CN114627680B - Navigation safety prediction method and system for ultra-large ship - Google Patents
Navigation safety prediction method and system for ultra-large ship Download PDFInfo
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- CN114627680B CN114627680B CN202210370799.8A CN202210370799A CN114627680B CN 114627680 B CN114627680 B CN 114627680B CN 202210370799 A CN202210370799 A CN 202210370799A CN 114627680 B CN114627680 B CN 114627680B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G3/00—Traffic control systems for marine craft
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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- G—PHYSICS
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
Abstract
The invention discloses a navigation safety prediction method and a navigation safety prediction system for an ultra-large ship, which relate to the field of navigation safety guarantee and mainly comprise the following steps: acquiring navigation data of a target water area; the target water area navigation data comprise target water area terrain data, wind level, visibility, ice condition and planned routes; predicting a tide level value, a cross flow velocity and a longitudinal flow velocity of a planned route of a target water area; predicting the navigation depth of a planned route of the target water area based on the terrain data of the target water area and the tide level value; comparing the navigation data of the target water area, the cross flow velocity, the longitudinal flow velocity and the navigation depth with corresponding risk indexes respectively to determine the risk level of the ultra-large ship during navigation of the target water area; and predicting the time period of the ultra-large ship entering the port according to the danger level. The method can accurately determine the time period for the ship to enter the port and guarantee the navigation safety of the ship.
Description
Technical Field
The invention relates to the field of navigation safety guarantee, in particular to a navigation safety prediction method and system for an ultra-large ship.
Background
With the rapid development of global economy integration, the amount of maritime transportation is increasing, and ships are becoming larger to reduce transportation costs. The demand for global development of low-carbon economy is undoubtedly a direct driving force for large-scale ships representing more advanced technology and more friendly environmental protection. Due to the rapid development momentum, the size and the particularity in the operation performance, the ultra-large ship is more and more paid attention by scholars, and meanwhile, the application prospect of the ultra-large ship is also widely concerned by the industry.
Under the current situation, due to the limitation of navigation conditions of offshore waters, an ultra-large ship cannot directly reach a harbor area, for example, the ultra-large ship entering the harbor can drive in after needing to switch to the harbor to reduce the load, so that the cargo carrying capacity is greatly reduced, the transportation cost is increased, and the development of the ship industry is adversely affected. The limitation of navigation environment is not favorable for the implementation of large-scale and integrated development strategy of ports. Therefore, the research work of safe port entry and exit and berthing of large-draft ultra-large ships represented by 40-ten-thousand-ton ships is carried out, the method has very important significance for promoting large-scale ships and improving port cargo operation quantity, and meanwhile, the method and the device provide solutions for solving similar navigation problems, and have scientific significance.
Because the momentum and inertia of the ultra-large ship are large, the wind-receiving area is large when the ship is in no load, and the operation difficulty and the navigation risk are higher compared with the common ship in the processes of sailing and berthing. The navigation conditions of coastal water areas are complex, the flow of ships is large, accidents such as collision, grounding and the like occur frequently, and once an accident occurs in an ultra-large ship, the navigation environment, the ecological environment and the economic development of local areas of port water areas are affected badly. Therefore, the method has important engineering value for comprehensively evaluating the risks of different ship types such as ultra-large bulk freighters in the whole process from entering ports to berthing, and especially forecasting the seaworthiness time of the ships.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the navigation safety of an ultra-large ship, which can accurately determine the time period for the ship to enter a port and ensure the navigation safety of the ship.
In order to achieve the purpose, the invention provides the following scheme:
in a first aspect, the invention provides a method for predicting navigation safety of an ultra-large ship, which comprises the following steps:
acquiring navigation data of a target water area; the target water area navigation data comprises target water area terrain data, wind level, visibility, ice condition and planned routes;
predicting the tide level value, the cross flow velocity and the longitudinal flow velocity of the planned route of the target water area;
predicting the navigation depth of a planned route of the target water area based on the terrain data of the target water area and the tide level value;
comparing the navigation data of the target water area, the transverse flow velocity, the longitudinal flow velocity and the navigation depth with corresponding risk degree indexes respectively to determine the risk level of the ultra-large ship during navigation of the target water area;
and predicting the time period of the ultra-large ship entering the port according to the danger level.
Optionally, the acquiring navigation data of the target water area specifically includes:
collecting historical navigation data of a target water area;
determining navigation conditions influencing the safe navigation of the ultra-large ship based on the historical navigation data of the target water area and a planned route water area model experiment;
and acquiring navigation data of a target water area based on the navigation conditions.
Optionally, the navigation condition includes: terrain conditions, hydrological conditions, planned routes, traffic flow characteristics, and weather characteristics;
the topographic conditions include: a target water area seafloor topography; the hydrological conditions include: flow direction, flow rate, tidal level, and tidal harmonic constant; the planned route comprises navigation information of a plurality of target points; the navigation information comprises longitude and latitude coordinates, steering points and steering angles; the target point is any one point in the planned route; the traffic flow characteristics comprise ship density, ship size, ship speed and ship type; the meteorological features include wind level, ice condition, and visibility.
Optionally, the predicting a tide level value, a cross flow velocity and a longitudinal flow velocity of the planned route of the target water area specifically includes:
carrying out grid division on a target water area;
setting water flow condition monitoring points on a planned route in the target water area after grid division;
determining a route line segment with the water depth smaller than a set threshold value on a planned route in the gridded target water area; a plurality of position points with the water depth smaller than a set threshold value are included in the route line segment;
setting water depth condition monitoring points at two ends of the route line segment;
establishing a water level and water flow forecasting model according to the water flow condition monitoring points and the water depth condition monitoring points;
and predicting the tide value, the cross flow velocity and the longitudinal flow velocity in the target water area in real time based on the water level and water flow forecasting model.
Optionally, the risk indicator comprises: the system comprises a navigation depth danger degree evaluation index, a visibility danger degree evaluation index, a wind danger degree evaluation index, a longitudinal flow speed danger degree evaluation index, a transverse flow speed danger degree evaluation index and an ice condition danger degree evaluation index.
Optionally, the risk levels include high risk, higher risk, general risk, and lower risk in a prioritized order.
In a second aspect, the present invention provides a system for predicting navigation safety of an ultra-large vessel, including:
the navigation data acquisition module is used for acquiring navigation data of a target water area; the target water area navigation data comprises target water area terrain data, wind level, visibility, ice condition and planned routes;
the flow field data prediction module is used for predicting the tide value, the cross flow velocity and the longitudinal flow velocity of the planned route of the target water area;
the navigation depth data prediction module is used for predicting the navigation depth of the planned route of the target water area based on the terrain data of the target water area and the tide level value;
the safety evaluation module is used for comparing the navigation data of the target water area, the transverse flow velocity, the longitudinal flow velocity and the navigation depth with corresponding risk degree indexes respectively and determining the risk level of the ultra-large ship during navigation of the target water area;
and the navigation suggestion module is used for predicting the time period of the ultra-large ship entering the port according to the danger level.
Optionally, the navigation data acquiring module includes:
the historical navigation data acquisition unit is used for collecting historical navigation data of a target water area;
the navigation condition determining unit is used for determining navigation conditions affecting the safe navigation of the ultra-large ship based on the historical navigation data of the target water area and a planned airline water area model experiment;
and the target water area navigation data acquisition unit is used for acquiring the target water area navigation data based on the navigation conditions.
Optionally, the flow field data prediction module includes:
the network division unit is used for carrying out grid division on the target water area;
the water flow condition monitoring point setting unit is used for setting water flow condition monitoring points on a planned route in the target water area after grid division;
a water depth condition monitoring point setting unit, which is used for determining a route line segment with the water depth smaller than a set threshold value on a planned route in the target water area after the grid division; the route line segment comprises a plurality of position points with the water depth smaller than a set threshold value; setting water depth condition monitoring points at two ends of the route line segment;
the water level and water flow forecasting model building unit is used for building a water level and water flow forecasting model according to the water flow condition monitoring points and the water depth condition monitoring points;
and the tide level flow rate prediction unit is used for predicting the tide level value, the cross flow rate and the longitudinal flow rate in the target water area in real time based on the water level and water flow prediction model.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, the tide value, the cross flow velocity, the longitudinal flow velocity and the ship navigation depth of the planned route of the target water area are predicted in real time according to the navigation data of the target water area, and on the basis of comprehensively considering navigation data such as the navigation depth, the cross flow velocity, the longitudinal flow velocity and the like, the risk degree is evaluated by adopting the risk degree index, so that the risk prediction of the whole process from entering a port to leaving the port can be accurately determined, particularly the navigation time period of the ship is predicted, and the navigation safety of the ship is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for predicting the navigation safety of an ultra-large ship in the embodiment of the invention;
FIG. 2 is a sectional view of a target water grid according to an embodiment of the present invention;
FIG. 3 is a tidal level process diagram according to an embodiment of the present invention;
FIG. 4 is a graph illustrating risk prediction according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating dynamic risk assessment according to an embodiment of the present invention;
fig. 6 is a structural diagram of a navigation safety prediction system of an ultra-large ship according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for predicting the navigation safety of an ultra-large ship, which can accurately determine the time period for the ship to enter a port and ensure the navigation safety of the ship.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Example one
As shown in fig. 1, a method for predicting navigation safety of an ultra-large ship provided by an embodiment of the present invention includes:
step 100: acquiring navigation data of a target water area; the target water area navigation data comprises target water area terrain data, wind level, visibility, ice condition and planned routes.
The step 100 is specifically:
step 101: collecting historical navigation data of a target water area;
step 102: and determining the navigation conditions influencing the safe navigation of the ultra-large ship based on the historical navigation data of the target water area and the model experiment of the planned route water area.
Specifically, a planned airline water area mathematical model experiment is carried out, and key factors influencing the safety of a navigable water area, namely the navigation conditions influencing the safety navigation of the ultra-large ship, are determined based on the planned airline water area mathematical model experiment.
The navigation conditions influencing the navigation safety of the ultra-large ship comprise:
topographic conditions: a target water area seafloor topography.
Hydrologic conditions: flow direction, flow rate, tide level, harmonic constant, etc.
Planning a route: the longitude and latitude coordinates, the steering point and the steering angle of the representative point.
Traffic flow characteristics: ship density, ship dimensions, ship speed, ship type, etc.
Meteorological features: wind, rain, ice conditions, visibility, etc.
Step 103: and acquiring navigation data of a target water area based on the navigation conditions.
Specifically, acquiring terrain data, traffic flow data, wind level, visibility, ice condition and planned routes of a target water area based on the navigation condition;
the method comprises the steps of collecting topographic data of a target water area, collecting data such as channel wind level, visibility and ice condition based on a public system network system, and collecting wind level, visibility and ice condition data in the future of 6 hours, 12 hours, 24 hours, 48 hours and 72 hours.
Step 200: and predicting the tide value, the cross flow velocity and the longitudinal flow velocity of the planned route of the target water area.
The step 200 specifically includes:
step 201: and carrying out grid division on the target water area.
The invention takes the middle water area of Bohai sea as an example, and carries out meshing on the water area of a route, as shown in figure 2, and carries out meshing on a research water area.
Step 202: and setting water flow condition monitoring points on a planned route in the target water area after meshing.
Furthermore, a water flow condition monitoring point is arranged in every 1 sea on the planned route.
Step 203: determining a route line segment with the water depth smaller than a set threshold value on a planned route in the gridded target water area; the interior of the route line segment comprises a plurality of position points with the water depth smaller than a set threshold value.
Step 204: and setting water depth condition monitoring points at two ends of the route line segment.
The set water depth threshold value is a lower value, and the purpose is to determine a route line segment with insufficient water depth, wherein the route line segment with insufficient water depth comprises a plurality of shallow water depth points.
And then setting water depth condition monitoring points at two ends of insufficient water depth on the planned route in the gridded target water area, as shown in fig. 4.
Step 205: establishing a water level and water flow forecasting model according to the water flow condition monitoring points and the water depth condition monitoring points:
wherein, A x 、A y 、B x 、B y 、D x 、D y 、τ bx And τ by Are respectively shown in formulas (2) to (10):
τ bx ＝Uτ * (8)
τ by ＝Vτ * (9)
wherein t is time, x is x-axis direction coordinate, y is y-axis direction coordinate, z is z-axis direction coordinate, τ 0 Is a weight coefficient which varies spatially, ζ is a sea level height from an average sea level, U is an x-direction vertical average flow velocity, V is a y-direction vertical average flow velocity, H is a water depth from the average sea level, S is a coefficient (1 here), H is a total water depth representing the sum of ζ and H, f is a Coriolis coefficient, p is a pressure value s Is the surface atmospheric pressure, rho is the density of water, rho 0 Is the reference density of water,
Is the vertical average density of water, g is the acceleration of gravity, eta is the action of Newton tide potential, gamma is the action of solid tide, tau sx Is the x-component, tau, of the surface wind and wave radiation stresses sy Is the y-component, tau, of surface wind and wave radiation stresses bx Is the x-component, tau, of the bottom shear stress by Is the y-component of the bottom shear stress, C f Base coefficient of friction, τ * Calculating parameters for the base friction, A x A representative variable being a combination term in the x direction, A y A representative variable, D, for the y-direction combination term y Is the y-component of the diffusion term, D x Is the x-component, D, of the diffusion term y Is the y-component of the diffusion term, E h Is the horizontal vortex viscosity coefficient, B x Is the x-component of the gradient term of the ramp pressure, B y The y-component of the ramp gradient term.
The open boundary water level is interpolated from the tidal harmonic constants of the boundary, with initial conditions of ζ = U = V =0.
Step 205: and forecasting and monitoring water flow data in the water area in real time based on the water level and water flow forecasting model, counting, forecasting the tide level and flow field changes in the water area in 6 hours, 12 hours, 24 hours, 48 hours and 72 hours in the future, and giving specific tide level values, transverse flow rates and longitudinal flow rates.
Step 300: and predicting the navigation depth of the planned route of the target water area based on the terrain data of the target water area and the tide level value.
As shown in FIG. 4, according to the draft of the ship and the water depth of the planned route, water depth condition monitoring points are arranged at two ends of the insufficient water depth of the route, for each tide process, the time from the valley time of the initial tide level station E is calculated, the cut-off time is the time when the ship sails to the last tide level station F, a certain calculated tide process is shown in FIG. 3, a line corresponding to the shallow area initial point is a tide process of the E point, and a line corresponding to the shallow area end point is a tide process of the F point. And (2) releasing the ship at a set navigation speed (such as 10 kt) at a proper time interval (such as 1 hr), obtaining the tide height of the ship reaching different positions in each tide process and each navigation process according to the result of the step 200, obtaining the minimum surplus depth value in each tide process and each navigation process by combining the collected terrain data of the target water area, wherein the minimum surplus depth value is the ratio of the minimum water depth to the draught value, and selecting the arrival time corresponding to the maximum value of the minimum surplus depth in one tide process and a plurality of navigation processes, namely the safest arrival time. Alternatively, the time when the maximum navigable draft for a harbor can be reached is also a condition where the rich depth value has been determined.
Wherein, the water depth is the sea depth when the terrain data of the target water area and the ship travel to the position of the F point.
Step 400: and comparing the navigation data of the target water area, the transverse flow velocity, the longitudinal flow velocity and the navigation depth with corresponding risk degree indexes respectively to determine the risk level of the ultra-large ship during navigation of the target water area.
Wherein, the index values of water depth, longitudinal flow velocity, transverse flow velocity and the like are determined according to relevant standard specifications such as 'harbor general design specifications' and relevant researches, and whether the index is in any safety range is judged by comparing the index with a threshold value.
The risk levels include high risk, higher risk, general risk, and lower risk in order of priority.
1) Navigation depth
The ratio of the water depth to the ship draft is used as an evaluation index of the navigation depth, and the evaluation standard of the risk degree of the navigation depth is shown in table 1:
TABLE 1 evaluation standard table for flight depth danger
2) Visibility
When the visible distance is less than 4km, certain influence is exerted on the navigation of the ship; when the visible distance is reduced to 1km, the number of ship accidents is increased sharply. The number of days of poor visibility within 2km of visibility is generally chosen as a criterion. The number of days less than 2km of visibility is taken as a standard, and the evaluation standard of the visibility risk is shown in table 2:
table 2 visibility risk evaluation criteria table
3) Wind power
In a navigation safety evaluation system, the influence of wind is also prominent. When a ship is sailing, the ship is easy to drift due to wind, and also suffers from wind-induced course deviation and reduced ship maneuvering capacity. The influence degree of different wind levels on the ship is different, and the probability of accidents is higher when the wind is higher. The wind risk evaluation criteria are shown in table 3.
TABLE 3 wind-class Risk assessment Standard Table
4) Longitudinal flow velocity
When the longitudinal flow velocity is large, the ship goes up and down with certain problems, the classification ranges of the longitudinal flow velocity are respectively less than 1, 1-2, 2-3, 3-4 and more than 4, and the classification ranges are 5 grades, so the risk evaluation criteria of the longitudinal flow velocity are shown in table 4:
TABLE 4 evaluation criteria for the risk of longitudinal flow
5) Transverse flow velocity
According to the general design of harbors, the classification ranges of crossflow are 5 grades less than 0.1, 0.1 to 0.25, 0.25 to 0.75, and 0.75 to 1.0, respectively, and thus the risk evaluation criteria for the transverse flow rate are shown in table 5:
TABLE 5 evaluation criteria for risk of cross-flow velocity
6) Ice condition
The evaluation criteria for the risk of ice conditions are shown in table 6:
TABLE 6 evaluation Standard Table for Ice Condition Risk
Step 500: and predicting the time period of the ultra-large ship entering the port according to the danger level.
And (3) orderly distributing the risk level evaluation result data collected in the step (300) to a master authority and a ship by adopting various informatization means such as App and the like, and giving a harbor entering time suggestion, as shown in figures 4-5.
When the forecast reaches the level higher than the higher danger within 48 hours, the ship and the main control office are alarmed, and avoidance measures are taken as early as possible.
When the forecast reaches the level above the general danger within 24 hours, the ship and the main control office are alarmed, and avoidance measures are taken as early as possible.
And when the ship approaches to reach the lower danger within 12 hours of forecast, early warning is carried out on the ship to prompt the possible dangerous situation.
And when the navigation condition is in a low danger level, no early warning is carried out.
As shown in fig. 6, an ultra-large vessel navigation safety prediction system provided in an embodiment of the present invention includes:
the navigation data acquisition module 601 is used for acquiring navigation data of a target water area; the target water area navigation data comprise target water area terrain data, wind level, visibility, ice condition and planned routes.
And the flow field data prediction module 602 is used for predicting the tide value, the cross flow velocity and the longitudinal flow velocity of the planned route of the target water area.
And a navigation depth data prediction module 603, configured to predict a navigation depth of the planned route of the target water area based on the terrain data of the target water area and the tide level value.
And the safety evaluation module 604 is configured to compare the target water area navigation data, the cross flow velocity, the longitudinal flow velocity, and the navigation depth with corresponding risk indicators, and determine a risk level of the ultra-large ship during the target water area navigation.
And the navigation suggestion module 605 is used for predicting the port entering time period of the ultra-large ship according to the danger level.
Further, the navigation data acquisition module comprises:
and the historical navigation data acquisition unit is used for collecting historical navigation data of the target water area.
And the navigation condition determining unit is used for determining the navigation conditions affecting the safe navigation of the ultra-large ship based on the historical navigation data of the target water area and the planned airline water area model experiment.
And the target water area navigation data acquisition unit is used for acquiring the target water area navigation data based on the navigation conditions.
Further, the flow field data prediction module comprises:
the network division unit is used for carrying out grid division on the target water area;
the water flow condition monitoring point setting unit is used for setting water flow condition monitoring points on a planned route in the target water area after grid division;
a water depth condition monitoring point setting unit, which is used for determining a route line segment with the water depth smaller than a set threshold value on a planned route in the target water area after the grid division; the route line segment comprises a plurality of position points with the water depth smaller than a set threshold value; setting water depth condition monitoring points at two ends of the route line segment;
the water level and water flow forecasting model building unit is used for building a water level and water flow forecasting model according to the water flow condition monitoring points and the water depth condition monitoring points;
and the tide level flow rate prediction unit is used for predicting the tide level value, the cross flow rate and the longitudinal flow rate in the target water area in real time based on the water level and water flow prediction model.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (7)
1. A method for predicting navigation safety of an ultra-large ship is characterized by comprising the following steps:
acquiring navigation data of a target water area; the target water area navigation data comprise target water area terrain data, wind level, visibility, ice condition and planned routes;
predicting a tide level value, a cross flow velocity and a longitudinal flow velocity of a planned route of a target water area, which specifically comprises the following steps:
carrying out grid division on a target water area;
setting water flow condition monitoring points on a planned route in the target water area after grid division;
determining a route line segment with the water depth smaller than a set threshold value on a planned route in the gridded target water area; the route line segment comprises a plurality of position points with the water depth smaller than a set threshold value;
setting water depth condition monitoring points at two ends of the route line segment;
establishing a water level and water flow forecasting model according to the water flow condition monitoring points and the water depth condition monitoring points;
predicting the tide level value, the cross flow velocity and the longitudinal flow velocity in the target water area in real time based on the water level and water flow forecasting model;
predicting the navigation depth of a planned route of the target water area based on the terrain data of the target water area and the tide level value;
comparing the navigation data of the target water area, the transverse flow velocity, the longitudinal flow velocity and the navigation depth with corresponding risk degree indexes respectively to determine the risk level of the ultra-large ship during navigation of the target water area;
and predicting the time period of the ultra-large ship entering the port according to the danger level.
2. The method for predicting the navigation safety of the ultra-large ship according to claim 1, wherein the acquiring of the navigation data of the target water area specifically comprises:
collecting historical navigation data of a target water area;
determining navigation conditions influencing the safe navigation of the ultra-large ship based on the historical navigation data of the target water area and a planned airline water area model experiment;
and acquiring navigation data of a target water area based on the navigation conditions.
3. The method according to claim 2, wherein the navigation condition includes: terrain conditions, hydrological conditions, planned routes, traffic flow characteristics, and weather characteristics;
the terrain conditions include: a target water area seafloor topography; the hydrologic conditions include: flow direction, flow rate, tidal level, and tidal harmonic constant; the planned route comprises navigation information of a plurality of target points; the navigation information comprises longitude and latitude coordinates, steering points and steering angles; the target point is any one point in the planned route; the traffic flow characteristics comprise ship density, ship size, ship speed and ship type; the meteorological features include wind level, ice condition, and visibility.
4. The method according to claim 1, wherein the risk indicator comprises: the system comprises a navigation depth danger degree evaluation index, a visibility danger degree evaluation index, a wind danger degree evaluation index, a longitudinal flow speed danger degree evaluation index, a transverse flow speed danger degree evaluation index and an ice condition danger degree evaluation index.
5. The very large vessel navigation safety prediction method according to claim 1, wherein the risk levels include high risk, higher risk, general risk and lower risk ranked in priority.
6. A navigation safety prediction system for an ultra-large ship is characterized by comprising:
the navigation data acquisition module is used for acquiring navigation data of a target water area; the target water area navigation data comprise target water area terrain data, wind level, visibility, ice condition and planned routes;
the flow field data prediction module is used for predicting the tide level value, the cross flow velocity and the longitudinal flow velocity of the planned route of the target water area, and specifically comprises the following steps:
the network division unit is used for carrying out grid division on the target water area;
the water flow condition monitoring point setting unit is used for setting water flow condition monitoring points on a planned route in the target water area after grid division;
a water depth condition monitoring point setting unit, which is used for determining a route line segment with the water depth smaller than a set threshold value on a planned route in the target water area after the grid division; a plurality of position points with the water depth smaller than a set threshold value are included in the route line segment; setting water depth condition monitoring points at two ends of the route line segment;
the water level and water flow forecasting model building unit is used for building a water level and water flow forecasting model according to the water flow condition monitoring points and the water depth condition monitoring points;
the tide level flow velocity prediction unit is used for predicting a tide level value, a cross flow velocity and a longitudinal flow velocity in a target water area in real time based on the water level and water flow prediction model;
the navigation depth data prediction module is used for predicting the navigation depth of the planned route of the target water area based on the terrain data of the target water area and the tide level value;
the safety evaluation module is used for comparing the navigation data of the target water area, the transverse flow velocity, the longitudinal flow velocity and the navigation depth with corresponding risk degree indexes respectively and determining the risk level of the ultra-large ship during navigation of the target water area;
and the navigation suggestion module is used for predicting the time period of the ultra-large ship entering the port according to the danger level.
7. The system according to claim 6, wherein the navigation data acquiring module comprises:
the historical navigation data acquisition unit is used for collecting historical navigation data of a target water area;
the navigation condition determining unit is used for determining navigation conditions affecting the safe navigation of the ultra-large ship based on the historical navigation data of the target water area and a planned airline water area model experiment;
and the target water area navigation data acquisition unit is used for acquiring the target water area navigation data based on the navigation conditions.
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