CN111027766A - Green tide biomass forecasting method, device, equipment and medium - Google Patents

Green tide biomass forecasting method, device, equipment and medium Download PDF

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CN111027766A
CN111027766A CN201911249340.7A CN201911249340A CN111027766A CN 111027766 A CN111027766 A CN 111027766A CN 201911249340 A CN201911249340 A CN 201911249340A CN 111027766 A CN111027766 A CN 111027766A
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CN111027766B (en
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万振文
黄娟
高松
李�杰
吴玲娟
徐江铃
丁一
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Nanjing Machen Digital Technology Co ltd
North China Sea Marine Forecasting Center Of State Oceanic Administration
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Abstract

The embodiment of the invention discloses a green tide biomass forecasting method, a green tide biomass forecasting device, green tide biomass forecasting equipment and a green tide biomass forecasting medium. The method comprises the following steps: determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter; and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image. By adopting the technical scheme provided by the invention, the green tide biomass can be predicted through the green tide biomass forecasting model, so that more time is saved for coping with green tide disasters, and the loss of the green tide disasters is reduced.

Description

Green tide biomass forecasting method, device, equipment and medium
Technical Field
The embodiment of the invention relates to a marine ecology prediction technology, in particular to a green tide biomass forecasting method, a device, equipment and a medium.
Background
Since 2007, yellow sea has a large scale green tide outbreak every year, which has a great influence on marine activities and coastal tourism. If the ocean green tide outbreak outside the island in 2008 is dangerous for the normal development of the Olympic Games, the local government organizes a lot of human resources for salvage to ensure the normal behavior of the Olympic Games. Moreover, the ocean green tide outbreak can cause the large area death of fishes and shrimps due to the exhaustion of dissolved oxygen. Therefore, the prediction of ocean green tide becomes an urgent problem to be solved.
At present, an ecological dynamics model numerical prediction technology is used for predicting through an ecological dynamics coupling model, but parameters are numerous, and the practicability is poor.
Therefore, an effective marine green tide prediction method is needed, so that more time is saved for coping with green tide disasters, and the loss of the green tide disasters is reduced.
Disclosure of Invention
The invention provides a green tide biomass forecasting method, a green tide biomass forecasting device, green tide biomass forecasting equipment and a green tide biomass forecasting medium, which are used for forecasting green tide biomass through a green tide forecasting model, thereby gaining more time for dealing with green tide disasters and reducing the loss of the green tide disasters.
In a first aspect, an embodiment of the present invention provides a method for building a green tide biomass prediction model, including:
determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
In a second aspect, an embodiment of the present invention further provides a green tide biomass forecasting model building apparatus, including:
the green tide biomass estimation model determining module is used for determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and the undetermined parameter determining module is used for determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area so as to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the green tide biomass prediction model building method according to any of the embodiments of the present invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the green tide biomass prediction model building method according to any one of the embodiments of the present invention.
The embodiment of the invention determines an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter; and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to the reference distribution data of the green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, thereby gaining more time for responding to the green tide disaster and reducing the loss of the green tide disaster.
Drawings
FIG. 1 is a schematic flow chart of a method for building a green tide biomass prediction model according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for building a green tide biomass prediction model according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a green tide biomass prediction model building apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus provided in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example one
Fig. 1 is a schematic flow chart of a green tide biomass forecasting model building method according to an embodiment of the present invention, which is applicable to predicting marine green tide biomass, and the method can be implemented by a green tide biomass forecasting model building apparatus, which can be implemented in software and/or hardware, and can be integrated in an electronic device, and specifically includes the following steps:
s110, determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter.
In this embodiment, the green tide biomass estimation model is a model capable of predicting green tide biomass, and the model takes into account a plurality of condition parameters affecting green tide biomass. These condition parameters include, but are not limited to: wind speed, water flow rate, and light intensity. The undetermined parameters are weights of the conditional parameters, and the undetermined parameters are respectively as follows: c. C1、c2、c3、c4And c5The weight of temperature, the weight of illumination intensity, the weight of nutrient salts, the weight of natural mortality in the life cycle and the weight of wave-breaking green suspension balance mortality are respectively.
S120, determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target region to obtain the green tide biomass estimation model after parameter determination.
Wherein the reference distribution data is determined from a satellite remote sensing image.
The target region is a region where the growth environment meets the growth conditions of the green tide, and the green tide can be grown in a large area in the region. The satellite remote sensing image is an image obtained in a target area through a satellite, the image can judge the number of green tides through the shades of colors, the green tide density is higher at the deeper position of the color on the satellite remote sensing image, the corresponding number of green tides is higher, the green tide density is lower at the lighter position of the color, and the number of green tides is lower correspondingly.
In the embodiment, reference distribution data of green tide biomass in the target area is determined through the satellite remote sensing image, and then the reference distribution data is substituted into the initially constructed green tide biomass estimation model to determine the green tide biomass estimation model after parameter determination.
The embodiment of the invention determines an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter; and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to the reference distribution data of the green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, thereby gaining more time for responding to the green tide disaster and reducing the loss of the green tide disaster.
Example two
Fig. 2 is a schematic flow chart of a method for building a green tide biomass prediction model according to a second embodiment of the present invention, and the technical solution is further optimized on the basis of the above technical solution, and the optimization is as follows: and determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid. The embodiment specifically comprises the following steps:
s210, determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter.
In the embodiment of the invention, the initially constructed green tide biomass estimation model is determined by adopting the following formula:
Figure BDA0002308570100000051
wherein G represents green tide biomass per unit area, t represents time, U, V represents flow velocity, U, V represents wind speed, e represents wind-sea current coefficient, and gamma represents distribution diffusion coefficient. S is the green tide biomass generated in unit time and unit area, P is the growth rate of the green tide, and D is the death rate of the green tide;
in the above formula, the growth rate P of green tide adopts the following formula:
Figure BDA0002308570100000061
wherein, c1、c2And c3Denotes the parameter to be determined, T denotes the temperature, T0Indicating the historical average temperature, L, of the seawater in the green season0Representing the historical average light intensity, H, of the sea surface in the green seasonNLimiting nutrient salt moieties for green tide growthA saturation constant;
in the above formula, the mortality rate D of green tide is obtained by using the following formula:
Figure BDA0002308570100000062
wherein I represents the life cycle of the green tide, the unit is day, HLRepresenting the half life cycle in days, W representing the sea surface wind speed, W0Representing the historical average wind speed, c, over the green tide growing season4Indicating natural mortality within the life cycle, c5Indicating the mortality of wind waves breaking the suspension balance of green tide.
S220, determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data are determined according to the satellite remote sensing image.
Optionally, the step of determining the reference distribution data includes:
acquiring a shot target area satellite remote sensing image;
carrying out gridding processing on the satellite remote sensing image of the target area to obtain a unit grid in the target area;
and determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid.
In the present embodiment, the gridding process in the target region is to divide the target region into horizontal and vertical grids. Further, the initial distribution data is plotted in a grid with the horizontal axis being the position coordinates of the region and the vertical axis being the green tide biomass. The reference distribution data can be determined by the image parameters in the unit grid.
Optionally, determining the reference distribution data according to image parameters of the satellite remote sensing image in each unit grid includes:
determining initial reference distribution data according to image parameters of the satellite remote sensing image in each unit grid;
dividing unit grids in the target area into grids with green tide and grids without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold;
clearing the distribution data in the grid without the green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with the green tide; if the uniformly distributed distribution data with the green tide grids is larger than a preset distribution upper limit threshold, uniformly distributing the parts larger than the preset distribution upper limit threshold to other green tide grids to obtain a redistribution result of the initial reference distribution data;
and determining final reference distribution data of the unit grids in the target area according to the redistribution result.
In this embodiment, the reference distribution data is determined according to image parameters of the satellite remote sensing image in each unit grid, and specifically, the reference distribution data is calculated by the following formula:
Figure BDA0002308570100000071
wherein G isi,j(x, y) represents the distribution of green tide biomass in the unit grid, xi,j，yi,jRepresenting the coordinates of the unit grid centroid.
Wherein, the preset distribution lower limit threshold is Gi,jAnd (x, y) comparing the reference distribution data with a preset distribution lower limit threshold, taking the unit grid corresponding to the data smaller than the preset distribution lower limit threshold in the reference distribution data as a green tide-free grid, and taking the unit grid corresponding to the data larger than the preset distribution lower limit threshold in the reference distribution data as a green tide-containing grid.
The advantage of this arrangement is that by distributing green tides to grids with green tides, the influence of grids with less green tides on the surroundings can also be taken into account.
If the distributed data of the uniformly distributed grids with the green tide is larger than the preset distribution upper limit threshold value, the fact that the green tide in the grids is concentrated is shown, and the green tide spreads to the periphery after growing to a certain thickness according to the growth rule of the green tide, so that the parts larger than the preset distribution upper limit threshold value are uniformly distributed to other grids with the green tide, and the reference distribution data of the unit grids in the target area are redrawn.
Preferably, the number of the green tide plaques in the target area can be numbered in advance, and the centroid of each plaque is determined. Illustratively, the green tide patch numbered 1 covers four grids with green tides, A, B, C and D respectively, wherein the green tide biomass in grid A is lower than a preset lower distribution threshold, the green tide biomass in grid B is higher than a preset upper distribution threshold, and the green tide biomass in grid C and grid D is between the preset lower distribution threshold and the preset upper distribution threshold. The green tide biomass in the a grid is collected and redistributed to the C grid or the D grid. And redistributing the green tide biomass which is larger than the preset distribution upper limit threshold value in the grid B into the grid C or the grid D, and redistributing the green tide biomass which is larger than the preset distribution upper limit threshold value into the grid C if the green tide biomass of the distributed grid D is larger than the preset distribution upper limit threshold value.
And S230, forecasting the green tide biomass in the target area by adopting the green tide biomass estimation model after parameter determination.
In this embodiment, the green tide biomass estimation model after parameter determination can predict the green tide biomass in the future one or more years of the region.
According to the embodiment of the invention, the reference distribution data is determined according to the image parameters of the satellite remote sensing image in each unit grid, so that more accurate undetermined parameters are obtained, and a more accurate parameter-fixed green tide biomass forecasting model is obtained, so that the green tide biomass is forecasted through the parameter-fixed green tide biomass forecasting model, more time is gained for coping with green tide disasters, and the loss of the green tide disasters is reduced.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a green tide biomass prediction model building apparatus according to a third embodiment of the present invention. The green tide biomass forecasting model establishing device provided by the embodiment of the invention can execute the green tide biomass forecasting model establishing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method. As shown in fig. 3, the apparatus includes:
a green tide biomass estimation model determining module 301, configured to determine an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and the undetermined parameter determining module 302 is configured to determine a numerical value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target region to obtain the reference-determined green tide biomass estimation model, wherein the reference distribution data is determined according to the satellite remote sensing image.
The device further comprises:
and the green tide biomass forecasting module 303 is configured to forecast the green tide biomass in the target area by using the green tide biomass estimation model after parameter determination.
The pending parameter determining module 302 is specifically configured to:
acquiring a shot target area satellite remote sensing image;
carrying out gridding processing on the satellite remote sensing image of the target area to obtain a unit grid in the target area;
and determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid.
The pending parameter determining module 302 is specifically configured to: dividing unit grids in the target area into grids with green tide and grids without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold;
clearing the distribution data in the grid without the green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with the green tide; if the uniformly distributed distribution data with the green tide grids is larger than a preset distribution upper limit threshold, uniformly distributing the parts larger than the preset distribution upper limit threshold to other green tide grids to obtain a redistribution result of the initial reference distribution data;
and determining final reference distribution data of the unit grids in the target area according to the redistribution result.
The green tide biomass estimation model determining module 301 is specifically configured to determine through the following formula:
Figure BDA0002308570100000101
wherein G represents green tide biomass per unit area, t represents time, U, V represents flow velocity, U, V represents wind speed, e represents wind-sea current coefficient, and gamma represents distribution diffusion coefficient. S is the green tide biomass generated in unit time and unit area, P is the growth rate of the green tide, and D is the death rate of the green tide;
in the above formula, the growth rate P of green tide adopts the following formula:
Figure BDA0002308570100000102
wherein, c1、c2And c3Denotes the parameter to be determined, T denotes the temperature, T0Indicating the historical average temperature, L, of the seawater in the green season0Representing the historical average light intensity, H, of the sea surface in the green seasonNLimiting nutrient salt half-saturation constant representing green tide growth;
in the above formula, the mortality rate D of green tide is obtained by using the following formula:
Figure BDA0002308570100000103
wherein I represents the life cycle of the green tide, the unit is day, HLRepresenting the half life cycle in days, W representing the sea surface wind speed, W0Representing the historical average wind speed, c, over the green tide growing season4Indicating natural mortality within the life cycle, c5Indicating the mortality of wind waves breaking the suspension balance of green tide.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described apparatus may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Example four
Fig. 4 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention, and fig. 4 is a schematic structural diagram of an exemplary apparatus suitable for implementing the embodiment of the present invention. The device 12 shown in fig. 4 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 4, device 12 is in the form of a general purpose computing device. The components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with device 12, and/or with any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown in FIG. 4, the network adapter 20 communicates with the other modules of the device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing a green tide biomass forecast model building method provided by the embodiment of the present invention, including:
determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program (or referred to as a computer-executable instruction) is stored, where the computer program, when executed by a processor, can implement the method for building a green tide biomass prediction model according to any of the embodiments described above, and the method includes:
determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A green tide biomass forecasting model building method is characterized by comprising the following steps:
determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
2. The method of claim 1, wherein after determining the parameterized green tide biomass estimation model, the method further comprises:
and forecasting the green tide biomass in the target area by adopting the green tide biomass estimation model after parameter determination.
3. The method of claim 1, wherein the step of determining the reference distribution data comprises:
acquiring a shot target area satellite remote sensing image;
carrying out gridding processing on the satellite remote sensing image of the target area to obtain a unit grid in the target area;
and determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid.
4. The method according to claim 3, wherein determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid comprises:
determining initial reference distribution data according to image parameters of the satellite remote sensing image in each unit grid;
dividing unit grids in the target area into grids with green tide and grids without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold;
clearing the distribution data in the grid without the green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with the green tide; if the uniformly distributed distribution data with the green tide grids is larger than a preset distribution upper limit threshold, uniformly distributing the parts larger than the preset distribution upper limit threshold to other green tide grids to obtain a redistribution result of the initial reference distribution data;
and determining final reference distribution data of the unit grids in the target area according to the redistribution result.
5. The method of claim 1, wherein the initially constructed green tide biomass estimation model adopts the following formula:
Figure FDA0002308570090000021
wherein G represents green tide biomass per unit area, t represents time, U, V represents flow velocity, U, V represents wind speed, e represents wind-sea current coefficient, and gamma represents distribution diffusion coefficient. S is the green tide biomass generated in unit time and unit area, P is the growth rate of the green tide, and D is the death rate of the green tide;
in the above formula, the growth rate P of green tide adopts the following formula:
Figure FDA0002308570090000022
wherein, c1、c2And c3Denotes the parameter to be determined, T denotes the temperature, T0Indicating the historical average temperature, L, of the seawater in the green season0Representing the historical average light intensity, H, of the sea surface in the green seasonNLimiting nutrient salt half-saturation constant representing green tide growth;
in the above formula, the mortality rate D of green tide is obtained by using the following formula:
Figure FDA0002308570090000023
wherein I represents the life cycle of the green tide, the unit is day, HLRepresenting the half life cycle in days, W representing the sea surface wind speed, W0Representing the historical average wind speed, c, over the green tide growing season4Indicating natural mortality within the life cycle, c5Indicating the mortality of wind waves breaking the suspension balance of green tide.
6. A green tide biomass forecast model building device is characterized by comprising:
the green tide biomass estimation model determining module is used for determining an initially constructed green tide biomass estimation model; the green tide biomass estimation model comprises at least one undetermined parameter;
and the undetermined parameter determining module is used for determining the value of at least one undetermined parameter included in the initially constructed green tide biomass estimation model according to reference distribution data of green tide biomass in the target area so as to obtain the green tide biomass estimation model after parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
7. The apparatus of claim 6, further comprising:
and the green tide biomass forecasting module is used for forecasting the green tide biomass in the target area by adopting the green tide biomass estimation model after parameter determination.
8. The apparatus of claim 6, wherein the pending parameter determination module is specifically configured to:
acquiring a shot target area satellite remote sensing image;
carrying out gridding processing on the satellite remote sensing image of the target area to obtain a unit grid in the target area;
and determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the green tide biomass prediction model building method of any of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the green tide biomass prediction model building method according to any one of claims 1-5.
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