隔/n mile	搜寻时间/h	发现概率/%
85.00	3.0	145.40	1.60	6.8	85.50
90.00	4.0	128.50	1.30	7.8	90.30
95.00	5.5	102.60	0.96	9.3	95.50

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表 6 算法对比

Table 6. Comparison of algorithms

约束概率/%	传统粒子群算法		改进粒子群算法
约束概率/%	发现概率/%	搜寻时间/h	发现概率/%	搜寻时间/h	搜寻时间缩减幅度/%
85.00	85.10	8.3	85.50	6.8	18.07
90.00	90.20	9.1	90.30	7.8	14.28
95.00	95.10	10.4	95.50	9.3	10.57

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