Ecological system service prediction method and device, computer equipment and storage medium
By downscaled the climate raster data and building a land use weight matrix, combining land use distribution and ecosystem service evaluation model, the prediction result deviation caused by insufficient data accuracy is solved, and more accurate ecosystem service prediction is achieved.
Patent Information
- Application Number
- CN202510327376.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-07-29
AI Technical Summary
In the prior art, the insufficient data accuracy of ecosystem service prediction leads to a large deviation in the prediction results.
The Kriging interpolation method is used to downscale the climate raster data, and combined with the land use type weight matrix and the comprehensive evaluation model, it is predicted through the land use distribution prediction model and the ecosystem service evaluation model.
Improve the accuracy of ecosystem service prediction and reduce the deviation of prediction results.
Smart Images
Figure CN120387534A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of ecosystem service analysis, and particularly to an ecosystem service prediction method, device, computer device, and storage medium. Background Art
[0002] The diversity and stability of ecosystems are crucial for the overall ecological balance. The interactions and dependencies among various organisms maintain the stability of ecosystems, preventing the over - reproduction or over - disappearance of certain species. The destruction of ecosystems may lead to the loss of biodiversity, thus affecting the entire food chain and ecological balance. Summary of the Invention
[0003] Based on this, the object of the present invention is to provide an ecosystem service prediction method, device, computer device, and storage medium. Based on the downscaled climate raster data of the target period and the land - use - related raster data of the target period and the historical period, predict the land - use distribution in the prediction period to obtain the land - use distribution prediction raster data of the prediction period. Based on the downscaled climate raster data of the target period and the land - use distribution prediction raster data of the prediction period, predict the ecosystem service situation in the prediction period to obtain the ecosystem service prediction raster data of the prediction period, overcoming the problem of large deviation in prediction results caused by insufficient data accuracy and improving the accuracy of ecosystem service prediction.
[0004] In the first aspect, an embodiment of the present application provides an ecosystem service prediction method, including the following steps:
[0005] Obtain the climate raster data, land - use type raster data of the target period of the target area, and the land - use type raster data of the historical period;
[0006] Adopt the Kriging interpolation method to perform downscaling processing on the climate raster data of the target period to obtain the downscaled climate raster data of the target period;
[0007] Construct a land - use type weight matrix according to the land - use type raster data of the target period and the historical period;
[0008] Input the land - use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land - use type weight matrix into a preset land - use distribution prediction model for land - use distribution prediction to obtain the land - use distribution prediction raster data of the prediction period of the target area;
[0009] Input the downscaled climate raster data for the target period and the predicted land use distribution raster data for the prediction period into a preset comprehensive assessment model of ecosystem services and trade-offs for ecosystem service prediction, to obtain the predicted ecosystem service raster data for the prediction period of the target area.
[0010] In a second aspect, an embodiment of the present application provides an ecosystem service prediction device, including:
[0011] A data acquisition module, configured to acquire the climate raster data, land use type raster data for the target period, and land use type raster data for the historical period of the target area;
[0012] A data downscaling module, configured to use the Kriging interpolation method to downscale the climate raster data for the target period to obtain the downscaled climate raster data for the target period;
[0013] A matrix construction module, configured to construct a land use type weight matrix according to the land use type raster data for the target period and the historical period;
[0014] A first prediction module, configured to input the land use type raster data for the target period and the historical period, the downscaled climate raster data for the target period, and the land use type weight matrix into a preset land use distribution prediction model for land use distribution prediction, to obtain the predicted land use distribution raster data for the prediction period of the target area;
[0015] A second prediction module, configured to input the downscaled climate raster data for the target period and the predicted land use distribution raster data for the prediction period into a preset comprehensive assessment model of ecosystem services and trade-offs for ecosystem service prediction, to obtain the predicted ecosystem service raster data for the prediction period of the target area.
[0016] In a third aspect, an embodiment of the present application provides a computer device, including: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the ecosystem service prediction method as described in the first aspect.
[0017] In a fourth aspect, an embodiment of the present application provides a storage medium, the storage medium stores a computer program, and when the computer program is executed by a processor, it implements the steps of the ecosystem service prediction method as described in the first aspect.
[0018] In an embodiment of the present application, an ecosystem service prediction method, device, computer device, and storage medium are provided. Based on the downscaled climate raster data in the target period and the land use related raster data in the target period and the historical period, the land use distribution in the prediction period is predicted to obtain the land use distribution prediction raster data in the prediction period. Based on the downscaled climate raster data in the target period and the land use distribution prediction raster data in the prediction period, the ecosystem service situation in the prediction period is predicted to obtain the ecosystem service prediction raster data in the prediction period, overcoming the problem of large deviation in the prediction result caused by insufficient data accuracy and improving the accuracy of ecosystem service prediction.
[0019] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 It is a schematic flowchart of an ecosystem service prediction method provided by an embodiment of the present application;
[0021] Figure 2 It is a schematic flowchart of S2 in the ecosystem service prediction method provided by an embodiment of the present application;
[0022] Figure 3 It is a schematic flowchart of S3 in the ecosystem service prediction method provided by an embodiment of the present application;
[0023] Figure 4 It is a schematic flowchart of S4 in the ecosystem service prediction method provided by an embodiment of the present application;
[0024] Figure 5 It is a schematic flowchart of S5 in the ecosystem service prediction method provided by an embodiment of the present application;
[0025] Figure 6 It is a schematic structural diagram of an ecosystem service prediction device provided by an embodiment of the present application;
[0026] Figure 7 It is a schematic structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0027] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.
[0028] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit the application. The singular forms "a", "the", and "said" used in this application and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
[0029] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".
[0030] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an ecosystem service prediction method provided for an embodiment of this application. The method includes the following steps:
[0031] S1: Obtain the climate raster data, land use type raster data of the target period of the target area, and the land use type raster data of the historical period.
[0032] The execution entity of the ecosystem service prediction method is a prediction device for the ecosystem service prediction method (hereinafter referred to as the prediction device). In an optional embodiment, the prediction device may be a computer device, which may be a server, or a server cluster formed by combining multiple computer devices.
[0033] The prediction device can obtain the climate raster data, land use type raster data of the target period of the target area, and the land use type raster data of the historical period. Specifically, there is a preset unit time interval between the target period and the historical period, and the unit time may be 5 years or 10 years. Among them, the climate raster data includes climate data of several rasters; the land use type raster data includes land use type data of several rasters.
[0034] In an optional embodiment, the prediction device performs radiometric correction, geometric correction, and geometric registration processing on the multispectral remote sensing image to obtain the processed multispectral remote sensing image, which is used to correct the multispectral remote sensing image of the area to be detected collected by the satellite and improve the accuracy of extracting the band information of the multispectral remote sensing image.
[0035] S2: Use the Kriging interpolation method to downscale the climate raster data of the target period to obtain the downscaled climate raster data of the target period.
[0036] To overcome the problem of large deviation in prediction results caused by insufficient data accuracy, in this embodiment, the prediction device uses the Kriging interpolation method to downscale the climate raster data of the target period to obtain the downscaled climate raster data of the target period.
[0037] Please refer to Figure 2 , Figure 2 FIG. is a schematic flowchart of S2 in the ecosystem service prediction method provided by an embodiment of the present application, including steps S21 to S22, specifically as follows:
[0038] S21: Convert the climate raster data of the target period into points to obtain the climate point data of the target period.
[0039] In this embodiment, the prediction device converts the climate raster data of the target period into points to obtain the climate point data of the target period, where the number of climate points includes the initial climate data of several points;
[0040] S22: Obtain the climate data of several grids around several points according to the preset number of grids, perform cumulative summation according to the climate data of several grids around several points and the preset climate weight coefficients of several grids to obtain the target climate data of several points, and combine the target climate data of several points to construct the downscaled climate raster data of the target period.
[0041] In this embodiment, the prediction device obtains the climate data of several grids around several points according to the preset number of grids, performs cumulative summation according to the climate data of several grids around several points and the preset climate weight coefficients of several grids to obtain the target climate data of several points, specifically as follows:
[0042]
[0043] In the formula, is the target climate data of the current point, n is the number of grids, z i is the climate data of the i-th grid around the current point, λ i is the weight parameter of the i-th grid around the current point, and the weight parameters of different grids are different. In an alternative embodiment, the weight parameter of the grid can adopt the coefficient in the Kriging module embedded in ArcGIS.
[0044] The prediction device combines the target climate data of the several points to construct the downscaled climate raster data for the target period, improving the data accuracy. At the same time, the physical parameters based on the secondary classification further subdivide the single land use data parameters for ecosystem service prediction, improving the prediction accuracy.
[0045] S3: Construct a land use type weight matrix according to the land use type raster data of the target period and the historical period.
[0046] In this embodiment, the prediction device constructs a land use type weight matrix according to the land use type raster data of the target period and the historical period.
[0047] Please refer to Figure 3 , Figure 3 which is a schematic flowchart of S3 in the ecosystem service prediction method provided by an embodiment of the present application, including steps S31 to S32, specifically as follows:
[0048] S31: Obtain the land use type change area data according to the land use type raster data of the target period and the historical period and the preset raster area.
[0049] In this embodiment, the prediction device analyzes whether the land use type has changed based on the land use type raster data of the historical period and the target period of the same raster according to the land use type raster data of the target period and the historical period and the preset raster area, determines several rasters where the land use type has changed, obtains several rasters where several types of land use types have changed, and respectively multiplies and accumulates them with the preset raster area to obtain the land use type change area data, where the land use type change area data includes the change areas of several types of land use types.
[0050] S32: Perform normalization processing on the land use type change area data to obtain the weight parameters of several types of land use types, and construct the land use type domain weight matrix.
[0051] In this embodiment, the prediction device performs normalization processing on the land use type change area data to obtain the weight parameters of several types of land use types, and constructs the land use type domain weight matrix, where the land use type domain weight matrix includes the domain weight parameters of several types of land use types, and the domain weight parameters of the land use type are:
[0052]
[0053] where W i is the domain weight parameter of the i-th type of land use type, TA iis the changed area of the i-th land use type, TA min is the changed area of the land use type with the smallest changed area in the land use type changed area data, TA max is the changed area of the land use type with the largest changed area in the land use type changed area data.
[0054] S4: Input the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix into a preset land use distribution prediction model for land use distribution prediction, to obtain the land use distribution prediction raster data of the prediction period in the target area.
[0055] The land use distribution prediction model adopts the PLUS (Patch-level Land Use Change Simulation) model, which integrates a land expansion strategy analysis module and a cellular automaton model based on multi-class random patch seeds, aiming to simulate future land use changes. By deeply interpreting the complex relationships between land uses, the PLUS model can more accurately predict the land use change trend and analyze relevant strategies.
[0056] In this embodiment, the prediction device inputs the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix into a preset land use distribution prediction model for land use distribution prediction, to obtain the land use distribution prediction raster data of the prediction period in the target area, where the prediction period is a future period based on the target time interval unit time.
[0057] The land use distribution prediction model includes a land use expansion prediction module, a land use expansion analysis strategy module, a land use area prediction module, and a land use distribution prediction module. Please refer to Figure 4 , Figure 4 is the process schematic diagram of S4 in the ecosystem service prediction method provided by an embodiment of the present application, including steps S41 to S44, specifically as follows:
[0058] S41: Input the land use type raster data of the target period and the historical period into the land use expansion prediction module for land use expansion prediction, to obtain the land use expansion raster data between the historical period and the target period.
[0059] In this embodiment, the prediction device inputs the land use type raster data of the target period and the historical period into the land use expansion prediction module (Extract Land Expansion) for land use expansion prediction, and obtains the land use expansion raster data between the historical period and the target period. Among them, the land use expansion raster data includes the land use type change data of several rasters, and the land use type change data is used to indicate whether the land use type of the corresponding raster has changed.
[0060] S42: Input the downscaled climate raster data of the target period and the land use expansion raster data between the historical period and the target period into the land use expansion analysis strategy module to calculate the land development probability, and obtain the land development probability raster data between the target period and the prediction period.
[0061] In this embodiment, the prediction device inputs the downscaled climate raster data of the target period and the land use expansion raster data between the historical period and the target period into the land use expansion analysis strategy module (Land Expansion Analysis Strategy, LEAS) to calculate the land development probability, and obtains the land development probability raster data between the target period and the prediction period. Among them, the land development probability raster data includes the land development probability data of several rasters.
[0062] S43: Input the land use type raster data of the target period and the historical period into the land use area prediction module to predict the land use type area, and obtain the land use area prediction data between the target period and the prediction period.
[0063] In this embodiment, the prediction device inputs the land use type raster data of the target period and the historical period into the land use area prediction module (Markov-Chain) to predict the land use type area, and obtains the land use area prediction data between the target period and the prediction period. Among them, the land use area prediction data includes the predicted areas of several land use types;
[0064] S44: Input the land use type weight matrix, the land development probability raster data between the target period and the prediction period, and the land use area prediction data into the land use distribution prediction module to perform land use distribution prediction, and obtain the land use distribution prediction raster data of the prediction period.
[0065] In this embodiment, the prediction device inputs the land use type weight matrix, the land development probability raster data between the target period and the prediction period, and the land use area prediction data into the land use distribution prediction module for land use distribution prediction. The land use distribution prediction module is a CA model based on multiple random patches seeds (CARS), and obtains the land use distribution prediction raster data for the prediction period. Among them, the land use distribution prediction raster data includes the land use type prediction data of several grids, and the land use type prediction data is used to indicate the land use type of the corresponding grid.
[0066] S5: Input the downscaled climate raster data for the target period and the land use distribution prediction raster data for the prediction period into a preset comprehensive evaluation model of ecosystem services and trade-offs for ecosystem service prediction, and obtain the ecosystem service prediction raster data for the prediction period of the target area.
[0067] The comprehensive evaluation model of ecosystem services and trade-offs, InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), integrates various biophysical and socioeconomic data and provides a series of ecosystem service evaluation capabilities.
[0068] In this embodiment, the prediction device inputs the downscaled climate raster data for the target period and the land use distribution prediction raster data for the prediction period into a preset comprehensive evaluation model of ecosystem services and trade-offs for ecosystem service prediction, and obtains the ecosystem service prediction raster data for the prediction period of the target area.
[0069] Based on the downscaled climate raster data for the target period and the land use related raster data between the target period and the historical period, predict the land use distribution situation for the prediction period, and obtain the land use distribution prediction raster data for the prediction period. Based on the downscaled climate raster data for the target period and the land use distribution prediction raster data for the prediction period, predict the ecosystem service situation for the prediction period, and obtain the ecosystem service prediction raster data for the prediction period, which overcomes the problem of large deviation in prediction results caused by insufficient data accuracy and improves the accuracy of ecosystem service prediction.
[0070] The comprehensive evaluation model of ecosystem services and trade-offs includes a rainfall erosion analysis module, an annual water yield module, and an ecosystem service prediction module. Please refer to Figure 5 , Figure 5It is a schematic flowchart of S5 in the ecosystem service prediction method provided by an embodiment of the present application, including steps S51 to S53, which are specifically as follows:
[0071] S51: Input the downscaled climate raster data of the target period into the rainfall erosion analysis module to calculate the rainfall erosion factor, and obtain the rainfall erosion factor raster data of the target period.
[0072] In this embodiment, the prediction device inputs the downscaled climate raster data of the target period into the rainfall erosion analysis module to calculate the rainfall erosion factor, and obtains the rainfall erosion factor raster data of the target period. Among them, the rainfall erosion factor raster data includes the rainfall erosion factor data of several grids, and the rainfall erosion factor data is:
[0073]
[0074] In the formula, R W is the rainfall erosion factor data of the current grid, P i is the rainfall of the i-th month of the current grid, and P is the annual average rainfall of the current grid.
[0075] S52: Input the rainfall erosion factor raster data of the target period and the predicted land use distribution raster data of the prediction period into the annual water yield module to predict the annual water yield, and obtain the annual water yield raster data of the prediction period.
[0076] In this embodiment, the prediction device inputs the rainfall erosion factor raster data of the target period and the predicted land use distribution raster data of the prediction period into the annual water yield module to predict the annual water yield, and obtains the annual water yield raster data of the prediction period. Among them, the annual water yield raster data includes the annual water yield data of several grids.
[0077] S53: Input the annual water yield raster data of the prediction period into the ecosystem service prediction module to predict the ecosystem service, and obtain the ecosystem service prediction raster data of the prediction period.
[0078] In this embodiment, the prediction device inputs the annual water yield raster data of the prediction period into the ecosystem service prediction module to predict the ecosystem service, and obtains the ecosystem service prediction raster data of the prediction period. Among them, the ecosystem service prediction raster data includes the ecosystem service prediction data of several grids.
[0079] Please refer to Figure 6 , Figure 6Schematic diagram of the structure of an ecosystem service prediction device provided by an embodiment of the present application. The device can implement all or part of the ecosystem service prediction device through software, hardware, or a combination of both. The device includes:
[0080] A data acquisition module 61, configured to acquire climate raster data, land use type raster data of a target period of a target area, and land use type raster data of a historical period;
[0081] A data downscaling module 62, configured to perform downscaling processing on the climate raster data of the target period by using the Kriging interpolation method to obtain the downscaled climate raster data of the target period;
[0082] A matrix construction module 63, configured to construct a land use type weight matrix according to the land use type raster data of the target period and the historical period;
[0083] A first prediction module 64, configured to input the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix into a preset land use distribution prediction model to perform land use distribution prediction, and obtain the land use distribution prediction raster data of the prediction period of the target area;
[0084] A second prediction module 65, configured to input the downscaled climate raster data of the target period and the land use distribution prediction raster data of the prediction period into a preset comprehensive evaluation model of ecosystem services and trade-offs to perform ecosystem service prediction, and obtain the ecosystem service prediction raster data of the prediction period of the target area.
[0085] In the embodiment of the present application, through a data acquisition module, climate raster data, land use type raster data of a target period, and land use type raster data of a historical period of a target area are acquired; through a data downscaling module, the Kriging interpolation method is used to downscale the climate raster data of the target period to obtain the downscaled climate raster data of the target period; through a matrix construction module, a land use type weight matrix is constructed according to the land use type raster data of the target period and the historical period; through a first prediction module, the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix are input into a preset land use distribution prediction model for land use distribution prediction to obtain land use distribution prediction raster data of a prediction period of the target area; through a second prediction module, the downscaled climate raster data of the target period and the land use distribution prediction raster data of the prediction period are input into a preset comprehensive evaluation model of ecosystem services and trade-offs for ecosystem service prediction to obtain ecosystem service prediction raster data of the prediction period of the target area. Based on the downscaled climate raster data of the target period and the land use-related raster data of the target period and the historical period, the land use distribution situation of the prediction period is predicted to obtain the land use distribution prediction raster data of the prediction period. Based on the downscaled climate raster data of the target period and the land use distribution prediction raster data of the prediction period, the ecosystem service situation of the prediction period is predicted to obtain the ecosystem service prediction raster data of the prediction period, which overcomes the problem of large deviation in prediction results caused by insufficient data accuracy and improves the accuracy of ecosystem service prediction.
[0086] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of a computer device provided by an embodiment of the present application. The computer device 7 includes: a processor 71, a memory 72, and a computer program 73 stored on the memory 72 and executable on the processor 71; the computer device may store multiple instructions, and the instructions are suitable for being loaded and executed by the processor 71 to perform the method steps of the above Figures 1 to 5 illustrated embodiment. The specific execution process may refer to the specific description of the Figures 1 to 5 illustrated embodiment and will not be elaborated here.
[0087] Among them, the processor 71 may include one or more processing cores. The processor 71 uses various interfaces and circuits to connect various parts within the server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 72, and by invoking the data stored in the memory 72, it executes various functions of the ecosystem service prediction device 6 and processes data. Optionally, the processor 71 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 71 may integrate one or a combination of several of the central processing unit (CPU), graphics processing unit (GPU), and modem, etc. Among them, the CPU mainly processes the operating system, user interface, application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the touch display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 71 and may be implemented separately by a single chip.
[0088] Among them, the memory 72 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 72 includes a non-transitory computer-readable storage medium. The memory 72 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 72 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing the operating system, instructions for at least one function (such as touch instructions, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store the data involved in the above-mentioned method embodiments. Optionally, the memory 72 may also be at least one storage device located far from the aforementioned processor 71.
[0089] The embodiment of the present application also provides a storage medium, which can store multiple instructions. The instructions are suitable for being loaded and executed by the processor to perform the method steps of the above Figures 1 to 5 illustrated embodiment. The specific execution process can be referred to Figures 1 to 5 the specific description of the illustrated embodiment, and details will not be repeated here.
[0090] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.
[0091] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0092] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.
[0093] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the module or unit is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.
[0094] The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0095] In addition, in each embodiment of the present invention, each functional unit may be integrated in a processing unit, may exist physically alone for each unit, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.
[0096] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it may also be completed by a computer program instructing related hardware. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc.
[0097] The present invention is not limited to the above embodiments. If various modifications or deformations of the present invention do not depart from the spirit and scope of the present invention, and if these modifications and deformations fall within the scope of the claims of the present invention and equivalent technical scope, then the present invention also intends to include these modifications and deformations.
Claims
1. An ecosystem service prediction method, characterized in that, The method includes the following steps: Obtain the climate raster data, land use type raster data of the target period in the target area, and land use type raster data of the historical period; Use the Kriging interpolation method to downscale the climate raster data of the target period to obtain the downscaled climate raster data of the target period; Construct a land use type weight matrix according to the land use type raster data of the target period and the historical period; Input the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix into a preset land use distribution prediction model for land use distribution prediction, to obtain the land use distribution prediction raster data of the prediction period in the target area; Input the downscaled climate raster data of the target period and the land use distribution prediction raster data of the prediction period into a preset comprehensive assessment model of ecosystem services and trade-offs for ecosystem service prediction, to obtain the ecosystem service prediction raster data of the prediction period in the target area.
2. The ecosystem service prediction method according to claim 2, wherein: The climate raster data includes climate data of a number of grids; The step of using the Kriging interpolation method to downscale the climate raster data of the target period to obtain the downscaled climate raster data of the target period includes the steps of: Convert the climate raster data of the target period from raster to points to obtain the climate point data of the target period, where the number of climate points includes the initial climate data of a number of points; Obtain the climate data of a number of grids around a number of points according to the preset number of grids, perform cumulative summation according to the climate data of a number of grids around a number of points and the preset climate weight coefficients of a number of grids to obtain the target climate data of a number of points, and combine the target climate data of a number of points to construct the downscaled climate raster data of the target period.
3. The ecosystem service prediction method according to claim 1, characterized in that: The land use type raster data includes land use type data of a number of grids; The step of constructing a land use type weight matrix according to the land use type raster data of the target period and the historical period includes the steps of: Obtain the land use type change area data according to the land use type raster data of the target period and the historical period and the preset grid area; Perform normalization processing on the land use type change area data to obtain the weight parameters of several land use types, and construct the land use type domain weight matrix.
4. The ecosystem service prediction method according to claim 3, wherein: The land use distribution prediction model includes a land use expansion prediction module, a land use expansion analysis strategy module, a land use area prediction module, and a land use distribution prediction module; The step of inputting the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix into a preset land use distribution prediction model for land use distribution prediction, to obtain the land use distribution prediction raster data of the prediction period in the target area includes the steps of: Input the land use type raster data of the target period and the historical period into the land use expansion prediction module for land use expansion prediction, and obtain the land use expansion raster data between the historical period and the target period; Input the downscaled climate raster data of the target period and the land use expansion raster data between the historical period and the target period into the land use expansion analysis strategy module for land development probability calculation, and obtain the land development probability raster data between the target period and the prediction period; Input the land use type raster data of the target period and the historical period into the land use area prediction module for land use type area prediction, and obtain the land use area prediction data between the target period and the prediction period; Input the land use type weight matrix, the land development probability raster data between the target period and the prediction period, and the land use area prediction data into the land use distribution prediction module for land use distribution prediction, and obtain the land use distribution prediction raster data of the prediction period.
5. The ecosystem service prediction method according to claim 4, characterized in that: The comprehensive evaluation model of ecosystem services and trade-offs includes a rainfall erosion analysis module, an annual water yield module, and an ecosystem service prediction module; Inputting the downscaled climate raster data of the target period and the land use distribution prediction raster data of the prediction period into a preset comprehensive evaluation model of ecosystem services and trade-offs for ecosystem service prediction, and obtaining the ecosystem service prediction raster data of the prediction period of the target area, including the steps of: Input the downscaled climate raster data of the target period into the rainfall erosion analysis module for rainfall erosion factor calculation, and obtain the rainfall erosion factor raster data of the target period; Input the rainfall erosion factor raster data of the target period and the land use distribution prediction raster data of the prediction period into the annual water yield module for annual water yield prediction, and obtain the annual water yield raster data of the prediction period; Input the annual water yield raster data of the prediction period into the ecosystem service prediction module for ecosystem service prediction, and obtain the ecosystem service prediction raster data of the prediction period.
6. An ecosystem service prediction device, characterized in that, Including: A data acquisition module for acquiring the climate raster data of the target period, the land use type raster data of the target area, and the land use type raster data of the historical period; A data downscaling module for downscaling the climate raster data of the target period by using the Kriging interpolation method to obtain the downscaled climate raster data of the target period; A matrix construction module for constructing a land use type weight matrix according to the land use type raster data of the target period and the historical period; A first prediction module for inputting the land use type raster data of the target period and the historical period, the downscaled climate raster data of the target period, and the land use type weight matrix into a preset land use distribution prediction model for land use distribution prediction, and obtaining the land use distribution prediction raster data of the prediction period of the target area; A second prediction module, configured to input the downscaled climate raster data of the target period and the predicted land use distribution raster data of the prediction period into a preset comprehensive assessment model of ecosystem services and trade-offs for ecosystem service prediction, so as to obtain the predicted ecosystem service raster data of the prediction period of the target area.
7. A computer device, characterized in that, It includes: a processor, a memory, and a computer program stored on the memory and executable on the processor; when the computer program is executed by the processor, the steps of the ecosystem service prediction method according to any one of claims 1 to 5 are implemented.
8. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the ecosystem service prediction method according to any one of claims 1 to 5 are implemented.