CN106126805A - A kind of offshore spilled oil Forecasting Methodology - Google Patents
A kind of offshore spilled oil Forecasting Methodology Download PDFInfo
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Abstract
The open a kind of offshore spilled oil Forecasting Methodology of the present invention, comprises the steps: (10) oil spilling Image Acquisition: gray processing Real-Time Ocean image, and the Real-Time Ocean image after gray processing is carried out Threshold segmentation and binary conversion treatment, obtains initial oil spilling image；(20) offshore spilled oil detection: according to initial oil spilling image, and according to the resolution of Real-Time Ocean image and longitude and latitude, obtain the oil spilling detection image including oil spilling zone aspect with area；(30) offshore spilled oil prediction: based on oil spilling detection image, according to offshore spilled oil behavior prediction aggregative model, is calculated the oil spilling prognostic chart picture including predicting oil spilling zone aspect with area.The offshore spilled oil Forecasting Methodology of the present invention, error is little, and accuracy is high.
Description
Technical Field
The invention belongs to the technical field of sea surface oil stain diffusion drift prediction, and particularly relates to a sea surface oil spill prediction method with small error and high accuracy.
Background
After an oil spill accident occurs on the sea surface, it is important to judge the motion track and the accommodation of the oil spill in advance for effectively cleaning oil stains and preventing pollution. However, as environmental conditions vary, their motion trajectories and homing difficulties increase over time.
In order to predict the movement track and the accommodation of oil spilling after the oil spilling is changed along with the change of time and environmental conditions, and provide technical support and decision basis for resource scheduling and configuration of marine pollution cleaning control, domestic and foreign scholars make a great deal of research on numerical simulation of oil spilling behaviors and establish related oil spilling prediction models. They can be roughly classified into oil film spreading mode, convective diffusion mode and "oil particle" mode 3.
The classic three-stage expansion theory of Fay in the expansion mode, the Blokker expansion mode and the Liuzhou hole formula are widely regarded.
The convection diffusion mode comprises a drift process and a discrete process, and drift models comprise an American Navy model established by Webb et al, an SEADOCK model established by Williams et al, a Delaware model and the like; the discrete model mostly adopts a Monte Carlo method.
Since Johansen, Elhot and the like put forward the oil particle concept, the oil particle model is developed greatly, the advection process is simulated by a common deterministic method, the convection diffusion process is simulated by a random method, and the research and application of the oil particle model in oil film expansion are less.
However, as the mutual influence and the relation research among the processes are less, the oil spill prediction has larger error.
Disclosure of Invention
The invention aims to provide a sea surface oil spill prediction method which is small in error and high in accuracy.
The technical solution for realizing the purpose of the invention is as follows: a sea surface oil spill prediction method comprises the following steps:
(10) acquiring an oil spilling image: graying the real-time marine image, and performing threshold segmentation and binarization processing on the grayed real-time marine image to obtain an initial oil spill image;
(20) sea surface oil spill detection: obtaining an oil spill detection image comprising the azimuth and the area of an oil spill area according to the initial oil spill image and the resolution and the longitude and latitude of the real-time marine image;
(30) predicting sea surface oil spill: and calculating to obtain an oil spill prediction image comprising the position and the area of the predicted oil spill region according to the sea surface oil spill behavior prediction comprehensive model on the basis of the oil spill detection image.
Compared with the prior art, the invention has the following remarkable advantages:
small error and high accuracy: most of traditional oil spill prediction models are independent calculation model researches on expansion, drift and weathering change modules, and all motion modules are not combined systematically to realize prediction calculation of the whole behavior after oil spill occurs. And the mutual influence and connection among the modules are less considered, and the prediction result may generate larger errors. In the calculation model summarized by the invention, all the change modules are combined into a whole, and the mutual influence and connection among all the modules are considered, so that the accuracy of a prediction result is improved;
the invention is described in further detail below with reference to the figures and the detailed description.
Drawings
Fig. 1 is a main flow chart of the sea surface oil spill prediction method of the present invention.
FIG. 2 is a flow chart of the sea surface oil spill prediction step of FIG. 1.
FIG. 3 is a flow chart of the oil spill weathering prediction step of FIG. 2.
FIG. 4 shows the density, API and molar volume of different oils.
FIG. 5 is a schematic diagram of the comparison of laboratory sink experimental results and simulation results, including the initial area, end area, length of movement, and movement time.
Fig. 6(a) is an infrared image of initial oil spill in the water tank experiment, and fig. 6(b) is an infrared image of oil spill in the water tank experiment after 10 seconds.
Fig. 7(a) is a MATLAB simulated oil spill detection image, and fig. 7(b) is a MATLAB simulated oil spill prediction image.
Fig. 8(a) is an oil spilling region image, and fig. 8(b) is an oil spilling region image subjected to graying, threshold segmentation, and binarization processing.
Fig. 9 is a bohai map used as an oil spill background marine image.
Fig. 10 is an initial oil spill detection image.
FIG. 11 is a predicted oil spill image
Fig. 12(a) is an initial oil spill detection image with the bohai sea as a background, and fig. 12(b) is an oil spill prediction image with the bohai sea as a background.
FIG. 13 is a schematic diagram of an interactive predictive simulation platform based on MATLAB GUI, including parameter settings, test result display, predictive result display, and display mode buttons.
Fig. 14 shows predicted trajectory images of crude oil at different times in the southwest wind, wind speed 5 m/s, the direction of ocean current to the east, and ocean current speed 4 m/s, where (a) to (d) show predicted images at 1 hour, 3 hours, 5 hours, and 10 hours after oil spill.
Fig. 15 shows predicted images of the crude oil 1 hour after the overflow in the environmental conditions of southwest wind, wind speed of 10 m/s, ocean current velocity of 5 m/s, and different ocean current directions, where (a) to (d) represent predicted images of the ocean current directions eastward, southward, westward, and northward, respectively.
Fig. 16 shows predicted images of oils of different varieties 1 hour after spillover under the environmental conditions of southwest wind, wind speed of 10 m/s, ocean current speed of 5 m/s, and ocean current direction toward the east, and (a) to (d) show predicted images of crude oil, motor gasoline, light diesel oil, and asphalt, respectively.
FIG. 17(a) shows a predicted image under an environmental condition of southwest wind, a wind speed of 3 m/sec, an ocean current velocity of 4 m/sec, and an ocean current direction oriented to the east 1 hour after the overflow of crude oil,
FIG. 17(b) shows a predicted image under an environmental condition of a west wind, a wind speed of 5 m/sec, an ocean current velocity of 4 m/sec, and an ocean current direction oriented to the east 3 hours after the overflow of crude oil,
FIG. 17(c) shows a predicted image under an environmental condition of northwest wind, wind speed of 8 m/sec, ocean current velocity of 5 m/sec, and ocean current direction to the east 5 hours after the overflow of crude oil,
fig. 17(d) shows a predicted image under an environmental condition of a north wind, a wind speed of 10 m/sec, an ocean current velocity of 5 m/sec, and an ocean current direction oriented to the east 10 hours after the overflow of the crude oil.
Detailed Description
As shown in fig. 1, the method for predicting sea surface oil spill of the present invention includes the following steps:
(10) acquiring an oil spilling image: graying the real-time marine image, and performing threshold segmentation and binarization processing on the grayed real-time marine image to obtain an initial oil spill image;
the image of the oil spill area is read, and as shown in fig. 8(a), the oil spill area is converted into a gray image, and the gray image is subjected to threshold segmentation processing by the maximum inter-class variance method. The maximum inter-class variance method (OTSU) is a method of adaptively determining a threshold value proposed by Nobuyuki OTSU in 1979. The image is divided into a background part and an object part according to different gray values of the image. The larger the inter-class variance between the background and the target, the larger the difference between the two parts constituting the image, and the smaller the difference between the two parts is caused if the target is mistaken for the background or the background is mistaken for the target. Thus, the probability of segmentation errors is minimized when the inter-class variance is maximized. For image I (x, y), the calculation principle is as follows:
n1+n2＝m×n (3)
p1+p2＝1 (4)
a＝p1a1+p2a2(5)
S＝p1(a1-a)2+p2(a2-a)2(6)
substituting formula (5) for formula (6) yields the equivalent formula:
S＝p1p2(a2-a1)2(7)
and (5) obtaining a threshold value when the inter-class variance is maximum by using a traversal method, and completing the threshold value segmentation of the image. It is composed ofTh in represents a segmentation threshold for the target and background; p is a radical of1Representing the number of pixels of the object in proportion to the total image, a1Is its average gray level; p is a radical of2Representing the ratio of the number of background pixels to the total image, a2Is its average gray scale, a represents the average gray scale of the total image, S represents the inter-class variance, and the image size is denoted as m × n, n1Representing the number of pixels in the image with a grey value smaller than Th, n2Indicating the number of pixels whose gray scale value is greater than Th. And (3) performing binarization processing on the image after threshold segmentation, and setting the gray value of the pixel point with the gray value smaller than the threshold Th as 0 and the gray value of the pixel point with the gray value larger than the threshold Th as 255, as shown in fig. 8(b), so that the detection and calculation of the oil spilling area and the azimuth in the later period are facilitated.
(20) Sea surface oil spill detection: obtaining an oil spill detection image comprising the azimuth and the area of an oil spill area according to the initial oil spill image and the resolution and the longitude and latitude of the real-time marine image;
using Google Earth software to query the map of bohai sea in china, and capturing a picture as a background of the research according to a certain scale, as shown in fig. 9. According to the resolution of fig. 9, a pure black background picture (the scale and the position information are the same as those of fig. 9) with the same resolution is made, and then the oil stain image is reduced and placed in the pure black background, as shown in fig. 8, and is detected and predicted by taking the initial oil spill image of fig. 8 as the image. The image 8 is scanned and the area and orientation of the oil stain is calculated from the scale, position information and resolution in the figure.
(30) Predicting sea surface oil spill: and calculating to obtain an oil spill prediction image comprising the position and the area of the predicted oil spill region according to the sea surface oil spill behavior prediction comprehensive model on the basis of the oil spill detection image.
And calculating according to the sea surface oil spill behavior prediction comprehensive model on the basis of the initial oil spill detection image to obtain an oil spill prediction image, and calculating the area and the direction of the predicted oil spill area. The oil spill is predicted by the prediction model to obtain a predicted image and the related area and orientation, as shown in fig. 11. The oil stain image and the background image are superimposed to form a more intuitive oil stain detection image, as shown in fig. 12.
As shown in fig. 2, the sea surface oil spill predicting step (30) includes:
(31) and (3) oil spill expansion prediction: the expansion of the spilled oil is mainly influenced by the composition and properties of the oil film, so that the expansion is influenced by the property change caused by weathering after the spilled oil overflows. Furthermore, the effect of surface wind speed on the expansion is also not negligible. The expansion process may be based on the three-segment expansion improvement theory of Fay.
The change of the long axis l and the short axis r of the oil film at each stage along with the time is calculated according to the following formula:
a gravity expansion stage:
r1＝K1(ΔgV)1/4t1/2， (8)
l1＝r1+cuf ηt ， (9)
a viscosity expansion stage:
l2＝r2+cuf ηt ， (11)
surface tension expansion stage:
l3＝r3+cuf ηt ， (13)
in the formula:
Δ＝1-ρ0/ρw， (14)
ρ0、ρwrespectively representing the density of oil and water; g represents the gravity acceleration, and V represents the oil spill volume; v iswThe ability of the liquid to resist deformation is characterized by the motion viscosity coefficient of water, is the main reason of energy loss generated by the liquid in the flow, and can be represented by a formula
νw＝0.01775/(1+0.0337T+0.000221T2)， (15)
Obtaining T as water temperature, v at 10 deg.Cw＝1.307×10-6Taking the net surface tension coefficient as 0.0308; t represents the oil spill time, K1、K2、K3Denotes the expansion coefficient of each stage, K1＝2.28，K2＝2.90，K3＝3.20；ufRepresenting wind speed, c represents an empirical constant, and c is 0.03, η is 4/3, 3/4;
(32) predicting oil spill drift: the drift diffusion mainly comprises an advection process and a turbulent diffusion process. The advection process is a result of surface currents and wind forces, with the most significant influencing factors being the speed of the ocean currents and their direction.
The drift velocity is calculated by:
wherein,
in the formula,is the vector of the migration velocity of the oil film,is the surface ocean velocity, αcThe surface ocean current drift coefficient is 1.0; a iswIs the wind drift coefficient, aw＝3.5%；Is the wind drift velocity vector, given by,
in the formula u10The wind speed is 10m above the sea surface, α is a wind direction angle, α' is a Korotkoff deflection angle, and the angle is 15 degrees;
when the turbulent diffusion is anisotropic, the turbulent diffusion velocity generated by the oil particles in the horizontal direction is:
wherein R represents a random number of normal distribution with a mean value of 0 and a standard deviation of 1, and Kx、KyRespectively represents diffusion coefficients in x and y directions, and the value is 10-50 m2/S；
(33) Predicting the weathering of the spilled oil: integrating evaporation, emulsification and density of the spilled oil, and predicting the weathering of the spilled oil;
the weathering process of the oil film includes evaporation, oxidation, emulsification, dissolution, biodegradation and sedimentation. Wherein evaporation and emulsification have a large, non-negligible impact on the properties of the oil spill.
As shown in fig. 3, the (33) oil spill weathering prediction step includes:
(331) and (3) calculating an evaporation coefficient: evaporation is the most important part of the oil spill weathering process, and is affected by factors such as the density, API, and molar volume of the oil spill. The evaporation coefficient was calculated by selecting the analytical method proposed by Mackay et al according to the actual situation.
The evaporation coefficient was calculated analytically as follows,
KE＝KMAVM/GTV0， (22)
KM＝0.0025u10 0.78， (23)
in the formula, FVIs the evaporation coefficient, t is the time, A is the area of the oil film, VMIs the molar volume and takes 150 × 10-6～600×10-6m3G is a gas constant of 8.206 × 10-5And T is the surface temperature of the oil, approximately equal to the atmospheric temperature TE，V0Is the initial volume of oil spill, P0Is the initial volatile gas pressure, the relationship is as follows:
in the formula, T0Is the initial boiling point, TEWhen 283K, B, T0The value of (d) can be calculated by the following formula:
B＝1158.9API-1.1435， (24)
T0＝542.6-30.275API+1.565API2-0.03439API3+0.0002604API4，(25)
wherein API represents the specific gravity adopted by the American Petroleum institute;
specific values for density, API and molar volume for the different oils are shown in figure 4.
(332) And (3) water content calculation: emulsification is affected by surface wind velocity, Mackay et al propose to mark the degree of emulsification with water content,
the water content indicating the degree of emulsification was calculated as follows:
in the formula, YWThe water content is expressed as the water content (%) in the emulsion,
KA＝4.5×10-6， (27)
representing the final water content, 0.8 ufSurface wind speed is adopted, and t is oil spilling time;
(333) calculating the oil overflow density: the oil spill density changes constantly due to the weathering process, mainly taking into account the effects of emulsification and evaporation. The influence of the two is synthesized.
The oil spill density is as follows,
ρ＝(1-YW)[(0.6ρ0-0.34)FV+ρ0]+YW·ρw， (29)
where ρ represents the density of the evaporated oilDegree, rho0Denotes the initial density, ρ, of the oilwIndicating the density of the seawater.
(334) Calculating the volume change: coefficient of evaporation FVIs the ratio of the evaporation to the total amount of oil.
The effect of the evaporation coefficient on the volume is calculated by the following formula,
V＝(1-FV)t·V0， (30)
(34) and (3) displacement calculation: setting a certain point of oil film at tiThe coordinate of time is S (t)i)，ti+1The coordinate of time is S (t)i+1) Then there is
For the synthesis of the displacement of the spread, drift vector over a period of Δ t, i.e.
To extend the displacement vector over a period of at, i.e.
For shifting the displacement vector within the time interval at, i.e.
The density and volume of the oil spill are changed due to evaporation and emulsification, so that a certain influence is caused on the long axis and the short axis of the oil film expansion, and in order to calculate accurately, the extended long axis and the extended short axis are calculated by substituting the formulas (29) and (30) into the extension formulas (8) to (13), and then substituted into the formula (33) to calculate the extended displacement vector.
To facilitate visual observation and simplify operations, an interactive interface is designed using MATLAB GUI, as shown in FIG. 13, and the top of the interface is a parameter setting column, which is divided into an extension item, a drift item and a time parameter according to the influencing factors. The expansion items comprise oil spill types which can be selected through a pull-down menu, the existing types comprise crude oil, motor gasoline, light diesel oil and asphalt, the density, API and molar volume of different oil types are different, and the specific numerical values are shown in figure 4. The drifting item comprises wind speed, wind direction, ocean current speed and ocean current direction. The wind speed and ocean current velocity may be entered directly into the values in the edit box. The wind direction and the ocean current direction may be selected through pull-down menus, respectively. Wherein the wind direction includes south wind, southwest wind, west wind, northwest wind, north wind, northeast wind, east wind and southeast wind. The ocean current directions include eight directions of north, northeast, eastern, southeast, southwest, westward and northwest. The middle part of the interface is an image display area, the left side is an initial image of oil spilling, the right side is a predicted image of oil spilling, and the lower parts of the two images are the initial area and the direction of oil spilling and the predicted area and the direction of oil spilling respectively. The lower right hand corner is the dynamic display and static display buttons. When the dynamic display is selected, only the oil spilling variety needs to be set, and other conditions change along with the change of time, so that the dynamic simulation of the oil spilling track can be realized. And if static display is selected, oil spilling varieties, wind speed, wind direction, ocean current speed, ocean current direction and time need to be set, so that oil spilling prediction under specific conditions is realized.
The invention is further described below with reference to simulation examples.
In order to verify the accuracy of the prediction model, a water tank experiment is adopted to verify the diffusion drift of the spilled oil. The specific values of density, API and molar volume for the experiments with crude oil as the spill sample are shown in figure 4. The density of the water is 1000kg/m3The water temperature is 25 ℃, then the formula is shown
vw＝0.01775/((1+0.0337TW+0.000221TW^2))
V is availablewIs 0.00896cm2And s. The width of the water tank is 30cm, the length of the water tank is 4m, a section of the water tank with uniform flow velocity is 59cm, the average flow velocity of water is about 6cm/s, and the wind speed is 0. The movement track of the oil spill on the water surface is collected by using an infrared camera, the collected initial oil spill image and the oil spill image after the oil spill occurs for 10s are used as actual measurement results, and the actual measurement results are compared and analyzed with simulation results under the same condition parameters, as shown in fig. 5, the comparison between the infrared actual measurement results and corresponding model calculation results, and the oil spill expansion area and the drifting distance are close to each other. Fig. 6(a) is an actually measured infrared image of initial oil spill, fig. 6(b) is an infrared oil spill image after 10s, fig. 7(a) is an oil spill detection image, and fig. 7(b) is an oil spill prediction image obtained by model calculation using the initial oil spill image of fig. 7 (a). According to the graph, the actual measurement result is consistent with the model calculation result, and the accuracy of the oil spill prediction model is verified.
In order to prove that the prediction method is simple, intuitive and quick, a plurality of groups of prediction simulation experiments are completed on a PC with a 2.1GHz CPU and a 4G memory by using a prediction simulation platform made by MATLAB R2014a software. FIG. 13 is a schematic diagram of an interactive predictive simulation platform based on MATLAB GUI with parameter setting bars at the top of the interface, divided into extension and drift terms and time parameters according to the influence factors. The expansion items comprise oil spilling types which can be selected through a pull-down menu, and the existing types comprise crude oil, motor gasoline, light diesel oil and asphalt. The drifting item comprises wind speed, wind direction, ocean current speed and ocean current direction. The wind speed and ocean current velocity may be entered directly into the values in the edit box. The wind direction and the ocean current direction may be selected through pull-down menus, respectively. The middle part of the interface is an image display area, the left side is an initial image of oil spilling, the right side is a predicted image of oil spilling, and the lower parts of the two images are the initial area and the direction of oil spilling and the predicted area and the direction of oil spilling respectively. The lower right hand corner is the dynamic display and static display buttons. When the dynamic display is selected, only the oil spilling variety needs to be set, and other conditions change along with the change of time, so that the dynamic simulation of the oil spilling track can be realized. And if static display is selected, oil spilling varieties, wind speed, wind direction, ocean current speed, ocean current direction and time need to be set, so that oil spilling prediction under specific conditions is realized.
FIG. 14(a) is a predicted trajectory image of crude oil after 1 hour of southwest wind, wind speed 5 m/s, ocean current direction eastward, ocean current velocity 4 m/s, and oil spill; FIG. 14(b) is a predicted trace image of crude oil after 3 hours of southwest wind, wind speed 5 m/s, ocean current direction eastward, ocean current velocity 4 m/s, and oil spill; FIG. 14(c) is a predicted trajectory image of crude oil after southwest wind, wind speed 5 m/s, ocean current direction eastward, ocean current velocity 4 m/s, and oil spill 5 hours; fig. 14(d) is a predicted trajectory image of crude oil after 10 hours of oil spill in southwest wind, a wind speed of 5 m/sec, an ocean current direction to the east, and an ocean current velocity of 4 m/sec. As can be seen from the figure, after the oil spill occurs, the shape, size, and position of the oil film change with time. In a short time, the oil film is rapidly expanded, and the area is rapidly increased; and in the later period, due to factors such as thickness reduction, evaporation and the like, the area growth is slowed down until the oil film thickness is reduced to a certain value, and the expansion is stopped.
FIG. 15(a) is a predicted image of crude oil 1 hour after overflow in the southwest wind, wind speed 10 m/s, ocean current speed 5 m/s, and ocean current direction to the east; FIG. 15(b) is a predicted image of crude oil 1 hour after overflow in the southwest wind, wind speed 10 m/s, ocean current speed 5 m/s, and ocean current direction southwest environmental conditions; FIG. 15(c) is a predicted image of crude oil 1 hour after overflow in the environmental conditions of southwest wind, wind speed 10 m/s, ocean current speed 5 m/s, and ocean current direction toward the west; fig. 15(d) is a predicted image of crude oil 1 hour after overflowing under an environmental condition of southwest wind, a wind speed of 10 m/sec, an ocean current speed of 5 m/sec, and an ocean current direction toward the north. As can be seen from the figure, the drift trajectory of the oil spill changes obviously along with the change of the direction of the ocean current, and the ocean current plays an important role in predicting the drift of the oil spill.
Fig. 16(a) is a predicted image of crude oil 1 hour after overflowing in an environmental condition of southwest wind, a wind speed of 10 m/sec, an ocean current speed of 5 m/sec, and an ocean current direction toward the east; fig. 16(b) is a predicted image of the gasoline for car 1 hour after overflowing in the environment conditions of southwest wind, wind speed of 10 m/s, ocean current speed of 5 m/s, and ocean current direction toward the east; fig. 16(c) is a predicted image of light diesel oil 1 hour after overflowing in an environmental condition of southwest wind, a wind speed of 10 m/s, an ocean current speed of 5 m/s, and an ocean current direction toward the east; fig. 16(d) is a predicted image of asphalt 1 hour after overflowing in an environmental condition of southwest wind, a wind speed of 10 m/sec, an ocean current speed of 5 m/sec, and an ocean current direction toward the east. It can be seen from the figure that the oil film expansion is greatly influenced by the type of oil spill, and the smaller the oil spill density is, the faster the expansion speed is, whereas the larger the density is, the slower the expansion speed is.
Fig. 17(a) shows a predicted image under an environmental condition where the wind velocity is 3 m/s, the ocean current velocity is 4 m/s, and the ocean current direction is eastward after 1 hour from the overflow of the crude oil, fig. 17(b) shows a predicted image under an environmental condition where the wind velocity is 5 m/s, the ocean current velocity is 4 m/s, and the ocean current direction is eastward after 3 hours from the overflow of the crude oil, fig. 17(c) shows a predicted image under an environmental condition where the wind velocity is 8 m/s, the ocean current velocity is 5 m/s, and the ocean current direction is eastward after 5 hours from the overflow of the crude oil, and fig. 17(d) shows a predicted image under an environmental condition where the wind velocity is 10 m/s, the ocean current velocity is 5 m/s, and the ocean current direction is eastward after 10 hours from the overflow of the crude oil. From fig. 17, the wind speed has a certain influence on the oil film expansion, and the larger the wind speed is, the faster the expansion is; the oil film expansion process mainly occurs in the early stage, and the later expansion effect is weakened to no expansion; the drift is mainly influenced by ocean current motion, and the influence of sea surface wind speed and wind direction is small.
Fig. 14 to 16 are analog simulation images in a static display mode, after the parameters are set, static display is clicked, and simulation images and area and longitude and latitude information of oil spill prediction can be obtained after about 10 seconds, so that the operation is simple, the result display is visual, and the simulation is rapid.
Claims (3)
1. A sea surface oil spill prediction method is characterized by comprising the following steps:
(10) acquiring an oil spilling image: graying the real-time marine image, and performing threshold segmentation and binarization processing on the grayed real-time marine image to obtain an initial oil spill image;
(20) sea surface oil spill detection: obtaining an oil spill detection image comprising the azimuth and the area of an oil spill area according to the initial oil spill image and the resolution and the longitude and latitude of the real-time marine image;
(30) predicting sea surface oil spill: and calculating to obtain an oil spill prediction image comprising the position and the area of the predicted oil spill region according to the sea surface oil spill behavior prediction comprehensive model on the basis of the oil spill detection image.
2. The method of predicting sea surface oil spill of claim 1, wherein said (30) sea surface oil spill predicting step comprises:
(31) and (3) oil spill expansion prediction: the change of the long axis l and the short axis r of the oil film at each stage along with the time is calculated according to the following formula:
a gravity expansion stage:
r1＝K1(ΔgV)1/4t1/2， (1)
l1＝r1+cuf ηt ， (2)
a viscosity expansion stage:
l2＝r2+cuf ηt ， (4)
surface tension expansion stage:
l3＝r3+cuf ηt ， (6)
in the formula:
Δ＝1-ρ0/ρw，
ρ0、ρwrespectively representing the density of oil and water; g represents the gravity acceleration, and V represents the oil spill volume; v. ofwThe ability of the liquid to resist deformation is characterized by the motion viscosity coefficient of water, is the main reason of energy loss generated by the liquid in the flow, and can be represented by a formula
vw＝0.01775/(1+0.0337T+0.000221T2)，
When T represents the water temperature and T is 10 ℃,vw＝1.307×10-6taking the net surface tension coefficient as 0.0308; t represents the oil spill time, K1、K2、K3Denotes the expansion coefficient of each stage, K1＝2.28，K2＝2.90，K3＝3.20；ufRepresenting wind speed, c represents an empirical constant, and c is 0.03, η is 4/3, 3/4;
(32) predicting oil spill drift: the drift velocity is calculated by:
wherein
In the formula,is the vector of the migration velocity of the oil film,is the surface ocean velocity, αcIs surface ocean current drift coefficient, 1.0, αwIs the wind drift coefficient, αw＝3.5％；Is the wind drift velocity vector, given by,
in the formula u10The wind speed is 10m above the sea surface, α is a wind direction angle, α' is a Korotkoff deflection angle, and the angle is 15 degrees;
when the turbulent diffusion is anisotropic, the turbulent diffusion velocity generated by the oil particles in the horizontal direction is:
wherein R represents a random number of normal distribution with a mean value of 0 and a standard deviation of 1, and Kx、KyRespectively represents diffusion coefficients in x and y directions, and the value is 10-50 m2/S；
(33) Predicting the weathering of the spilled oil: and (4) integrating evaporation, emulsification and density of the spilled oil and predicting the weathering of the spilled oil.
(34) Predicting oil spill displacement: setting a certain point of oil film at tiThe coordinate of time is S (t)i)，ti+1The coordinate of time is S (t)i+1) Then there is
For the synthesis of the displacement of the spread, drift vector over a period of Δ t, i.e.
To extend the displacement vector over a period of at, i.e.
For shifting the displacement vector within the time interval at, i.e.
3. The method of predicting sea surface oil spill of claim 2, wherein the (33) oil spill weathering predicting step includes:
(331) and (3) calculating an evaporation coefficient: the evaporation coefficient was calculated analytically as follows,
KE＝KMAVM/GTV0， (13)
KM＝0.0025u10 0.78， (14)
in the formula, FVIs the evaporation coefficient, t is the time, A is the area of the oil film, VMIs the molar volume and takes 150 × 10-6～600×10-6m3G is a gas constant of 8.206 × 10-5And T is the surface temperature of the oil, approximately equal to the atmospheric temperature TE，V0Is the initial volume of oil spill, P0Is the initial volatile gas pressure, the relationship is as follows:
in the formula, T0Is the initial boiling point, TEWhen 283K, B, T0The value of (d) can be calculated by the following formula:
B＝1158.9API-1.1435， (16)
T0＝542.6-30.275API+1.565API2-0.03439API3+0.0002604API4， (17)
wherein API represents the specific gravity adopted by the American Petroleum institute;
(332) and (3) water content calculation: the water content indicating the degree of emulsification was calculated as follows:
in the formula, YWThe water content is expressed as the water content (%) in the emulsion,
KA＝4.5×10-6，
representing the final water content, 0.8 ufSurface wind speed is adopted, and t is oil spilling time;
(333) calculating the oil overflow density: the oil spill density is as follows,
ρ＝(1-YW)[(0.6ρ0-0.34)FV+ρ0]+YW·ρw， (19)
where ρ represents the density of the evaporated oil, ρ0Denotes the initial density, ρ, of the oilwIndicating the density of the seawater.
(334) Calculating the volume change: the effect of the evaporation coefficient on the volume is calculated by the following formula,
V＝(1-FV)t·V0(20)。
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