CN110442665A - Polar region sea area ice condition merges display methods - Google Patents
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Abstract
It is suitable for polar region sea area ice condition the invention discloses one kind and merges display methods, by the ice concentration and sea ice thickness data of the polar region sea area target of acquisition, each self-channel effective information, the comprehensive sea ice information image at high quality are extracted by data processing image procossing.Wherein, the size that ice concentration and sea ice depth are represented with the saturation degree of color keeps sea area ice condition information very clear.Model is established by causal influence network algorithm, ice concentration and sea ice thickness are predicted, the following sea area ice condition information is provided, accomplishes the popularity of time, while the sea chart being superimposed is the prerequisite of arctic navigation flight course planning.
Description
Technical field
The present invention relates to intelligent navigational fields, are suitable for polar region sea area ice condition more particularly to one kind and merge display methods.
Background technique
In recent years, the problems such as global climate increasingly warms, and south poles sea ice is caused to melt, sea-level rise.At the same time,
In summer, the condition in polar region sea area has been able to satisfy the normal pass of ship.With the development of ice-breaking technology, marine technology, I
State is reached the voyage of North America east bank by " northwest course line ", will substantially be shortened by the voyage that " northeast course line " reaches Europe, is saved
The transportation cost of ship is saved.Polar region mining deposits abundant, the scientific investigation team that attract various countries comes to explore, in recent years, more
Start to drive towards polar region sea area come more ships.
Currently, lacking since map open platform Baidu map, Google Maps, Mapbox are defaulted as Mercator projection
The projection of polar region, developer lack polar region sea area map data mining platform, and no APP is shown and divided to polar region sea area ice condition information
Analysis prediction, crewman can not be clearly understood that ice concentration and depth information around ship.It is suitable for and polar region due to lacking
The navigation in sea area, path planning system, ship are selected polar region ice navigation Intermediate Course largely according to navigator's
Experience is completed, and the safety of navigation cannot be ensured effectively.Research is suitable for the intelligent navigation of polar region ice navigation
The top priority of APP system is collected including ice concentration, the sea areas such as sea ice thickness ice condition information, by ice condition data and electronics
Sea chart additive fusion provides surrounding sea ice condition information for ship in real time.Destination path is planned on this basis, thus
Ensure the passage normally and efficiently of ship.Any support ice formation path planning, while can in view of what is limited on physics and operation
By tool, all it is popular in maritime world.So being suitable for the intelligent navigation APP system of polar region ice navigation to follow-up work
Development has great significance.In order to develop the intelligent navigation APP system for being suitable for polar region ice navigation, it is a kind of suitable to need
Polar region sea area ice condition merge display methods.
Summary of the invention
Since map open platform Baidu map, Google Maps, Mapbox are defaulted as Mercator projection, lack polar region
Projection, can not completely show polar region information.Projection is defined to complete polar region map picture using ArcGIS, by picture and sky
Between location information it is corresponding, projection setting is carried out with EPSG:3571, to figure layer point of addition point by geographic position name and longitude and latitude
Degree corresponds to, while correcting the precision of longitude and latitude, stops addition when longitude and latitude error is less than 0.0001 degree, completes polar region base map
Production, with polar stereographic projection polar plot address in server, polar stereographic projection map overlay is shown in platform.
Ice concentration, the sea ice thickness data file of FTP downloading are normalized, y=(x-
MinValue)/(MaxValue-MinValue), x, y are respectively to convert forward and backward value MaxValue, MinValue to be respectively
Data are converted into the decimal between (0,1) by the maximum value and minimum value of sample, are eliminated bad caused by unusual sample data
It influences, data is limited to the range being easily processed, output result is corresponding with rgb value, indicate that sea ice is close with color saturation
The size of intensity and sea ice thickness.Ergodic Matrices mark corresponding ice concentration and the sea ice depth rgb value of representing of longitude and latitude
In polar stereographic projection base map.
Sea area ice condition data are predicted using data mining statistical learning, arrange real-time ice condition and history ice condition data, it will
Longitude and latitude matrix corresponds to grid, establishes model using causal influence network algorithm:
Wherein, Y represents certain month to be predicted sea ice density or depth,It indicated before month to be predicted K months i-th
J-th of Climatic of observation point, Climatic be sea surface temperature, sea-level pressure, ocean surface wind speed,ForRecurrence
Coefficient, Z-kIndicate k months ice concentrations or thickness, q before month to be predicted-kFor Z-kRegression coefficient, O indicate the data moon
Number amount, M are grid data number, and N indicates the Climatic quantity, and ε is constant；Prediction data is compared with truthful data
It is right, prediction accuracy is stepped up, the popularity of sea ice information time is provided.
It provides navigation risk to vessel underway oceangoing ship using Bayesian network to remind, node is to cause ship to be sent out in arctic navigation
Make trouble thus factor, the dependence between node is obtained by expert investigation and historical empirical data, using Pearson came product
Square correlation coefficient process carries out correlation analysis to bivariate, and production node completes correlation among nodes analysis, constructs polar region boat
Row risk bayesian network structure figure provides navigation risk to vessel underway oceangoing ship and reminds.
Detailed description of the invention
Fig. 1 is a kind of intelligent navigation APP system using polar region sea area of the present invention ice condition fusion display methods.
Specific embodiment
A specific embodiment of the invention is further illustrated with reference to the accompanying drawing.
As shown in Figure 1, a kind of excellent with data mining and extractive technique, multiple target suitable for the fusion display of polar region sea area ice condition
Change the technologies such as algorithm, safety of ship analytical technology based on data-driven and combines the intelligent navigation APP for forming polar region ice navigation
System.
As shown in Figure 1, ice condition display module is defined projection to complete polar region map picture using ArcGIS, make picture
With spatial positional information, projection setting is carried out with EPSG:3571, by the method for point of addition point by geographic position name
It is corresponding with longitude and latitude, stop addition when inspection data longitude and latitude error is less than 0.0001 degree, completes the calibration of longitude and latitude precision.
With polar stereographic projection polar plot address in server, polar stereographic projection map overlay is shown in platform.By sea ice information data into
Row normalized, y=(x-MinValue)/(MaxValue-MinValue), x, y are respectively to convert forward and backward value
MaxValue, MinValue are respectively the maximum value and minimum value of sample, and data are converted into the decimal between (0,1), processing
Result afterwards is corresponding with rgb value, and the saturation degree of color indicates the size of ice concentration and sea ice thickness.Data are corresponding
Color mark is in polar stereographic projection base map.
As shown in Figure 1, ice prediction module uses data mining statistical learning technology, ice condition data are compiled, it will be through
Latitude matrix is corresponding with ice condition data, establishes model using causal influence network algorithm:
Wherein, Y represents certain month to be predicted sea ice density or depth,It indicated before month to be predicted K months i-th
J-th of Climatic of observation point, Climatic be sea surface temperature, sea-level pressure, ocean surface wind speed,ForRecurrence
Coefficient, Z-kIndicate k months ice concentrations or thickness, q before month to be predicted-kFor Z-kRegression coefficient, O indicate the data moon
Number amount, M are grid data number, and N indicates the Climatic quantity, and ε is constant；Prediction data is compared with truthful data
It is right, prediction accuracy is stepped up, the predictive information of ice condition is provided.
As shown in Figure 1, flight course planning module carries out path in conjunction with meteorological condition on the basis of sea ice information is merged and shown
Planning, the optimal path under difference preference is provided for user, thus, the optimal route of ship's navigation eliminates the reliance on the warp of navigator
It tests, significantly reduces ship in the risk of ice navigation.
As shown in Figure 1, risk reminding module uses safety of ship analytical technology, vessel underway oceangoing ship is mentioned using Bayesian network
It is reminded for navigation risk, node is the factor for causing ship that accident occurs in arctic navigation, is passed through by expert investigation and history
Data are tested to obtain the dependence between node, correlation point is carried out to bivariate using Pearson product-moment correlation coefficient method
Analysis, production node complete correlation among nodes analysis, arctic navigation risk bayesian network structure figure are constructed, to vessel underway oceangoing ship
Navigation risk is provided to remind.
Manager function module audits user data, check and update on time to sea ice data, it is ensured that system can be just
Often display sea ice situation, and correctly prediction relatively.Steamer line data are analyzed, are updated, it is ensured that flight course planning
Reasonability.
The succinct function of system interface is simple, only need to input destination coordinate or best road that place name can be navigated by water
Line, the data repository of foundation update sea area ice condition data and navigation channel information data on time, provide for the stable operation of system
It ensures.
It should be noted that specific embodiment of the present invention illustrates inventive principle and the scope of application.But it is unlimited
In a kind of embodiment, under the exploration of experimental spirits, according to other solutions that the invention occurs, the invention is belonged to
Protection scope.
Claims (3)
1. a kind of polar region sea area ice condition merge display methods, it is characterised in that the following steps are included: one, to polar region map picture into
Row definition projection, picture is corresponding with spatial positional information, projection setting is carried out with EPSG:3571, to figure layer point of addition
Point is corresponding with longitude and latitude by geographic position name, while correcting the precision of longitude and latitude, when longitude and latitude error is less than 0.0001 degree
Stop addition；Two, polar stereographic projection vector base map is made, base map is published to server；Three, it using map overlay method, calls
Polar stereographic projection polar plot address in server, polar stereographic projection map overlay is shown in platform, will be corresponding with longitude and latitude matrix
Hundred-mark system sea ice density and sea ice thickness data carry out following normalized: y=(x-MinValue)/(MaxValue-
MinValue), x, y are respectively to convert forward and backward value, and MaxValue, MinValue are respectively the maximum value and minimum value of sample,
Data are converted into the decimal between (0,1), convert rgb value for output result, it is close to represent sea ice using the saturation degree of color
The size of intensity and sea ice depth, loops through longitude, latitude, ice concentration, sea ice matrix of depths, is traversed every time by longitude and latitude
The corresponding ice concentration and sea ice depth rgb value label of representing is spent in polar stereographic projection base map.
2. polar region sea area according to claim 1 ice condition merges display methods, which is characterized in that sea area ice prediction uses
Longitude and latitude matrix is corresponded to grid, is made by data mining statistical learning by the arrangement to real-time ice condition data and history ice condition
Model is established with causal influence network algorithm:
Wherein, Y represents certain month to be predicted sea ice density or depth,Indicate i-th of observation in K months before month to be predicted
Point j-th of Climatic, Climatic be sea surface temperature, sea-level pressure, ocean surface wind speed,ForRegression coefficient,
Z-kIndicate k months ice concentrations or thickness, q before month to be predicted-kFor Z-kRegression coefficient, O indicate data moon number
Amount, M are grid data number, and N indicates the Climatic quantity, and ε is constant；Prediction data is compared with truthful data,
Prediction accuracy is stepped up, the popularity of sea ice information time is provided.
3. merging display methods according to polar region sea area ice condition described in right 1 or 2, which is characterized in that
To cause the factor of marine incident as node in arctic navigation, complete Bayesian network is further constructed, shellfish is utilized
This network of leaf provides navigation risk to vessel underway oceangoing ship and reminds, and the dependence between node passes through historical data or the side of expert investigation
Method obtains, while carrying out correlation analysis to bivariate using Pearson product-moment correlation coefficient method, completes node and correlation
After analysis, arctic navigation risk bayesian network structure figure is constructed.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111783321A (en) * | 2020-08-05 | 2020-10-16 | 中国水利水电科学研究院 | Simulation method for winter ice condition development process of river channel in data-lacking area |
| CN112445856A (en) * | 2020-12-01 | 2021-03-05 | 海南长光卫星信息技术有限公司 | Sea surface height influence correlation analysis method and device |
| CN110849311B (en) * | 2019-11-19 | 2021-03-26 | 中国科学院海洋研究所 | Estimation method for sea ice output area flux of polar region key channel |
| CN113342292A (en) * | 2021-05-19 | 2021-09-03 | 大连陆海科技股份有限公司 | Sea ice remote sensing and numerical data superposition display method based on electronic chart |
| CN116226311A (en) * | 2023-03-15 | 2023-06-06 | 广州海宁海务技术咨询有限公司 | Navigation chart data processing and synthesizing method based on pay-per-flight |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110849311B (en) * | 2019-11-19 | 2021-03-26 | 中国科学院海洋研究所 | Estimation method for sea ice output area flux of polar region key channel |
| CN111783321A (en) * | 2020-08-05 | 2020-10-16 | 中国水利水电科学研究院 | Simulation method for winter ice condition development process of river channel in data-lacking area |
| CN112445856A (en) * | 2020-12-01 | 2021-03-05 | 海南长光卫星信息技术有限公司 | Sea surface height influence correlation analysis method and device |
| CN113342292A (en) * | 2021-05-19 | 2021-09-03 | 大连陆海科技股份有限公司 | Sea ice remote sensing and numerical data superposition display method based on electronic chart |
| CN113342292B (en) * | 2021-05-19 | 2024-04-19 | 大连陆海科技股份有限公司 | Sea ice remote sensing and numerical data superposition display method based on electronic chart |
| CN116226311A (en) * | 2023-03-15 | 2023-06-06 | 广州海宁海务技术咨询有限公司 | Navigation chart data processing and synthesizing method based on pay-per-flight |
| CN116226311B (en) * | 2023-03-15 | 2023-11-10 | 广州海宁海务技术咨询有限公司 | Navigation chart data processing and synthesizing method based on pay-per-flight |
| CN118194066A (en) * | 2024-05-15 | 2024-06-14 | 交通运输部水运科学研究所 | Arctic navigation ship ice condition data processing method and system |
| CN118194066B (en) * | 2024-05-15 | 2024-08-23 | 交通运输部水运科学研究所 | Arctic navigation ship ice condition data processing method and system |
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