Navigation Status Control of Cargo Ships in the Three Gorges Dam-Gezhouba Dam Cascade Navigation Hubs
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摘要: 针对三峡-葛洲坝梯级通航枢纽两坝间水域在汛期流量剧烈变化条件下货船通航风险较大的问题，本研究提出基于贝叶斯网络的货船航行状态控制模型，用于识别不满足通航条件的货船并评估其许可通航概率。模型结构结合两坝间水域通航特征，设置了环境条件、流量突变及交通复杂度3个模块，并构建了船舶属性与航道流量变化耦合的影响因素体系。在流量突变模块中，针对汛期流量变化特征，利用流量峰值、流量变幅及跃迁等级指标对突变等级进行评估。交通复杂度模块结合该水域上下行分道通航特点，以5小时通航周期为分析窗口，设置船舶数量分级和船舶间距分级指标，用以表征航道交通状态。针对模型节点参数难以直接获取的问题，本研究结合《三峡—葛洲坝水利枢纽两坝间航道汛期流量标准》、问卷调查结果及IF-THEN规则确定节点条件概率表。在此基础上，通过贝叶斯网络推理计算货船许可通航概率，形成面向通航准入的航行状态控制模型。模型验证采用实船数据、违规负样本及过坝数据进行，同时通过敏感性分析识别关键影响因素。验证结果显示：5组实船数据的许可通航概率均大于80%，与实际航行记录一致；违规负样本的不可航概率为88%~95%，能够有效识别高风险工况；60组过坝数据的许可通航概率为82%~88%，平均值为85%，与实际运行情况吻合。敏感性分析结果表明，流量条件和船舶功率为影响货船航行状态控制的主要因素。Abstract: This study addresses the high navigation risk of cargo ships caused by the drastic flood-season flow changes between the Three Gorges and Gezhouba navigation hubs. A navigation state control model for cargo ships based on a Bayesian network is developed to identify ships that fail to meet navigation conditions and to evaluate their permissible navigation probability. The model structure is designed according to the waterway's navigation characteristics and includes three modules: environmental conditions, flow mutation, and traffic complexity. An influencing factor system couples ship attributes with channel flow variations. The flow mutation module evaluates mutation grades using flow peak, variation amplitude, and transition grade to represent flood-season flow changes.The traffic complexity module considers separate upstream and downstream navigation, using a five-hour analysis window, and graded indexes of ship quantity and spacing to characterize channel traffic state. Node conditional probability tables are determined using the Flood Season Navigation Flow Standard, survey results, and IF-THEN rules to address difficulty in directly obtaining model parameters. The Bayesian network calculates permissible navigation probabilities, forming a navigation state control framework for navigation access. Verification with five actual ship samples, multiple noncompliant samples, and sixty dam-passing records shows that all five actual ship samples have permissible navigation probabilities above 80%, consistent with observed operations. Noncompliant samples have non-navigation probabilities of 88%~95%, identifying high-risk conditions. Sixty dam-passing records show permissible navigation probabilities of 82%~88%, averaging 85%, consistent with operational records. Sensitivity analysis identifies flow conditions and ship power as the main factors influencing cargo ship navigation state control.
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Key words:
- Navigation safety /
- Safety management /
- Probability estimation /
- Bayesian network
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图 3 基于贝叶斯网络的货船航行状态控制模型初始情景
Figure 3. The initial scenario of the cargo ship navigation state control model based on Bayesian network
图 4 货船航行状态控制模型的敏感性分析
Figure 4. Sensitivity analysis of cargo ship navigation state control model
表 1 货船航行控制状态模型的影响因素及状态
Table 1. Influencing factors and states of the navigation control status model for cargo ships.
影响因素 状态 环境条件 良好、不良 最大载重量/t > 0~1200、 > 1200~1600、 > 1600~2000、 > 2000~2400、 > 2400~2800、 > 2800~3200、 > 3200~4000、 > 4000~4800、 > 4800~5600、 > 5600~6400、 > 6400 满载排水量/t > 0~1500、 > 1500~2000、 > 2000~2500、 > 2500~3000、 > 3000~3500、 > 3500~4000、 > 4000~5000、 > 5000~6000、 > 6000~7000、 > 7000~8000、 > 8000 流量/(m3/s) > 0~25000、 > 25000~27500、 > 27500~30000、 > 30000~32500、 > 32500~35000、 > 35000~37500、 > 37500~40000、 > 40000~42500、 > 42500~45000 船舶功率/kW > 0~270、 > 270~368、 > 368~440、 > 440~630、 > 630~1000、 > 1000 载重与功率比 > 0~1、 > 1~2、 > 2~3、 > 3~4、 > 4~5、 > 5~6、 > 6~7、 > 7~8、 > 8 船舶航行状态 上行、下行 交通复杂度 复杂、可控、简单 上行航道复杂度 复杂、可控、简单 下行航道复杂度 复杂、可控、简单 下行船舶数量/艘 > 0~10、 > 10~15、 > 15 下行船舶间距/(n mile) > 0~3、 > 3~6、 > 6 上行船舶数量/艘 > 0~10、 > 10~15、 > 15 上行船舶间距/(n mile) > 0~3、 > 3~6、 > 6 流量突变 无预警、Ⅳ级预警、Ⅲ级预警、Ⅱ级预警、Ⅰ级预警 流量跃迁等级 > 0~1、 > 1~2、 > 2~3、 > 3~4、 > 4~5、 > 5 流量峰值/(m3/s) > 0~25000、 > 25000~35000、 > 35000~45000、 > 45000 流量变幅/(m3/s) > 0~5000、 > 5000~10000、 > 10000~15000 
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表 2 环境条件模块根节点的先验概率
Table 2. The prior probability of the root node of the environmental condition module
节点 最大载重量/t 满载排水量/t 流量/(m3/s) 船舶功率/kW 状态 先验概率 状态 先验概率 状态 先验概率 状态 先验概率 1 > 0~1200 0.013 > 0~1500 0.007 > 0~25000 0.917 > 0~270 0.001 2 > 1200~1600 0.040 > 1500~2000 0.023 > 25000~27500 0.003 > 270~368 0.012 3 > 1600~2000 0.024 > 2000~2500 0.018 > 27500~30000 0.022 > 368~440 0.050 4 > 2000~2400 0.058 > 2500~3000 0.067 > 30000~32500 0.041 > 440~630 0.176 5 > 2400~2800 0.089 > 3000~3500 0.107 > 32500~35000 0.013 > 630~1000 0.246 6 > 2800~3200 0.114 > 3500~4000 0.112 > 35000~37500 0.001 > 1000 0.515 7 > 3200~4000 0.094 > 4000~5000 0.113 > 37500~40000 0.001 8 > 4000~4800 0.102 > 5000~6000 0.105 > 40000~42500 0.001 9 > 4800~5600 0.155 > 6000~7000 0.169 > 42500~45000 0.001 10 > 5600~6400 0.106 > 7000~8000 0.128 11 > 6400 0.205 > 8000 0.151
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表 3 交通复杂度模块根节点的先验概率
Table 3. The prior probability of the root node of the traffic complexity module
节点 1 2 3 4 5 6 流量跃迁等级 > 0~1 > 1~2 > 2~3 > 3~4 > 4~5 > 5 0.522 0.304 0.109 0.063 0.001 0.001 流量峰值/(m3/s) > 0~25 000 > 25 000~35 000 > 35 000~45 000 > 45 000 0.561 0.332 0.089 0.018 流量变幅/(m3/s) > 0~5 000 > 5 000~10 000 > 10 000~15 000 0.790 0.200 0.010 船舶航行状态 上行 下行 0.522 0.478 下行船舶数量/艘 > 0~10 > 10~15 > 15 0.18 0.804 0.016 下行船舶间距/(n mile) > 0~3 > 3~6 > 6 0.260 0.729 0.011 上行船舶数量/艘 > 0~10 > 10~15 > 15 0.162 0.723 0.115 上行船舶间距/(n mile) > 0~3 > 3~6 > 6 0.074 0.172 0.754
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表 4 载重与功率比部分条件概率表
Table 4. Partial conditional probability table of load-to-power ratio
父节点 子节点 最大载重量/t 船舶功率/kW 载重与功率比 > 0~1 > 1~2 > 2~3 > 3~4 > 4~5 > 5~6 > 6~7 > 7~8 > 8 > 2 000~2 400 > 0~270 0 0 0 0 0 0 0 0 1 > 2 000~2 400 > 270~368 0 0 0 0 0 0 0.677 0.218 0.105 > 2 000~2 400 > 368~440 0 0 0 0 0.099 0.813 0.088 0 0 > 2 000~2 400 > 440~630 0 0 0 0.401 0.375 0.224 0 0 0 > 2 000~2 400 > 630~1 000 0 0 0.403 0.597 0 0 0 0 0 > 2 000~2 400 > 1 000 0.036 0.345 0.619 0 0 0 0 0 0
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表 6 流量突变模块区间评分表
Table 6. Scoring values of the traffic peak interval
节点 状态 流量峰值/(m3/s) > 0~25 000 > 25 000~35 000 > 35 000~45 000 > 45 000 评分值 2 3 4 5 流量变幅/(m3/s) > 5 000~10 000 > 10 000~15 000 评分值 3 5 跃迁等级 > 0~1 > 1~2 > 2~3 > 3~4 > 4 评分值 1 2 3 4 5
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表 7 流量突变程度测量表
Table 7. Measurement scale of sudden change of flow rate
流量变幅/(m3/s) 跃迁等级 峰值区间/(m3/s) > 0~25 000 > 25 000~35 000 > 35 000~45 000 > 45 000 > 5 000~10 000 > 0~1 6 7 8 9 > 5 000~10 000 > 1~2 7 8 9 10 > 5 000~10 000 > 2~3 8 9 10 11 > 5 000~10 000 > 3~4 9 10 11 12 > 5 000~10 000 > 4 10 11 12 13 > 10 000~15 000 > 0~1 8 9 10 11 > 10 000~15 000 > 1~2 9 10 11 12 > 10 000~15 000 > 2~3 10 11 12 13 > 10 000~15 000 > 3~4 11 12 13 14 > 10 000~15 000 > 4 12 13 14 15
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表 8 流量突变模块部分条件概率表
Table 8. Partial conditional probability table of sudden change of flow
父节点 子节点 流量跃迁等级 流量变幅/(m3/s) 流量峰值/(m3/s) 流量突变 无预警 Ⅳ级预警 Ⅲ级预警 Ⅱ级预警 Ⅰ级预警 > 2~3 > 10 000~15 000 > 0~25 000 0 0 0.95 0.05 0 > 2~3 > 10 000~15 000 > 25 000~35 000 0 0 0 0.95 0.05 > 2~3 > 10 000~15 000 > 35 000~45 000 0 0 0 0.95 0.05 > 2~3 > 10 000~15 000 > 45 000 0 0 0 0 1
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表 9 环境条件节点条件概率表
Table 9. Partial conditional probability table of environmental conditions
父节点 子节点 满载排水量/(m3/s) 船舶功率/kW 载重与功率比 流量/(m3/s) 船舶航行状况 环境条件 良好 不良 > 0~1500 > 368~440 > 1~2 > 35000~37500 上行 0.733 0.267 > 0~1500 > 368~440 > 1~2 > 35000~37500 下行 0.950 0.050 > 0~1500 > 368~440 > 1~2 > 35000~37500 上行 0.242 0.758 > 0~1500 > 368~440 > 1~2 > 35000~37500 下行 0.728 0.272
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表 10 模块交通复杂度节点部分条件概率表
Table 10. Partial conditional probability table of module traffic complexity module
规则 父节点 子节点 航行状况 下行航道交通复杂度 上行航道交通复杂度 交通复杂度 复杂 可控 简单 1 上行 复杂 复杂 1 0 0 2 上行 复杂 可控 0.333 0.667 0 17 下行 简单 可控 0 0.333 0.667 18 下行 简单 简单 0 0 1
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表 11 货船航行状态节点部分概率表
Table 11. Probability table of navigable part of cargo ship sailing state
父节点 子节点 环境条件 交通复杂度 流量突变 货船航行状态 可航 不可航 良好 可控 无预警 0.90 0.10 良好 可控 Ⅳ级预警 0.88 0.12 良好 可控 Ⅲ级预警 0.86 0.14 良好 可控 Ⅱ级预警 0.85 0.15 良好 可控 Ⅰ级预警 0.82 0.18
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表 12 历史航行记录的部分根节点状态
Table 12. States of some root nodes of historical navigation records
船名 JUHANG88 TONGXIN8 GANCHANGJIANG1HAO JIANGHAIYOU5 JIANGJIYUN1238 >满载排水量/t 9630.67 3223.58 3374 5605.5 7429.7 >最大载重量/t 7600 2585 2641 4400 6271 >船舶功率/kW 1470 440 800 1202 1150 >航行状况 下行 下行 下行 上行 上行 >流量初始值/(m3/s) 5900 32000 26000 32500 10300 >流量变幅/(m3/s) 361 7300 2484 450 900 >流量峰值/(m3/s) 6261 32400 26100 32500 11200 >船舶数量/艘 12 15 8 17 16 船舶相对间距/(n mile) 4.69 6.03 9.72 4.09 5.83
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表 13 航行数据的验证结果
Table 13. Verification results of navigation data
船名 MMSI 通航概率/% JUHANG88 413 940 153 85 TONGXIN8 413 813 548 81 GANCHANGJIANG1HAO 413 801 349 88 JIANGHAIYOU5 413 992 341 84 JIANGJIYUN1238 413 940 534 84
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