CN111291665A - Ocean oil spill emulsification prediction method, equipment and storage medium - Google Patents
Ocean oil spill emulsification prediction method, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, equipment and a storage medium for predicting ocean oil spill emulsification, wherein the method comprises the following steps: the method comprises the steps of obtaining a remote sensing image set of a marine test field which is provided with spilled oil samples in advance and comprises a plurality of spilled oil sample putting points, obtaining a hyperspectral sensitive wave band and emulsification degree corresponding relation table according to the remote sensing image set, obtaining a spilled oil emulsification change initial model according to the hyperspectral sensitive wave band and emulsification degree corresponding relation table, the remote sensing image set, influence factor change data and spilled oil emulsification occurrence time, correcting the initial spilled oil emulsification change model, obtaining a spilled oil emulsification change model, applying the spilled oil emulsification change model, predicting the spilled oil emulsification degree change and the spilled oil distribution change of any spilled oil sea area, fully considering the emulsification change process which continuously occurs in spilled oil, and therefore accurately and effectively predicting the development process of spilled oil emulsification.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to a marine oil spill emulsification prediction method, marine oil spill emulsification prediction equipment and a storage medium.
Background
The oil spill in the ocean is variable, and under the influence of factors such as wind field, flow field, temperature and the like, the oil spill not only can be diffused to continuously pollute the ocean, but also can be accompanied by complex physical change and chemical change, and once emulsion which is difficult to eliminate is formed, great harm can be caused to the ecological environment. Emulsification means that oil and water are mixed together, oil particles are continuously dispersed to a water phase through a disturbance effect (disturbance of natural environment wind, flow and wave), and water drops continuously invade an oil phase to form an emulsion, so that the environment is obviously damaged.
In this regard, special attention needs to be paid to the emulsification changes of marine spills. In the prior art, the emulsification area and diffusion of oil spill are only observed, the emulsification change process which continuously occurs in the oil spill is not considered, the emulsification process is difficult to be effectively and continuously observed, and the development process of the oil spill emulsification cannot be accurately and effectively predicted.
Disclosure of Invention
In view of the above problems, the present invention provides a method, an apparatus and a storage medium for predicting emulsification of marine oil spill, which can fully consider the emulsification change process occurring inside the oil spill, so as to accurately and effectively predict the development process of the emulsification of the oil spill.
In a first aspect, an embodiment of the present invention provides a method for predicting marine oil spill emulsification, including:
acquiring a remote sensing image set of an ocean test field in which an oil spill sample is put in advance; the remote sensing image set comprises remote sensing images respectively corresponding to the multiple oil spill sample feeding points, and each oil spill sample feeding point is fed with an oil spill sample before the corresponding remote sensing image is obtained;
obtaining a corresponding relation table of hyperspectral sensitive wave bands and emulsification degrees according to the remote sensing image set; wherein, the emulsification degree of the oil-spilling emulsion refers to the percentage of the volume of water contained in the oil-spilling emulsion in the total volume of the oil-spilling emulsion;
acquiring influence factor change data respectively corresponding to the plurality of oil spill sample feeding points; the change data of the influence factors corresponding to one oil spill sample feeding point is a change function of the influence factors of the oil spill sample feeding point in the corresponding oil spill emulsification occurrence time, and the oil spill emulsification occurrence time corresponding to one oil spill sample feeding point is a time period from the emulsification of the oil spill sample fed at the oil spill sample feeding point to the acquisition of the remote sensing image corresponding to the oil spill sample feeding point;
obtaining an initial model of the oil spill emulsification change according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree, the remote sensing image set, the influence factor change data respectively corresponding to the plurality of oil spill sample feeding points and the oil spill emulsification occurrence time respectively corresponding to the plurality of oil spill sample feeding points; the oil spill emulsification change initial model comprises an emulsification degree change initial model, an emulsion diffusion area initial model and an emulsion drift distance initial model;
correcting the initial oil spilling emulsification change model to obtain an oil spilling emulsification change model;
and acquiring a remote sensing image of any oil spilling sea area, and applying the oil spilling emulsification change model to predict the oil spilling emulsification degree change and the oil spilling emulsified material distribution change of the oil spilling sea area.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
acquiring a remote sensing image set of an ocean test field which is previously thrown with an oil spill sample and comprises a plurality of oil spill sample throwing points, obtaining a corresponding relation table of hyperspectral sensitive wave bands and emulsification degrees according to the remote sensing image set, obtaining influence factor change data respectively corresponding to the feeding points of the plurality of oil spilling samples, obtaining an initial model of the emulsification change of the oil spill according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree, the remote sensing image set, the change data of the influence factors and the occurrence time of the emulsification of the oil spill, and correcting the initial oil spill emulsification change model to obtain an oil spill emulsification change model, applying the oil spill emulsification change model to predict the oil spill emulsification degree change and the oil spill distribution change of any oil spill sea area, and fully considering the emulsification change process which continuously occurs in the oil spill, thereby accurately and effectively predicting the development process of oil spill emulsification.
As an improvement of the above scheme, obtaining a correspondence table between hyperspectral sensitive bands and emulsification degrees according to the remote sensing image set includes:
marking a plurality of sample pixels in the remote sensing image according to the longitude and latitude data of the plurality of oil spilling sample feeding points; wherein each sample pixel in the plurality of sample pixels corresponds to an oil-spilling emulsion;
drawing a corresponding spectrum curve according to the sample pixel; the abscissa of the spectrum curve represents the wavelength of the reflected electromagnetic wave corresponding to the plurality of sample pixels, and the ordinate represents the reflectivity of the reflected electromagnetic wave corresponding to the plurality of sample pixels;
smoothing the spectrum curve;
normalizing the spectrum curve after the smoothing treatment;
selecting an emulsification characteristic wave band of the spectrum curve after normalization processing;
and obtaining a corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree according to the predetermined relation between the reflectivity and the emulsification degree and the wave band value and the reflectivity corresponding to the emulsification characteristic wave band.
As an improvement of the above scheme, the obtaining an initial model of the change of the oil spill emulsification according to the table of the correspondence between the hyperspectral sensitive wave bands and the emulsification degrees, the remote sensing image set, the influence factor change data corresponding to each of the plurality of oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the plurality of oil spill sample feeding points includes:
obtaining the corresponding emulsification degree of each pixel in the remote sensing image corresponding to any oil spill sample feeding point according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree;
obtaining an initial model of the change of the emulsification degree according to the emulsification degree corresponding to the pixel corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points;
acquiring the resolution of a remote sensing image corresponding to any oil spill sample putting point;
calculating the space area of the emulsion in the remote sensing image corresponding to any oil spilling sample feeding point according to the resolution ratio of the remote sensing image corresponding to any oil spilling sample feeding point and the emulsification degree corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point;
obtaining an initial model of the diffusion area of the emulsion according to the space area of the emulsion in the remote sensing image corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points;
acquiring pixel values corresponding to all pixels from a remote sensing image corresponding to any oil spill sample putting point;
calculating the thickness of the emulsion corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point according to the pixel value corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point and a pre-established thickness function;
calculating the quality of the emulsion in the remote sensing image corresponding to any oil spill sample feeding point according to the resolution ratio of the remote sensing image corresponding to any oil spill sample feeding point, the emulsification degree corresponding to each pixel in the remote sensing image corresponding to any oil spill sample feeding point, the thickness of the emulsion corresponding to each pixel in the remote sensing image corresponding to any oil spill sample feeding point, the quality of the oil spill sample corresponding to any oil spill sample feeding point obtained in advance and the density of water;
according to the emulsion quality in the remote sensing image that a plurality of oil spilling sample input points correspond respectively, the influence factor change data that a plurality of oil spilling sample input points correspond respectively and the oil spilling emulsification time of occurrence that a plurality of oil spilling sample input points correspond respectively obtain the emulsion drift distance initial model.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
the initial model of the emulsification change of the oil spill is obtained based on the change data of the influence factors obtained from the reality, and a model which can predict the emulsification degree and the distribution condition of the oil spill emulsion closer to the reality can be obtained.
As an improvement of the above scheme, the influence factors include: seawater temperature, wind speed, seawater flow velocity, seawater salinity, illumination intensity, wind power, flow field intensity and yaw force.
As an improvement of the above-mentioned scheme, obtaining the initial model of the change of the emulsification degree according to the emulsification degree corresponding to the pixel corresponding to each of the plurality of oil spill sample feeding points, the influence factor change data corresponding to each of the plurality of oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the plurality of oil spill sample feeding points, includes:
establishing a first regression equation representing the relationship between the change value of the emulsification degree corresponding to the pixel corresponding to each oil spill sample feeding point and the influence factor:
C＝a1∫Tt+a2∫Sat+a3∫Vat+a4∫Wt+a5∫Lt+b
c represents the variation value of the emulsification degree corresponding to the pixel corresponding to the oil spill sample putting point, T represents the variation function of the corresponding seawater temperature, Sa represents the variation function of the corresponding seawater salinity, Va represents the variation function of the corresponding seawater flow velocity, W represents the variation function of the corresponding wind speed, L represents the variation function of the corresponding illumination intensity, T in the first regression equation represents the variation time of the emulsification degree corresponding to C, a1、a2、a3、a4、a5And b represents the regression coefficients of the first regression equation;
according to the emulsification degree corresponding to the pixels respectively corresponding to the oil spilling sample feeding points and the oil spilling sample feeding pointsDetermining the oil spill emulsification occurrence time corresponding to the influence factor change data and the oil spill sample feeding points respectively, and determining a in the first regression equation1、a2、a3、a4、a5And b, obtaining an initial model of the change of the emulsification degree.
As the improvement of above-mentioned scheme, according to emulsion spatial area in the remote sensing image that a plurality of oil spilling sample input points correspond respectively, a plurality of oil spilling sample input points respectively correspond influence factor change data and a plurality of oil spilling sample input points respectively correspond oil spilling emulsification emergence time, obtain emulsion diffusion area initial model includes:
establishing a second regression equation representing the relationship between the emulsion spatial area change and the influence factor:
ΔS＝(A1∫Vat+A2∫Wt)T+B
wherein Δ S represents a variation value of emulsion space area, Va represents a variation function of corresponding seawater flow velocity, W represents a variation function of corresponding wind velocity, T represents a variation function of corresponding seawater temperature, T in the second regression equation represents emulsion diffusion time corresponding to Δ S, and a1、A2And B represents the regression coefficient of the second regression equation;
determining the oil spill emulsification occurrence time corresponding to the oil spill sample feeding points in the second regression equation according to the emulsion space area in the remote sensing image corresponding to the oil spill sample feeding points respectively, the influence factor change data corresponding to the oil spill sample feeding points respectively and the oil spill emulsification occurrence time corresponding to the oil spill sample feeding points respectively1、A2And B, obtaining an initial model of the diffusion area of the emulsion.
As an improvement of the above-mentioned scheme, according to the emulsion quality in the remote sensing image that a plurality of oil spilling sample input points correspond respectively, the influence factor change data that a plurality of oil spilling sample input points correspond respectively and the oil spilling emulsification time that a plurality of oil spilling sample input points correspond respectively, obtain emulsion drift distance initial model includes:
establishing a third regression equation representing the relationship between drift distance and the impact factor:
wherein D represents the drift distance, t in the third regression equation represents the oil spill emulsification occurrence time corresponding to D, Fw represents the average value of wind power in the time t, Fs represents the average value of the field intensity in the time t, Fz represents the turning deviation force corresponding to the oil spill sample feeding point, M represents the emulsion mass of the corresponding oil spill emulsion, B represents the emulsion mass of the corresponding oil spill emulsion, and1and B2Regression coefficients representing the third regression equation; wherein, the drift distance of an oil-spilling emulsion is the distance from the mass center of the space region of the oil-spilling emulsion to the corresponding oil-spilling sample feeding point;
determining the emulsification occurrence time of the spilled oil corresponding to the multiple spilled oil sample feeding points according to the emulsion quality in the remote sensing images corresponding to the multiple spilled oil sample feeding points, the influence factor change data corresponding to the multiple spilled oil sample feeding points and the spilled oil emulsification occurrence time corresponding to the multiple spilled oil sample feeding points respectively in the third regression equation1And B2And obtaining an initial model of the drift distance of the emulsion.
As an improvement of the above scheme, the correcting the initial oil spill emulsification change model to obtain the oil spill emulsification change model includes:
acquiring a first corrected remote sensing image of an oil spill sample feeding point of the ocean test field at a first moment, and processing the first corrected remote sensing image to obtain a first remote sensing image interpretation map;
obtaining a first prediction graph corresponding to a second moment and a second prediction graph corresponding to a third moment according to the first remote sensing image interpretation graph and the oil spill emulsification change initial model; wherein the second time is before the first time and the third time is after the first time;
simulating an oil spill emulsification dynamic change process according to the first remote sensing image interpretation graph, the first prediction graph and the second prediction graph, and obtaining a plurality of prediction graphs corresponding to a plurality of correction moments;
acquiring a plurality of corrected remote sensing image interpretation graphs corresponding to the plurality of correction moments;
and correcting the emulsification degree change initial model, the emulsion diffusion area initial model and the emulsion drift distance initial model according to the plurality of prediction graphs and the plurality of corrected remote sensing image interpretation graphs to obtain the oil spill emulsification change model.
Compared with the prior art, the embodiment of the invention has the beneficial effects that:
the method comprises the steps of utilizing the established oil spill emulsification change initial model to reversely predict the emulsification degree of oil spill corresponding to the acquired first remote sensing image interpretation graph and the previous state of the distribution condition and predict the subsequent state, and correcting the emulsification degree change initial model, the emulsion diffusion area initial model and the emulsion drift distance initial model in the oil spill emulsification change initial model according to the actually acquired remote sensing image interpretation graph, so that the finally obtained prediction accuracy of the oil spill emulsification change model is higher.
In a second aspect, an embodiment of the present invention provides a marine oil spill emulsification prediction apparatus, which is characterized by comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the marine oil spill emulsification prediction method according to any one of the first aspect when executing the computer program.
In a third aspect, the embodiment of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, the apparatus in which the computer-readable storage medium is located is controlled to execute the marine oil spill emulsification prediction method according to any one of the first aspect.
Drawings
In order to more clearly illustrate the technical solution of the present invention, 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 that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for predicting marine oil spill emulsification according to a first embodiment of the present invention;
fig. 2 is a block diagram of a marine oil spill emulsification prediction apparatus according to a second 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.
Referring to fig. 1, a first embodiment of the present invention provides a method for predicting marine oil spill emulsification, comprising the following steps:
s11, acquiring a remote sensing image set of the marine test field in which the oil spill sample is put in advance; the remote sensing image set comprises remote sensing images respectively corresponding to the multiple oil spill sample feeding points, and each oil spill sample feeding point is fed with an oil spill sample before the corresponding remote sensing image is obtained;
in the embodiment of the invention, the remote sensing image set can be shot by a transit satellite.
S12, obtaining a corresponding relation table of hyperspectral sensitive wave bands and emulsification degrees according to the remote sensing image set; wherein, the emulsification degree of the oil-spilling emulsion refers to the percentage of the volume of water contained in the oil-spilling emulsion in the total volume of the oil-spilling emulsion;
s13, obtaining influence factor change data corresponding to the oil spill sample feeding points respectively; the change data of the influence factors corresponding to one oil spill sample feeding point is a change function of the influence factors of the oil spill sample feeding point in the corresponding oil spill emulsification occurrence time, and the oil spill emulsification occurrence time corresponding to one oil spill sample feeding point is a time period from the emulsification of the oil spill sample fed at the oil spill sample feeding point to the acquisition of the remote sensing image corresponding to the oil spill sample feeding point;
it should be noted that the influence factor change data corresponding to one oil spill sample feeding point reflects the overall environmental situation around the oil spill sample feeding point, the emulsification degree change and the emulsion distribution change of the oil spill sample fed at the oil spill sample feeding point are influenced by the influence factor change data corresponding to the oil spill sample feeding point, and it can be directly judged by remote sensing technology whether the oil spill sample fed at one oil spill sample feeding point starts emulsification, that is, whether the oil spill emulsification occurrence time corresponding to the oil spill sample feeding point starts.
S14, obtaining an initial model of the oil spill emulsification change according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree, the remote sensing image set, the influence factor change data respectively corresponding to the oil spill sample feeding points and the oil spill emulsification occurrence time respectively corresponding to the oil spill sample feeding points; the oil spill emulsification change initial model comprises an emulsification degree change initial model, an emulsion diffusion area initial model and an emulsion drift distance initial model;
s15, correcting the initial oil spill emulsification change model to obtain an oil spill emulsification change model;
s16, obtaining a remote sensing image of any oil spilling sea area, and applying the oil spilling emulsification change model to predict the oil spilling emulsification degree change and the oil spilling emulsified material distribution change of the oil spilling sea area.
It should be noted that, the arbitrary oil spilling sea area is not limited, for example, the arbitrary oil spilling sea area may be an oil spilling sea area in south sea of China, before the oil spilling emulsification change model is applied, the regression coefficient in the oil spilling emulsification change model may be adjusted according to the specific oil spilling sea area, and in the actual prediction, before the oil spilling emulsification degree change and the oil spilling emulsified material distribution change in the oil spilling sea area are predicted, the influence factor change data corresponding to the oil spilling sea area before the time that needs to be predicted is obtained first, and the influence factor change data may be obtained through weather forecast information and the like.
In the embodiment of the invention, a remote sensing image set of an ocean test field which is provided with spilled oil samples in advance and comprises a plurality of spilled oil sample feeding points is obtained, a hyperspectral sensitive wave band and emulsification degree corresponding relation table is obtained according to the remote sensing image set, an initial spilled oil emulsification change model is obtained according to the hyperspectral sensitive wave band and emulsification degree corresponding relation table, the remote sensing image set, influence factor change data and spilled oil emulsification occurrence time, the initial spilled oil emulsification change model is corrected to obtain a spilled oil emulsification change model, the spilled oil emulsification change model is applied to predict the spilled oil emulsification degree change and spilled oil distribution change of any spilled oil sea area, the emulsification change process which continuously occurs in spilled oil is fully considered, and therefore the development process of spilled oil emulsification is accurately and effectively predicted.
In an optional embodiment, the obtaining a table of correspondence between hyperspectral sensitive bands and emulsification degrees according to the remote sensing image set includes:
marking a plurality of sample pixels in the remote sensing image according to the longitude and latitude data of the plurality of oil spilling sample feeding points; wherein each sample pixel in the plurality of sample pixels corresponds to an oil-spilling emulsion;
drawing a corresponding spectrum curve according to the sample pixel; the abscissa of the spectrum curve represents the wavelength of the reflected electromagnetic wave corresponding to the plurality of sample pixels, and the ordinate represents the reflectivity of the reflected electromagnetic wave corresponding to the plurality of sample pixels;
smoothing the spectrum curve;
normalizing the spectrum curve after the smoothing treatment;
in the embodiment of the present invention, the spectrum curve after the smoothing process is normalized, that is, values on the spectrum curve after the smoothing process are mapped between the intervals (0, 1).
Selecting an emulsification characteristic wave band of the spectrum curve after normalization processing;
it should be noted that the emulsification characteristic band refers to a band interval with distinct peaks or troughs, and the emulsification characteristics of these bands are distinct.
And obtaining a corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree according to the predetermined relation between the reflectivity and the emulsification degree and the wave band value and the reflectivity corresponding to the emulsification characteristic wave band.
In the embodiment of the invention, the relation between the reflectivity and the emulsification degree can be obtained by carrying out a putting experiment in a marine test field in advance.
It should be noted that the spectrum curve reflects the capability of the ground object, such as the oil emulsion, to reflect the electromagnetic radiation, that is, the characteristic that the capability of the ground object to reflect the electromagnetic radiation changes with the wavelength of the reflected electromagnetic wave, and the reflection spectrum characteristics of the ground objects with different properties or the ground objects with the same property are different when the components, colors, surface structures, water content and the like are different, so as to form the difference of the spectrum curve, and the remote sensing is to identify the property of the ground object by acquiring and recording the information of the reflected electromagnetic wave of different wave bands of different ground objects and analyzing the difference.
In an optional embodiment, the obtaining an initial model of the change of the oil spill emulsification according to the table of correspondence between the hyperspectral sensitive wave bands and the emulsification degrees, the remote sensing image set, the influence factor change data corresponding to each of the plurality of oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the plurality of oil spill sample feeding points includes:
obtaining the corresponding emulsification degree of each pixel in the remote sensing image corresponding to any oil spill sample feeding point according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree;
obtaining an initial model of the change of the emulsification degree according to the emulsification degree corresponding to the pixel corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points;
acquiring the resolution of a remote sensing image corresponding to any oil spill sample putting point;
calculating the space area of the emulsion in the remote sensing image corresponding to any oil spilling sample feeding point according to the resolution ratio of the remote sensing image corresponding to any oil spilling sample feeding point and the emulsification degree corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point;
it should be noted that the spatial area of the oil emulsion in the remote sensing image can be obtained by multiplying the resolution of any remote sensing image by the number of pixels corresponding to the oil emulsion in the remote sensing image.
Obtaining an initial model of the diffusion area of the emulsion according to the space area of the emulsion in the remote sensing image corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points;
acquiring pixel values corresponding to all pixels from a remote sensing image corresponding to any oil spill sample putting point;
calculating the thickness of the emulsion corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point according to the pixel value corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point and a pre-established thickness function;
it should be noted that in the remote sensing image, the pixel value indicates the intensity of the reflected light, emulsions with different thicknesses, the reflection intensity is different, and the pixel value in the remote sensing image is also different, and the thickness function is used for calculating the emulsion thickness corresponding to a certain pixel according to the pixel value of the pixel.
Calculating the quality of the emulsion in the remote sensing image corresponding to any oil spill sample feeding point according to the resolution ratio of the remote sensing image corresponding to any oil spill sample feeding point, the emulsification degree corresponding to each pixel in the remote sensing image corresponding to any oil spill sample feeding point, the thickness of the emulsion corresponding to each pixel in the remote sensing image corresponding to any oil spill sample feeding point, the quality of the oil spill sample corresponding to any oil spill sample feeding point obtained in advance and the density of water;
it should be noted that the emulsion mass in the remote sensing image corresponding to any oil spill sample feeding point is obtained by integration.
According to the emulsion quality in the remote sensing image that a plurality of oil spilling sample input points correspond respectively, the influence factor change data that a plurality of oil spilling sample input points correspond respectively and the oil spilling emulsification time of occurrence that a plurality of oil spilling sample input points correspond respectively obtain the emulsion drift distance initial model.
In the embodiment of the invention, the initial model of the emulsification change of the oil spill is obtained based on the change data of the influence factors obtained from practice, and a model which can predict the emulsification degree and the distribution condition of the oil spill emulsion closer to the practice can be obtained.
In an alternative embodiment, the influencing factor includes: seawater temperature, wind speed, seawater flow velocity, seawater salinity, illumination intensity, wind power, flow field intensity and yaw force.
In an optional embodiment, the obtaining the initial model of the change of the emulsification degree according to the emulsification degree corresponding to the pixel corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points includes:
establishing a first regression equation representing the relationship between the change value of the emulsification degree corresponding to the pixel corresponding to each oil spill sample feeding point and the influence factor:
C＝a1∫Tt+a2∫Sat+a3∫Vat+a4∫Wt+a5∫Lt+b
wherein C represents the variation value of the emulsification degree corresponding to the pixel corresponding to the oil spill sample feeding point, and T represents the corresponding seawater temperatureSa represents the corresponding function of the change of the seawater salinity, Va represents the corresponding function of the change of the seawater flow velocity, W represents the corresponding function of the change of the wind velocity, L represents the corresponding function of the change of the illumination intensity, t in the first regression equation represents the change time of the emulsification degree corresponding to C, a1、a2、a3、a4、a5And b represents the regression coefficients of the first regression equation;
determining the emulsification occurrence time of the oil spilling corresponding to the oil spilling sample feeding points according to the emulsification degree corresponding to the pixels corresponding to the oil spilling sample feeding points, the influence factor change data corresponding to the oil spilling sample feeding points and the oil spilling emulsification occurrence time corresponding to the oil spilling sample feeding points1、a2、a3、a4、a5And b, obtaining an initial model of the change of the emulsification degree.
In an optional embodiment, the emulsion diffusion area initial model is obtained according to the emulsion spatial area in the remote sensing image that a plurality of oil spilling sample input points correspond respectively, the influence factor change data that a plurality of oil spilling sample input points correspond respectively and the oil spilling emulsification occurrence time that a plurality of oil spilling sample input points correspond respectively, includes:
establishing a second regression equation representing the relationship between the emulsion spatial area change and the influence factor:
ΔS＝(A1∫Vat+A2∫Wt)T+B
wherein Δ S represents a variation value of emulsion space area, Va represents a variation function of corresponding seawater flow velocity, W represents a variation function of corresponding wind velocity, T represents a variation function of corresponding seawater temperature, T in the second regression equation represents emulsion diffusion time corresponding to Δ S, and a1、A2And B represents the regression coefficient of the second regression equation;
according to the emulsion space area in the remote sensing image corresponding to the plurality of oil spilling sample feeding points, the influence factor change data corresponding to the plurality of oil spilling sample feeding points and the plurality of oil spillingDetermining the oil spill emulsification occurrence time corresponding to the sample feeding points respectively, and determining A in the second regression equation1、A2And B, obtaining an initial model of the diffusion area of the emulsion.
In an optional embodiment, the obtaining the initial model of drift distance of the emulsion according to the emulsion quality in the remote sensing images corresponding to the oil spill sample feeding points, the influence factor change data corresponding to the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to the oil spill sample feeding points, includes:
establishing a third regression equation representing the relationship between drift distance and the impact factor:
wherein D represents the drift distance, t in the third regression equation represents the oil spill emulsification occurrence time corresponding to D, Fw represents the average value of wind power in the time t, Fs represents the average value of the field intensity in the time t, Fz represents the turning deviation force corresponding to the oil spill sample feeding point, M represents the emulsion mass of the corresponding oil spill emulsion, B represents the emulsion mass of the corresponding oil spill emulsion, and1and B2Regression coefficients representing the third regression equation; wherein, the drift distance of an oil-spilling emulsion is the distance from the mass center of the space region of the oil-spilling emulsion to the corresponding oil-spilling sample feeding point;
it should be noted that the drift distance corresponding to each oil spill sample feeding point can be directly obtained from the corresponding remote sensing image.
Determining the emulsification occurrence time of the spilled oil corresponding to the multiple spilled oil sample feeding points according to the emulsion quality in the remote sensing images corresponding to the multiple spilled oil sample feeding points, the influence factor change data corresponding to the multiple spilled oil sample feeding points and the spilled oil emulsification occurrence time corresponding to the multiple spilled oil sample feeding points respectively in the third regression equation1And B2And obtaining an initial model of the drift distance of the emulsion.
It should be noted that, as long as the amount of the oil spill sample feeding points is enough and the corresponding influence factor change data is obtained, regression analysis can be performed on the first regression equation, the second regression equation and the third regression equation to determine the value of the regression coefficient.
In an optional embodiment, the correcting the initial oil spill emulsification change model to obtain an oil spill emulsification change model includes:
acquiring a first corrected remote sensing image of an oil spill sample feeding point of the ocean test field at a first moment, and processing the first corrected remote sensing image to obtain a first remote sensing image interpretation map;
it should be noted that the first corrected remote sensing image may also be directly obtained through a transit satellite.
Obtaining a first prediction graph corresponding to a second moment and a second prediction graph corresponding to a third moment according to the first remote sensing image interpretation graph and the oil spill emulsification change initial model; wherein the second time is before the first time and the third time is after the first time;
it should be noted that, in the process of obtaining the first prediction graph and the second prediction graph, at least the impact factor change data corresponding to the oil spill sample feeding point from the second time to the third time is also obtained first, and these impact factor change data are loaded into the oil spill emulsification change initial model.
Simulating an oil spill emulsification dynamic change process according to the first remote sensing image interpretation graph, the first prediction graph and the second prediction graph, and obtaining a plurality of prediction graphs corresponding to a plurality of correction moments;
it should be noted that the correction timings are between the second timing and the third timing.
Acquiring a plurality of corrected remote sensing image interpretation graphs corresponding to the plurality of correction moments;
it should be noted that the method for obtaining the plurality of corrected remote sensing image interpretation graphs is the same as the method for obtaining the first remote sensing image interpretation graph.
And correcting the emulsification degree change initial model, the emulsion diffusion area initial model and the emulsion drift distance initial model according to the plurality of prediction graphs and the plurality of corrected remote sensing image interpretation graphs to obtain the oil spill emulsification change model.
In the embodiment of the invention, the established oil spill emulsification change initial model is used for carrying out back-prediction on the emulsification degree and the previous state of the distribution condition of the oil spill corresponding to the acquired first remote sensing image interpretation graph and predicting the subsequent state of the oil spill, and then the emulsification degree change initial model, the emulsion diffusion area initial model and the emulsion drift distance initial model in the oil spill emulsification change initial model are corrected according to the actually acquired remote sensing image interpretation graph, so that the prediction accuracy of the finally acquired oil spill emulsification change model is higher.
Referring to fig. 2, which is a block diagram illustrating a marine oil spill emulsification prediction apparatus according to a second embodiment of the present invention, as shown in fig. 2, the marine oil spill emulsification prediction apparatus includes: at least one processor 11, such as a CPU, at least one network interface 14 or other user interface 13, a memory 15, at least one communication bus 12, the communication bus 12 being used to enable connectivity communications between these components. The user interface 13 may optionally include a USB interface, and other standard interfaces, wired interfaces. The network interface 14 may optionally include a Wi-Fi interface as well as other wireless interfaces. The memory 15 may comprise a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 15 may optionally comprise at least one memory device located remotely from the aforementioned processor 11.
In some embodiments, memory 15 stores the following elements, executable modules or data structures, or a subset thereof, or an expanded set thereof:
an operating system 151, which contains various system programs for implementing various basic services and for processing hardware-based tasks;
and (5) a procedure 152.
Specifically, the processor 11 is configured to call the program 152 stored in the memory 15 to execute the marine oil spill emulsification prediction method according to the above embodiment, for example, step S11 shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the marine oil spill emulsification prediction device.
The marine oil spill emulsification prediction device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The marine oil spill emulsification prediction device may include, but is not limited to, a processor, a memory. It will be understood by those skilled in the art that the schematic is merely an example of a marine spill oil emulsification prediction device and does not constitute a limitation of a marine spill oil emulsification prediction device and may include more or fewer components than shown, or some components in combination, or different components.
The Processor 11 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor 11 is a control center of the marine oil spill emulsification prediction device, and various interfaces and lines are used to connect various parts of the marine oil spill emulsification prediction device.
The memory 15 may be used to store the computer programs and/or modules, and the processor 11 implements various functions of the marine oil spill emulsification prediction apparatus by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory 15 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 15 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the integrated module/unit of the marine oil spill emulsification prediction device can be stored in a computer readable storage medium if the integrated module/unit is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
A third embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute the marine oil spill emulsification prediction method according to any one of the first embodiments.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A marine oil spill emulsification prediction method is characterized by comprising the following steps:
acquiring a remote sensing image set of an ocean test field in which an oil spill sample is put in advance; the remote sensing image set comprises remote sensing images respectively corresponding to the multiple oil spill sample feeding points, and each oil spill sample feeding point is fed with an oil spill sample before the corresponding remote sensing image is obtained;
obtaining a corresponding relation table of hyperspectral sensitive wave bands and emulsification degrees according to the remote sensing image set; wherein, the emulsification degree of the oil-spilling emulsion refers to the percentage of the volume of water contained in the oil-spilling emulsion in the total volume of the oil-spilling emulsion;
acquiring influence factor change data respectively corresponding to the plurality of oil spill sample feeding points; the change data of the influence factors corresponding to one oil spill sample feeding point is a change function of the influence factors of the oil spill sample feeding point in the corresponding oil spill emulsification occurrence time, and the oil spill emulsification occurrence time corresponding to one oil spill sample feeding point is a time period from the emulsification of the oil spill sample fed at the oil spill sample feeding point to the acquisition of the remote sensing image corresponding to the oil spill sample feeding point;
obtaining an initial model of the oil spill emulsification change according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree, the remote sensing image set, the influence factor change data respectively corresponding to the plurality of oil spill sample feeding points and the oil spill emulsification occurrence time respectively corresponding to the plurality of oil spill sample feeding points; the oil spill emulsification change initial model comprises an emulsification degree change initial model, an emulsion diffusion area initial model and an emulsion drift distance initial model;
correcting the initial oil spilling emulsification change model to obtain an oil spilling emulsification change model;
and acquiring a remote sensing image of any oil spilling sea area, and applying the oil spilling emulsification change model to predict the oil spilling emulsification degree change and the oil spilling emulsified material distribution change of the oil spilling sea area.
2. The marine oil spill emulsification prediction method of claim 1, wherein the obtaining of the correspondence table of hyperspectral sensitive bands and emulsification degrees according to the remote sensing image set comprises:
marking a plurality of sample pixels in the remote sensing image according to the longitude and latitude data of the plurality of oil spilling sample feeding points; wherein each sample pixel in the plurality of sample pixels corresponds to an oil-spilling emulsion;
drawing a corresponding spectrum curve according to the sample pixel; the abscissa of the spectrum curve represents the wavelength of the reflected electromagnetic wave corresponding to the plurality of sample pixels, and the ordinate represents the reflectivity of the reflected electromagnetic wave corresponding to the plurality of sample pixels;
smoothing the spectrum curve;
normalizing the spectrum curve after the smoothing treatment;
selecting an emulsification characteristic wave band of the spectrum curve after normalization processing;
and obtaining a corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree according to the predetermined relation between the reflectivity and the emulsification degree and the wave band value and the reflectivity corresponding to the emulsification characteristic wave band.
3. The marine oil spill emulsification prediction method according to claim 1 or 2, wherein the obtaining of the initial model of oil spill emulsification change according to the correspondence table of the hyperspectral sensitive band and the emulsification degree, the remote sensing image set, the influence factor change data corresponding to each of the plurality of oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the plurality of oil spill sample feeding points comprises:
obtaining the corresponding emulsification degree of each pixel in the remote sensing image corresponding to any oil spill sample feeding point according to the corresponding relation table of the hyperspectral sensitive wave band and the emulsification degree;
obtaining an initial model of the change of the emulsification degree according to the emulsification degree corresponding to the pixel corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points;
acquiring the resolution of a remote sensing image corresponding to any oil spill sample putting point;
calculating the space area of the emulsion in the remote sensing image corresponding to any oil spilling sample feeding point according to the resolution ratio of the remote sensing image corresponding to any oil spilling sample feeding point and the emulsification degree corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point;
obtaining an initial model of the diffusion area of the emulsion according to the space area of the emulsion in the remote sensing image corresponding to each of the oil spill sample feeding points, the influence factor change data corresponding to each of the oil spill sample feeding points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample feeding points;
acquiring pixel values corresponding to all pixels from a remote sensing image corresponding to any oil spill sample putting point;
calculating the thickness of the emulsion corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point according to the pixel value corresponding to each pixel in the remote sensing image corresponding to any oil spilling sample feeding point and a pre-established thickness function;
calculating the quality of the emulsion in the remote sensing image corresponding to any oil spill sample feeding point according to the resolution ratio of the remote sensing image corresponding to any oil spill sample feeding point, the emulsification degree corresponding to each pixel in the remote sensing image corresponding to any oil spill sample feeding point, the thickness of the emulsion corresponding to each pixel in the remote sensing image corresponding to any oil spill sample feeding point, the quality of the oil spill sample corresponding to any oil spill sample feeding point obtained in advance and the density of water;
according to the emulsion quality in the remote sensing image that a plurality of oil spilling sample input points correspond respectively, the influence factor change data that a plurality of oil spilling sample input points correspond respectively and the oil spilling emulsification time of occurrence that a plurality of oil spilling sample input points correspond respectively obtain the emulsion drift distance initial model.
4. The method for predicting marine oil spill emulsification according to claim 3, wherein said influence factors comprise: seawater temperature, wind speed, seawater flow velocity, seawater salinity, illumination intensity, wind power, flow field intensity and yaw force.
5. The method according to claim 4, wherein the obtaining of the initial model of the change of the emulsification degree according to the emulsification degree corresponding to the pixel corresponding to each of the plurality of oil spill sample dispensing points, the influence factor change data corresponding to each of the plurality of oil spill sample dispensing points, and the oil spill emulsification occurrence time corresponding to each of the plurality of oil spill sample dispensing points comprises:
establishing a first regression equation representing the relationship between the change value of the emulsification degree corresponding to the pixel corresponding to each oil spill sample feeding point and the influence factor:
C＝a1∫Tt+a2∫Sat+a3∫Vat+a4∫Wt+a5∫Lt+b
wherein C represents the emulsification degree corresponding to the pixel corresponding to the oil spill sample putting pointT represents the corresponding function of the change of the seawater temperature, Sa represents the corresponding function of the change of the seawater salinity, Va represents the corresponding function of the change of the seawater flow velocity, W represents the corresponding function of the change of the wind velocity, L represents the corresponding function of the change of the illumination intensity, T in the first regression equation represents the change time of the emulsification degree corresponding to C, a1、a2、a3、a4、a5And b represents the regression coefficients of the first regression equation;
determining the emulsification occurrence time of the oil spilling corresponding to the oil spilling sample feeding points according to the emulsification degree corresponding to the pixels corresponding to the oil spilling sample feeding points, the influence factor change data corresponding to the oil spilling sample feeding points and the oil spilling emulsification occurrence time corresponding to the oil spilling sample feeding points1、a2、a3、a4、a5And b, obtaining an initial model of the change of the emulsification degree.
6. The marine oil spill emulsification prediction method of claim 5, wherein the obtaining of the initial model of the emulsion diffusion area according to the emulsion spatial area in the remote sensing image corresponding to each of the oil spill sample dispensing points, the influence factor change data corresponding to each of the oil spill sample dispensing points, and the oil spill emulsification occurrence time corresponding to each of the oil spill sample dispensing points comprises:
establishing a second regression equation representing the relationship between the emulsion spatial area change and the influence factor:
ΔS＝(A1∫Vat+A2∫Wt)T+B
wherein Δ S represents a variation value of emulsion space area, Va represents a variation function of corresponding seawater flow velocity, W represents a variation function of corresponding wind velocity, T represents a variation function of corresponding seawater temperature, T in the second regression equation represents emulsion diffusion time corresponding to Δ S, and a1、A2And B represents the regression coefficient of the second regression equation;
respectively corresponding remote sensing images according to the plurality of oil spilling sample putting pointsThe space area of the emulsion in the image, the influence factor change data corresponding to the plurality of oil spilling sample feeding points respectively and the oil spilling emulsification occurrence time corresponding to the plurality of oil spilling sample feeding points respectively are determined according to the A in the second regression equation1、A2And B, obtaining an initial model of the diffusion area of the emulsion.
7. The marine oil spill emulsification prediction method of claim 6, wherein the obtaining of the initial model of the drift distance of the emulsion according to the amount of the emulsion in the remote sensing images corresponding to the oil spill sample dispensing points, the influence factor change data corresponding to the oil spill sample dispensing points, and the oil spill emulsification occurrence time corresponding to the oil spill sample dispensing points comprises:
establishing a third regression equation representing the relationship between drift distance and the impact factor:
wherein D represents the drift distance, t in the third regression equation represents the oil spill emulsification occurrence time corresponding to D, Fw represents the average value of wind power in the time t, Fs represents the average value of the field intensity in the time t, Fz represents the turning deviation force corresponding to the oil spill sample feeding point, M represents the emulsion mass of the corresponding oil spill emulsion, B represents the emulsion mass of the corresponding oil spill emulsion, and1and B2Regression coefficients representing the third regression equation; wherein, the drift distance of an oil-spilling emulsion is the distance from the mass center of the space region of the oil-spilling emulsion to the corresponding oil-spilling sample feeding point;
determining the emulsification occurrence time of the spilled oil corresponding to the multiple spilled oil sample feeding points according to the emulsion quality in the remote sensing images corresponding to the multiple spilled oil sample feeding points, the influence factor change data corresponding to the multiple spilled oil sample feeding points and the spilled oil emulsification occurrence time corresponding to the multiple spilled oil sample feeding points respectively in the third regression equation1And B2And obtaining an initial model of the drift distance of the emulsion.
8. The marine oil spill emulsification prediction method of claim 7, wherein said correcting said initial oil spill emulsification change model to obtain an oil spill emulsification change model comprises:
acquiring a first corrected remote sensing image of an oil spill sample feeding point of the ocean test field at a first moment, and processing the first corrected remote sensing image to obtain a first remote sensing image interpretation map;
obtaining a first prediction graph corresponding to a second moment and a second prediction graph corresponding to a third moment according to the first remote sensing image interpretation graph and the oil spill emulsification change initial model; wherein the second time is before the first time and the third time is after the first time;
simulating an oil spill emulsification dynamic change process according to the first remote sensing image interpretation graph, the first prediction graph and the second prediction graph, and obtaining a plurality of prediction graphs corresponding to a plurality of correction moments;
acquiring a plurality of corrected remote sensing image interpretation graphs corresponding to the plurality of correction moments;
and correcting the emulsification degree change initial model, the emulsion diffusion area initial model and the emulsion drift distance initial model according to the plurality of prediction graphs and the plurality of corrected remote sensing image interpretation graphs to obtain the oil spill emulsification change model.
9. A marine oil spill emulsification prediction device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the marine oil spill emulsification prediction method of any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the marine oil spill emulsification prediction method according to any one of claims 1-8.
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