CN111027766B - Green tide biomass forecasting method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the invention discloses a green tide biomass forecasting method, a device, equipment and a medium. The method comprises the following steps: determining an initially constructed green tide biomass estimation model; wherein 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 so as to obtain the green tide biomass estimation model after the 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 by the green tide biomass prediction model, so that more time is won for coping with the green tide disaster, and the loss of the green tide disaster is reduced.
Description
Technical Field
The embodiment of the invention relates to a marine ecological prediction technology, in particular to a green tide biomass prediction method, a device, equipment and a medium.
Background
Since 2007, yellow sea has a large-scale green tide outbreak every year, and has great influence on offshore activities and coastal travel sightseeing. If the green tide outbreak outside the green island of 2008 endangers the normal development of the oshan, the local government organizes a lot of manpower resources for salvage to ensure the normal running of the oshan. Moreover, the ocean green outbreak can also lead to large-area death of fish and shrimp due to dissolved oxygen depletion. Therefore, marine green tide prediction is a problem to be solved urgently.
At present, an ecological dynamic model numerical forecasting technology predicts through an ecological dynamic coupling model, but has numerous parameters and poor practicability.
Therefore, an effective ocean green tide prediction method is needed to gain more time for coping with green tide disasters and reduce the loss of the green tide disasters.
Disclosure of Invention
The invention provides a green tide biomass forecasting method, a device, equipment and a medium, which are used for forecasting green tide biomass through a green tide forecasting model, so that more time is won for coping with green tide disasters, and the loss of the green tide disasters is reduced.
In a first aspect, an embodiment of the present invention provides a method for establishing a green tide biomass forecasting model, including:
determining an initially constructed green tide biomass estimation model; wherein 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 so as to obtain the green tide biomass estimation model after the determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
In a second aspect, the embodiment of the present invention further provides a green tide biomass forecasting model building device, including:
the green tide biomass estimation model determining module is used for determining an initially constructed green tide biomass estimation model; wherein the green tide biomass estimation model comprises at least one undetermined parameter;
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 the reference distribution data of the green tide biomass in the target area so as to obtain the green tide biomass estimation model after the parameter 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 in the memory and capable of running on the processor, where the processor implements the method for building a green tide biomass forecasting model according to any one of the embodiments of the present invention when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a green tide biomass forecasting 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; wherein 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 so as to obtain the green tide biomass estimation model after the harvest, thereby winning more time for coping with the green tide disaster and reducing the loss of the green tide disaster.
Drawings
FIG. 1 is a flow chart of a method for establishing a green tide biomass forecasting model provided in a first embodiment of the invention;
FIG. 2 is a schematic flow chart of a method for establishing a green tide biomass forecasting model provided in a second embodiment of the invention;
FIG. 3 is a schematic structural diagram of a green tide biomass forecasting model building device according to a third embodiment of the invention;
fig. 4 is a schematic structural view of an apparatus according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, 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 figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example 1
Fig. 1 is a flow chart of a green tide biomass forecasting model establishing method according to an embodiment of the present invention, where the embodiment is applicable to predicting marine green tide biomass, and the method may be performed by a green tide biomass forecasting model establishing device, and the device may be implemented in a software and/or hardware manner and may be integrated in an electronic device, and specifically includes the following steps:
s110, determining an initially constructed green tide biomass estimation model; wherein 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, which takes into consideration a plurality of condition parameters affecting green tide biomass. These condition parameters include, but are not limited to: wind speed, water flow rate, light intensity. The undetermined parameters are weights of conditional parameters, and are respectively: c 1 、c 2 、c 3 、c 4 And c 5 The weight of temperature, the weight of illumination intensity, the weight of nutrient salt, the weight of natural mortality in the life cycle and the weight of the death rate of the stormy waves to destroy the suspension balance of green tide are respectively.
S120, 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 so as to obtain the green tide biomass estimation model after the parameter determination.
The reference distribution data is determined according to satellite remote sensing images.
The target region is a region in which the growth environment meets the growth conditions of green tide, and green tide growing in a large area occurs in the region. The satellite remote sensing image is an image in a target area obtained through a satellite, the image can judge the quantity of green tides through the color darkness, the greater the green tide density is at the darker place on the satellite remote sensing image, the greater the corresponding quantity of green tides is, the lesser the green tide density is at the lighter place, and the corresponding quantity of green tides is also less.
In this embodiment, reference distribution data of green tide biomass in a target area is determined through a satellite remote sensing image, and then the reference distribution data is substituted into an 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; wherein 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 so as to obtain the green tide biomass estimation model after the harvest, thereby winning more time for coping with the green tide disaster and reducing the loss of the green tide disaster.
Example two
Fig. 2 is a flow chart of a green tide biomass forecasting model establishing method according to a second embodiment of the present invention, where the technical solution is further optimized based on 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; wherein the green tide biomass estimation model comprises at least one undetermined parameter.
In the embodiment of the invention, the green tide biomass estimation model which is initially constructed is determined by adopting the following formula:
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 current coefficient, and gamma represents distribution diffusion coefficient. S is green tide biomass generated in unit area of unit time, P is the growth rate of green tide, and D is the death rate of green tide;
in the above formula, the growth rate P of green tide adopts the following formula:
wherein c 1 、c 2 And c 3 Indicating the parameter to be determined, T indicating the temperature,T 0 represents the historical average temperature, L, of seawater in the green tide growing season 0 Represents the historical average illumination intensity of the sea surface in the green tide growing season, H N A limiting nutrient salt half-saturation constant representing green tide growth;
in the above formula, the mortality rate D of green tide is obtained by adopting the following formula:
wherein I represents the life cycle of green tide in days, H L Representing half life cycle in days, W representing sea surface wind speed, W 0 Represents the historical average wind speed in green tide growing season, c 4 Representing natural mortality over the lifecycle, c 5 The mortality rate of the stormy waves in breaking the green tide suspension balance is shown.
S220, 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 so as to obtain the determined green tide biomass estimation model, wherein the reference distribution data is determined according to a satellite remote sensing image.
Optionally, the step of determining the reference distribution data includes:
acquiring a photographed satellite remote sensing image of a target area;
performing gridding treatment 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 this embodiment, the meshing process in the target area refers to dividing the target area into horizontal and vertical meshes. Further, initial distribution data is plotted in a grid, with the horizontal axis being the location coordinates of the region and the vertical axis being 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 the 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 a unit grid in a target area into a grid with green tide and a grid without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold value;
clearing the distribution data in the grid without green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with green tide; if the distributed data of the uniformly distributed green tide grids are 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 the image parameters of the satellite remote sensing image in each unit grid, and specifically, the reference distribution data is calculated by the following formula:
wherein G is i,j (x, y) represents the distribution of green tide biomass in a unit grid, x i,j ，y i,j Representing coordinates of the centroid of the unit mesh.
Wherein the preset distribution lower limit threshold value is G i,j And (3) comparing the reference distribution data with a preset distribution lower limit threshold value by one numerical value in (x, y), taking a unit grid corresponding to the data smaller than the preset distribution lower limit threshold value in the reference distribution data as a green tide-free grid, and taking a unit grid corresponding to the data larger than the preset distribution lower limit threshold value in the reference distribution data as a green tide-free grid.
The advantage of integrating the distribution data in the grid without green tide and distributing the data to the grid with green tide is that by distributing the green tide to the grid with green tide, the influence of the grid with less green tide on the surrounding environment can be taken into account.
If the distribution data of the uniformly-spread grid with green tide is larger than the preset distribution upper limit threshold value, the green tide in the grid is concentrated, and the green tide grows to a certain thickness according to the growth rule of the green tide and then is diffused to the periphery, so that the part larger than the preset distribution upper limit threshold value is uniformly spread to other grids with green tide, and the reference distribution data of the unit grid in the target area is redrawn.
Preferably, the number may be pre-numbered according to the number of green-damp patches in the target area and the centroid of each patch determined. Illustratively, the green tide patch numbered 1 covers four green tide grids A, B, C and D, respectively, wherein the green tide biomass in grid a is below a preset distribution lower threshold, the green tide biomass in grid B is above a preset distribution upper threshold, and the green tide biomass in grid C and grid D is between the preset distribution lower threshold and the preset distribution upper threshold. The green tide biomass in grid a is summarized and redistributed to either grid C or grid D. 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 which is distributed in the grid D is larger than the preset distribution upper limit threshold value.
S230, forecasting the green tide biomass in the target area by adopting a green tide biomass estimation model after parameter setting.
In this embodiment, the green tide biomass estimation model after parameter determination can predict green tide biomass in the next year or years of the area.
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 further, a more accurate green tide biomass forecasting model after the undetermined parameters is obtained, so that the green tide biomass is predicted through the green tide biomass forecasting model after the undetermined parameters, more time is won for coping with the 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 forecasting model building device according to a third embodiment of the present invention. The green tide biomass forecasting model building device provided by the embodiment of the invention can be used for executing the green tide biomass forecasting model building method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method. As shown in fig. 3, the apparatus includes:
the green tide biomass estimation model determining module 301 is configured to determine an initially constructed green tide biomass estimation model; wherein the green tide biomass estimation model comprises at least one undetermined parameter;
the undetermined parameter determining module 302 is configured to determine a 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 a target area, so as to obtain a green tide biomass estimation model after the parameters are determined according to a satellite remote sensing image.
The apparatus further comprises:
the green tide biomass forecasting module 303 is configured to forecast green tide biomass in the target area by using the green tide biomass forecasting model after parameter setting.
The pending parameter determining module 302 is specifically configured to:
acquiring a photographed satellite remote sensing image of a target area;
performing gridding treatment 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 a unit grid in a target area into a grid with green tide and a grid without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold value;
clearing the distribution data in the grid without green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with green tide; if the distributed data of the uniformly distributed green tide grids are 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 by the following formula:
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 current coefficient, and gamma represents distribution diffusion coefficient. S is green tide biomass generated in unit area of unit time, P is the growth rate of green tide, and D is the death rate of green tide;
in the above formula, the growth rate P of green tide adopts the following formula:
wherein c 1 、c 2 And c 3 Represents a pending parameter, T represents temperature, T 0 Represents the historical average temperature, L, of seawater in the green tide growing season 0 Represents the historical average illumination intensity of the sea surface in the green tide growing season, H N A limiting nutrient salt half-saturation constant representing green tide growth;
in the above formula, the mortality rate D of green tide is obtained by adopting the following formula:
wherein I represents the life cycle of green tide in days, H L Representing half life cycle in days, W representing sea surface wind speed, W 0 Represents the historical average wind speed in green tide growing season, c 4 Representing natural mortality over the lifecycle, c 5 The mortality rate of the stormy waves in breaking the green tide suspension balance is shown.
It will 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, which is not repeated herein.
Example IV
Fig. 4 is a schematic structural diagram of an apparatus provided in a fourth embodiment of the present invention, and fig. 4 shows a schematic structural diagram of an exemplary apparatus suitable for implementing an embodiment of the present invention. The device 12 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 4, device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include 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 can 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 or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules 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 in, for example, 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 or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with device 12, and/or any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, via network adapter 20. As shown in fig. 4, network adapter 20 communicates with other modules of device 12 over bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, to implement a green tide biomass forecasting model building method provided by an embodiment of the present invention, including:
determining an initially constructed green tide biomass estimation model; wherein 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 so as to obtain the green tide biomass estimation model after the determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium, on which a computer program (or called computer executable instructions) is stored, where the program when executed by a processor can implement a green tide biomass forecasting model building method according to any of the foregoing embodiments, including:
determining an initially constructed green tide biomass estimation model; wherein 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 so as to obtain the green tide biomass estimation model after the determination, wherein the reference distribution data is determined according to the satellite remote sensing image.
The computer storage media of embodiments of the invention may take the form of 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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 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.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. 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 ++ 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. 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, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (7)
1. A method for building a green tide biomass forecasting model, comprising the steps of:
determining an initially constructed green tide biomass estimation model; wherein the green tide biomass estimation model comprises at least one undetermined parameter;
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 a green tide biomass estimation model after the determination, wherein the reference distribution data is determined according to a satellite remote sensing image;
the step of determining the reference distribution data includes:
acquiring a photographed satellite remote sensing image of a target area;
performing gridding treatment on the satellite remote sensing image of the target area to obtain a unit grid in the target area;
determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid;
the reference distribution data is determined according to the image parameters of the satellite remote sensing image in each unit grid, and specifically, the reference distribution data is calculated through the following formula:
wherein G is i,j (x, y) represents the distribution of green tide biomass in a unit grid, x i,j ，y i,j Coordinates representing the centroid of the unit mesh;
the determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid comprises the following steps:
determining initial reference distribution data according to image parameters of the satellite remote sensing image in each unit grid;
dividing a unit grid in a target area into a grid with green tide and a grid without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold value;
clearing the distribution data in the grid without green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with green tide; if the distributed data of the uniformly distributed green tide grids are 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.
2. The method of claim 1, wherein after determining the pre-determined green tide biomass estimation model, the method further comprises:
and forecasting the green tide biomass in the target area by adopting a green tide biomass estimation model after parameter setting.
3. The method of claim 1, wherein the initially constructed green tide biomass estimation model uses the formula:
wherein G represents green tide biomass in unit area, t represents time, U, V represents flow velocity, U, V represents wind speed, e represents wind current coefficient, gamma represents distribution diffusion coefficient, S represents green tide biomass generated in unit area in unit time, P represents growth rate of green tide, and D represents death rate of green tide;
in the above formula, the growth rate P of green tide adopts the following formula:
wherein c 1 、c 2 And c 3 Represents a pending parameter, T represents temperature, T 0 Represents the historical average temperature, L, of seawater in the green tide growing season 0 Represents the historical average illumination intensity of the sea surface in the green tide growing season, H N A limiting nutrient salt half-saturation constant representing green tide growth;
in the above formula, the mortality rate D of green tide is obtained by adopting the following formula:
wherein I represents the life cycle of green tide in days, H L Representing half life cycle in days, W representing sea surface wind speed, W 0 Represents the historical average wind speed in green tide growing season, c 4 Representing natural mortality over the lifecycle, c 5 The mortality rate of the stormy waves in breaking the green tide suspension balance is shown.
4. A green tide biomass forecasting model building device, characterized by comprising:
the green tide biomass estimation model determining module is used for determining an initially constructed green tide biomass estimation model; wherein the green tide biomass estimation model comprises at least one undetermined parameter;
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 the reference distribution data of the green tide biomass in the target area so as to obtain the green tide biomass estimation model after the parameter determination, wherein the reference distribution data is determined according to the satellite remote sensing image;
the undetermined parameter determining module is specifically configured to:
acquiring a photographed satellite remote sensing image of a target area;
performing gridding treatment on the satellite remote sensing image of the target area to obtain a unit grid in the target area;
determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid;
the reference distribution data is determined according to the image parameters of the satellite remote sensing image in each unit grid, and specifically, the reference distribution data is calculated through the following formula:
wherein G is i,j (x, y) represents the distribution of green tide biomass in a unit grid, x i,j ，y i,j Coordinates representing the centroid of the unit mesh;
the determining the reference distribution data according to the image parameters of the satellite remote sensing image in each unit grid comprises the following steps:
determining initial reference distribution data according to image parameters of the satellite remote sensing image in each unit grid;
dividing a unit grid in a target area into a grid with green tide and a grid without green tide according to the size relation between the initial reference distribution data and a preset distribution lower limit threshold value;
clearing the distribution data in the grid without green tide, and summarizing and uniformly spreading the cleared distribution data to the grid with green tide; if the distributed data of the uniformly distributed green tide grids are 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 apparatus of claim 4, wherein the apparatus further comprises:
and the green tide biomass forecasting module is used for forecasting the green tide biomass in the target area by adopting a green tide biomass forecasting model after parameter setting.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the green tide biomass forecasting model establishment method of any one of claims 1-3 when the program is executed.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements a green tide biomass forecasting model establishment method as claimed in any one of claims 1 to 3.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103743700A (en) * | 2014-01-17 | 2014-04-23 | 中国科学院南京地理与湖泊研究所 | High-precision monitoring method for cyanobacterial blooms in large shallow lake through MODIS (Moderate Resolution Imaging Spectroradiometer) and satellite |
| KR101421197B1 (en) * | 2013-05-15 | 2014-08-13 | 김혜봉 | Method and apparatus for forecasting generation of green tide and red tide based on yellow sand phenomenon |
| KR20150117971A (en) * | 2014-04-11 | 2015-10-21 | 한국해양과학기술원 | Method and system for predicting ocean circulation and wave in coastal areas |
| CN105427305A (en) * | 2015-11-19 | 2016-03-23 | 国家海洋局北海预报中心 | Green tide information extraction method |
| KR101866703B1 (en) * | 2017-02-17 | 2018-06-12 | 성균관대학교산학협력단 | Method for selecting a location for water intake of a plant using satellite information and current information |
| CN108764168A (en) * | 2018-05-31 | 2018-11-06 | 合肥工业大学 | The method and system of target are moved in multi-obstacle avoidance surface-search for imaging satellite |
| CN109326086A (en) * | 2018-10-16 | 2019-02-12 | 天津科技大学 | A kind of green tide disaster early warning system and its application method based on ecological model |
-
2019
- 2019-12-09 CN CN201911249340.7A patent/CN111027766B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101421197B1 (en) * | 2013-05-15 | 2014-08-13 | 김혜봉 | Method and apparatus for forecasting generation of green tide and red tide based on yellow sand phenomenon |
| CN103743700A (en) * | 2014-01-17 | 2014-04-23 | 中国科学院南京地理与湖泊研究所 | High-precision monitoring method for cyanobacterial blooms in large shallow lake through MODIS (Moderate Resolution Imaging Spectroradiometer) and satellite |
| KR20150117971A (en) * | 2014-04-11 | 2015-10-21 | 한국해양과학기술원 | Method and system for predicting ocean circulation and wave in coastal areas |
| CN105427305A (en) * | 2015-11-19 | 2016-03-23 | 国家海洋局北海预报中心 | Green tide information extraction method |
| KR101866703B1 (en) * | 2017-02-17 | 2018-06-12 | 성균관대학교산학협력단 | Method for selecting a location for water intake of a plant using satellite information and current information |
| CN108764168A (en) * | 2018-05-31 | 2018-11-06 | 合肥工业大学 | The method and system of target are moved in multi-obstacle avoidance surface-search for imaging satellite |
| CN109326086A (en) * | 2018-10-16 | 2019-02-12 | 天津科技大学 | A kind of green tide disaster early warning system and its application method based on ecological model |
Non-Patent Citations (4)
| Title |
|---|
| Arcgis矢量图层符号化方法在绿潮分布显示中的应用;辛蕾;王宁;钟山;胡伟;孙青;;海洋开发与管理(04);第38-40, 104页 * |
| 基于地面观测的遥感监测蒸散量验证方法研究;贾贞贞;刘绍民;毛德发;王志良;徐自为;张茹;;地球科学进展(11);第1248-1260页 * |
| 李大鸣 ; 林毅 ; 宋双霞 ; 解以扬 ; 韩素琴 ; 李培彦 ; .二维赤潮生态数学模型及其在渤海的应用.海洋科学.2010,(09),第87-93页. * |
| 辛蕾 ; 黄娟 ; 刘荣杰 ; 钟山 ; 肖艳芳 ; 王宁 ; 崔廷伟 ; .基于混合像元分解的MODIS绿潮覆盖面积精细化提取方法研究.激光生物学报.2014,(06),第585-589页. * |
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