Method and device for processing resource data in black soil area based on geographic information system

Abstract

The application relates to a black soil area resource data processing method and device based on a geographic information system. The method comprises the following steps: acquiring remote sensing image data, soil attribute data and land use data of a black soil area; determining a terrain slope by using the remote sensing image data, determining a soil type by using the soil attribute data, and determining a land use mode by using the land use data; dividing the black soil area into a plurality of relatively homogeneous spatial evaluation units based on the terrain slope, the soil type and the land use mode; evaluating the execution of various black soil protection measures in each spatial evaluation unit by using preset regional cost-effectiveness ratio targets, economic benefit targets and ecological benefit targets, and determining target protection measures of each spatial evaluation unit; and configuring corresponding target protection measures in each spatial evaluation unit to form a spatial configuration scheme. The method can refine the spatial configuration granularity and improve the configuration accuracy of the black soil protection measures.

Description

Technical Field

[0001] This application relates to the field of agricultural production planning technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing resource data in black soil areas based on geographic information systems. Background Technology

[0002] As a crucial grain production base, the black soil region faces severe challenges in coordinating its protection with agricultural production. While existing black soil protection measures, such as conservation tillage and organic fertilizer application, can effectively improve soil quality, they often come with increased agricultural production costs and fluctuating short-term economic benefits, leading to low farmer adoption rates and difficulties in policy promotion. How to scientifically quantify the long-term ecological and short-term economic benefits of black soil protection measures and achieve synergistic optimization of both at the regional scale is a critical issue that urgently needs to be addressed.

[0003] Existing resource data processing schemes for black soil regions typically focus on a single economic or ecological benefit assessment and are mostly static analysis methods. However, black soil protection is a long-term process, and its benefits accumulate over time. Furthermore, information such as soil properties, topographic features, and cultivation history in black soil regions exhibits significant spatial heterogeneity. Therefore, existing technologies struggle to simulate the dynamic changes in different plots after implementing black soil protection measures, leading to errors in resource data processing for black soil regions. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing resource data in black soil areas based on geographic information systems, which can improve the accuracy of configuration, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for processing resource data in black soil regions based on a geographic information system, including:

[0006] Acquire remote sensing image data, soil property data, and land use data of the black soil region;

[0007] The terrain slope is determined using the remote sensing image data, the soil type is determined using the soil property data, and the land use pattern is determined using the land use data.

[0008] Based on the terrain slope, soil type, and land use pattern, the black soil region is divided into several relatively homogeneous spatial assessment units.

[0009] The implementation of various black soil protection measures in each of the spatial assessment units is evaluated using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and the target protection measures corresponding to each of the spatial assessment units are determined.

[0010] Corresponding target protection measures are configured within each of the aforementioned spatial assessment units to form a spatial configuration scheme corresponding to the black soil region.

[0011] In one embodiment, the division of the black soil region into several relatively homogeneous spatial assessment units based on the terrain slope, soil type, and land use pattern includes:

[0012] Obtain the digital elevation model of the black soil region, wherein the digital elevation model is a raster layer;

[0013] The terrain slope is rasterized to obtain slope pixel values, and the slope pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a slope raster map.

[0014] The soil type is rasterized to obtain type pixel values, and the type pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a type raster map;

[0015] The land use pattern is rasterized to obtain pattern pixel values, and the pattern pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a pattern raster map;

[0016] The slope raster, the type raster, and the mode raster are superimposed to obtain a superimposed raster. The superimposed raster includes several initial relatively homogeneous units, and the slope pixel value, type pixel value, and mode pixel value are the same at each pixel position in each relatively homogeneous unit.

[0017] If the area of ​​the relatively homogeneous unit is less than a preset area threshold, the relatively homogeneous units adjacent to the relatively homogeneous unit in the overlay raster are determined, and the relatively homogeneous unit is merged with the adjacent relatively homogeneous units to obtain the spatial evaluation unit.

[0018] In one embodiment, the evaluation of the implementation of various black soil protection measures in each spatial assessment unit using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and the determination of the target protection measures corresponding to each spatial assessment unit, includes:

[0019] Determine the cost parameters of each of the aforementioned black soil protection measures within each of the aforementioned spatial assessment units;

[0020] Based on crop growth models, predict the economic benefit parameters of various black soil protection measures in each of the spatial assessment units;

[0021] Using a soil process model, the changes in ecological indicators of various black soil protection measures within each spatial assessment unit are simulated to generate corresponding ecological benefit parameters.

[0022] The cost-effectiveness ratio parameter is generated using the cost parameter, the economic benefit parameter, and the ecological benefit parameter.

[0023] Using the cost-effectiveness ratio parameter, the economic benefit parameter, and the ecological benefit parameter, multi-objective evaluation values ​​corresponding to the regional cost-effectiveness ratio target, the economic benefit target, and the ecological benefit target are generated for various black soil protection measures in each spatial evaluation unit;

[0024] Based on the multi-objective evaluation values, target protection measures corresponding to each of the spatial evaluation units are determined.

[0025] In one embodiment, generating multi-objective evaluation values ​​for various black soil protection measures within each spatial assessment unit, corresponding to the regional cost-effectiveness target, the economic benefit target, and the ecological benefit target, using the cost-effectiveness ratio parameter, the economic benefit parameter, and the ecological benefit parameter, includes:

[0026] Determine the first weighting coefficient corresponding to the current configured regional cost-effectiveness ratio target, the second weighting coefficient corresponding to the economic benefit target, and the third weighting coefficient corresponding to the ecological benefit target;

[0027] The cost-effectiveness ratio parameter is used to determine a first target value under the cost-effectiveness ratio target of the region, and the first target value is weighted using the first weighting coefficient to obtain a first evaluation value;

[0028] The second target value under the economic benefit objective is determined using the economic benefit parameters, and the second target value is weighted using the second weighting coefficient to obtain the second evaluation value;

[0029] The third target value under the ecological benefit objective is determined using the ecological benefit parameters, and the third target value is weighted using the third weighting coefficient to obtain the third evaluation value;

[0030] The first evaluation value, the second evaluation value, and the third evaluation value are added together to obtain the multi-objective evaluation value.

[0031] In one embodiment, determining the first weighting coefficient corresponding to the currently configured regional cost-effectiveness target, the second weighting coefficient corresponding to the economic benefit target, and the third weighting coefficient corresponding to the ecological benefit target includes:

[0032] Obtain multiple preset weight combinations;

[0033] The first weight coefficient, the second weight coefficient, and the third weight coefficient are read from each of the weight combinations respectively;

[0034] The step of determining the target protection measures corresponding to each of the spatial assessment units based on the multi-target evaluation values ​​includes:

[0035] Based on the multi-objective evaluation value corresponding to each of the weight combinations, the target protection measures corresponding to each of the spatial evaluation units and each of the weight combinations are determined respectively.

[0036] In one embodiment, generating the cost-effectiveness ratio parameter using the cost parameter, the economic benefit parameter, and the ecological benefit parameter includes:

[0037] The total benefit parameter is obtained by adding the economic benefit parameter to the ecological benefit parameter.

[0038] The net present value is determined by the difference between the total benefit parameter and the cost parameter;

[0039] The investment payback period is determined based on the net present value.

[0040] The benefit-cost ratio corresponding to each of the black soil protection measures is determined by using the ratio of the total benefit parameter to the cost parameter.

[0041] The cost-effectiveness ratio is obtained by calculating the dynamic cost-effectiveness ratio using the net present value, the investment payback period, and the benefit-cost ratio.

[0042] In one embodiment, before evaluating the implementation of various black soil protection measures in each of the spatial assessment units using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and determining the target protection measures corresponding to each of the spatial assessment units, the method further includes:

[0043] The implementation of various black soil protection measures in each spatial assessment unit is evaluated using spatial and temporal constraints.

[0044] Black soil protection measures that do not meet the spatial constraints and black soil protection measures that do not meet the temporal constraints are filtered out.

[0045] Secondly, this application also provides a black soil region resource data processing device based on a geographic information system, comprising:

[0046] The data acquisition module is used to acquire remote sensing image data, soil attribute data, and land use data of the black soil region;

[0047] The data analysis module is used to determine the terrain slope using the remote sensing image data, determine the soil type using the soil attribute data, and determine the land use pattern using the land use data.

[0048] The unit division module is used to divide the black soil area into several relatively homogeneous spatial evaluation units based on the soil type, the terrain slope, and the land use pattern.

[0049] The measure decision module is used to evaluate the implementation of various black soil protection measures in each of the spatial assessment units using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and to determine the target protection measures corresponding to each of the spatial assessment units.

[0050] The scheme formation module is used to configure corresponding target protection measures in each of the spatial assessment units to form a spatial configuration scheme corresponding to the black soil region.

[0051] In one embodiment, the unit partitioning module is further configured to:

[0052] Obtain the digital elevation model of the black soil region, wherein the digital elevation model is a raster layer;

[0053] The terrain slope is rasterized to obtain slope pixel values, and the slope pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a slope raster map.

[0054] The soil type is rasterized to obtain type pixel values, and the type pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a type raster map;

[0055] The land use pattern is rasterized to obtain pattern pixel values, and the pattern pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a pattern raster map;

[0056] The slope raster, the type raster, and the mode raster are superimposed to obtain a superimposed raster. The superimposed raster includes several initial relatively homogeneous units, and the slope pixel value, type pixel value, and mode pixel value are the same at each pixel position in each relatively homogeneous unit.

[0057] If the area of ​​the relatively homogeneous unit is less than a preset area threshold, the relatively homogeneous units adjacent to the relatively homogeneous unit in the overlay raster are determined, and the relatively homogeneous unit is merged with the adjacent relatively homogeneous units to obtain the spatial evaluation unit.

[0058] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the black soil region resource data processing method based on a geographic information system as described in any of the embodiments of the first aspect.

[0059] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the black soil region resource data processing method based on a geographic information system as described in any of the embodiments of the first aspect.

[0060] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the black soil region resource data processing method based on a geographic information system as described in any of the embodiments of the first aspect.

[0061] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing resource data in black soil areas based on geographic information systems acquire remote sensing image data, soil attribute data, and land use data of black soil areas; determine topographic slope using remote sensing image data, soil type using soil attribute data, and land use pattern using land use data; divide the black soil area into several relatively homogeneous spatial assessment units based on topographic slope, soil type, and land use pattern; and evaluate the implementation of various black soil protection measures in each spatial assessment unit using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, thereby determining the target protection for each spatial assessment unit. The measures involve configuring corresponding target protection measures within each spatial assessment unit to form a spatial configuration scheme corresponding to the black soil region. This allows for unit division based on the spatial heterogeneity of different areas within the black soil region in terms of topographic slope, soil type, and land use patterns, refining the granularity of spatial configuration. Then, for each spatial assessment unit after division, the implementation of various black soil protection measures in the spatial assessment unit is evaluated from multiple dimensions using regional cost-effectiveness targets, long-term ecological benefit targets, and short-term economic benefit targets. This determines the optimal target protection measures for each spatial assessment unit, thereby improving the matching accuracy between target protection measures and spatial assessment units, and ultimately enhancing the configuration accuracy of resource data processing schemes for the entire black soil region. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart illustrating a method for processing resource data in black soil regions based on a geographic information system in one embodiment.

[0064] Figure 2 This is a flowchart illustrating the steps for determining the target protection measures in one embodiment;

[0065] Figure 3 This is a flowchart illustrating the steps for calculating multi-objective evaluation values ​​in one embodiment;

[0066] Figure 4 This is a flowchart illustrating the weight combination acquisition step in one embodiment;

[0067] Figure 5 This is a flowchart illustrating the cost-effectiveness parameter calculation steps in one embodiment;

[0068] Figure 6 This is a flowchart illustrating the spatial evaluation unit division steps in one embodiment;

[0069] Figure 7 This is a structural block diagram of a black soil region resource data processing device 700 based on a geographic information system in one embodiment.

[0070] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0071] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0072] It should be noted that the term "several" as used in this application may refer to one or more types. The terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "multiple" as used in this application refers to two or more. The term "and / or" as used in this application refers to one of the solutions, or any combination of multiple solutions. The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant regulations.

[0073] In one exemplary embodiment, such as Figure 1 As shown, a method for processing resource data in black soil areas based on a geographic information system is provided. This embodiment illustrates the method by applying it to a server. It is understood that this method can also be applied to a terminal, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps S102 to S110. Wherein:

[0074] Step S102: Obtain remote sensing image data, soil attribute data, and land use data of the black soil region.

[0075] For example, the server can acquire high-resolution remote sensing image data of the black soil region through a Geographic Information System (GIS). It can download the following data from a data communication network or receive data uploaded by the user via an input device: soil property data such as organic matter content, bulk density, and pH value of the black soil region; and land use data such as cultivated land type, forest land type, grassland type, wetland type, construction land type, crop distribution data, cultivation history data, and protection measure implementation data.

[0076] Step S104: Determine the terrain slope using remote sensing image data, determine the soil type using soil property data, and determine the land use pattern using land use data.

[0077] For example, the server can acquire a corresponding digital elevation model (DEM) using remote sensing image data. The elevation values ​​of the corresponding pixels at each location are read from the DEM. The slope value corresponding to each pixel is calculated using the difference in elevation values ​​between adjacent pixels and the pixel resolution. The topographic slope at each location within the black soil region is determined based on the slope values. The fertility level at each location within the black soil region can be determined by comparing the organic matter content in the soil attribute data with a preset content threshold. The bulk density in the soil attribute data can be used to determine whether the soil at each location within the black soil region is loose or compacted. The pH value in the soil attribute data can be used to determine whether the soil at each location within the black soil region is acidic, neutral, or alkaline. Land use data can be used to determine whether the land use type at each location within the black soil region is arable land, forest land, corn cultivation, or soybean cultivation, etc.

[0078] Step S106: Based on soil type, topographic slope and land use, the black soil region is divided into several relatively homogeneous spatial assessment units.

[0079] For example, the server can cluster black soil regions based on soil type, topographic slope, and land use: assessing the differences in soil type, topographic slope, and land use at two locations. If the difference is less than a preset difference threshold, the two locations can be determined to be relatively homogeneous, and thus grouped into the same region. If the difference is greater than the preset difference threshold, the two locations are grouped into different regions. This results in several clustered regions, each serving as an independent spatial evaluation unit. Within the same spatial evaluation unit, the soil type, topographic slope, and land use at each location are relatively homogeneous.

[0080] Step S108: The implementation of various black soil protection measures in each spatial assessment unit is evaluated using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and the target protection measures corresponding to each spatial assessment unit are determined.

[0081] For example, the server can store various black soil protection measures such as no-till farming, deep tillage, and organic fertilizer substitution, as well as preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets. The regional cost-effectiveness target can be used to characterize the goal of maximizing the regional cost-effectiveness ratio. The economic benefit target can be used to characterize the goal of maximizing economic benefits. The ecological benefit target can be used to characterize the goal of maximizing ecological benefits.

[0082] The server can perform the following operations for each spatial assessment unit: Determine the implementation status of various black soil protection measures within the current spatial assessment unit by considering the costs required to implement these measures and the expected benefits. Evaluate the implementation status of each black soil protection measure within the current spatial assessment unit using regional cost-effectiveness, economic benefit, and ecological benefit objectives, respectively, to obtain evaluation values ​​corresponding to each measure. Select the black soil protection measure with the highest evaluation value or an evaluation value greater than a preset threshold as the optimal target protection measure within the current spatial assessment unit. Optionally, in some implementations, the non-dominated sorting genetic algorithm NSGA-II can be used to perform multi-objective optimization operations to solve for the target protection measure.

[0083] Step S110: Configure corresponding target protection measures in each spatial assessment unit to form a spatial configuration scheme corresponding to the black soil region.

[0084] For example, the server can configure corresponding target protection measures in each spatial assessment unit within the black soil region according to the correspondence between spatial assessment units and target protection measures. The configuration of target protection measures in all spatial assessment units within the black soil region is then summarized to form a spatial configuration scheme corresponding to the black soil region.

[0085] The aforementioned method for processing resource data in black soil regions based on geographic information systems involves acquiring remote sensing image data, soil attribute data, and land use data of the black soil region; determining topographic slope using remote sensing image data, soil type using soil attribute data, and land use pattern using land use data; dividing the black soil region into several relatively homogeneous spatial assessment units based on topographic slope, soil type, and land use pattern; evaluating the implementation of various black soil protection measures in each spatial assessment unit using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and determining the target protection measures corresponding to each spatial assessment unit; and configuring corresponding target protection measures within each spatial assessment unit to form a system corresponding to the black soil region. The spatial configuration scheme can divide the black soil region into units based on the spatial heterogeneity of different areas in terms of topographic slope, soil type, and land use, and refine the granularity of spatial configuration. This facilitates the implementation of "one policy for one place" protection measures, improves the spatial targeting and feasibility of black soil protection measures, and then conducts a multi-dimensional evaluation of the implementation of various black soil protection measures in each spatial assessment unit using regional cost-effectiveness targets, long-term ecological benefit targets, and short-term economic benefit targets. This determines the optimal target protection measures for each spatial assessment unit, thereby improving the matching accuracy between target protection measures and spatial assessment units, and ultimately enhancing the configuration accuracy of resource data processing schemes for the entire black soil region.

[0086] In one exemplary embodiment, such as Figure 2 As shown, step S110 may include steps S202 to S212. Wherein:

[0087] Step S202: Determine the cost parameters of various black soil protection measures within each spatial assessment unit.

[0088] For example, the server can perform the following operations for each spatial assessment unit: quantify the direct costs (such as seed costs, fertilizer costs, machinery costs, and labor costs) required to implement various black soil protection measures within the current spatial assessment unit based on cost calculation logic, as well as the indirect costs due to unstable seed yields. The direct and indirect costs corresponding to each black soil protection measure are then added together to obtain the cost parameters for each measure within the current spatial assessment unit. Optionally, in some implementations, mathematical modeling of input costs in farmer survey data can be performed to learn the inherent relationships within the data, thereby forming a digitized cost calculation logic.

[0089] Step S204: Based on the crop growth model, predict the economic benefit parameters of various black soil protection measures in each spatial assessment unit.

[0090] For example, the server can be pre-deployed with any of the following models as crop growth models: Decision Support System for Agrotechnology Transfer (DSSAT), Agricultural Production Systems Intercomparison and Synthesis Model (APSIM), or Multidisciplinary Standard Crop Simulator (STICS). Optionally, in some implementations, any of the above models can be pre-trained and fine-tuned using crop yield data from farmer surveys and corresponding management practices to obtain a crop growth model suitable for black soil regions.

[0091] The server can perform the following operations for each spatial assessment unit: Using a crop growth model, it performs calculations based on various black soil protection measures, soil attribute data corresponding to the current spatial assessment unit, and land use data to predict crop yields under different black soil protection measures within the current spatial assessment unit, and quantifies the corresponding output value. The output value corresponding to each black soil protection measure is then used as the economic benefit parameter for that measure within the current spatial assessment unit.

[0092] Step S206: Use a soil process model to simulate the changes in ecological indicators of various black soil protection measures in each spatial assessment unit, and generate corresponding ecological benefit parameters.

[0093] For example, the server can pre-store any of the following models as soil process models: Environmental Policy Integrated Climate Model (EPIC), Agricultural Policy / Environmental eXtender Model (APEX), Soil and Water Assessment Tool (SWAT).

[0094] The server can perform the following operations for each spatial assessment unit: using a soil process model, it simulates the changes in ecological indicators such as soil organic carbon storage, soil moisture content, and soil nitrogen and phosphorus content within a preset time period during which various black soil protection measures are implemented in the current spatial assessment unit. By replacing the cost method with a market value approach, the changes in ecological indicators are monetized, thereby obtaining the ecological benefit parameters of various black soil protection measures within the current spatial assessment unit.

[0095] Step S208: Generate cost-effectiveness ratio parameters using cost parameters, economic benefit parameters, and ecological benefit parameters.

[0096] For example, the server may store a preset cost-effectiveness calculation formula. The server may perform the following operations for each spatial assessment unit: calculate and process the cost parameters, economic benefit parameters, and ecological benefit parameters of various black soil protection measures within the current spatial assessment unit based on the cost-effectiveness calculation formula, and obtain the cost-effectiveness parameters of various black soil protection measures within the current spatial assessment unit.

[0097] Step S210: Using cost-effectiveness parameters, economic benefit parameters, and ecological benefit parameters, generate multi-objective evaluation values ​​for various black soil protection measures within each spatial assessment unit, corresponding to the regional cost-effectiveness target, economic benefit target, and ecological benefit target.

[0098] For example, the server can perform the following operations for each spatial assessment unit: evaluate the cost-effectiveness parameters of various black soil protection measures in the current spatial assessment unit using the regional cost-effectiveness target, evaluate the economic benefit parameters of various black soil protection measures in the current spatial assessment unit using the economic benefit target, and evaluate the ecological benefit parameters of various black soil protection measures in the current spatial assessment unit using the ecological benefit target, thereby generating multi-objective evaluation values ​​for various black soil protection measures in the current spatial assessment unit.

[0099] Step S212: Based on the multi-objective evaluation values, determine the target protection measures corresponding to each spatial evaluation unit.

[0100] For example, the server can perform the following operations for each spatial assessment unit: compare the multi-objective evaluation values ​​of various black soil protection measures within the current spatial assessment unit. The black soil protection measure with the highest multi-objective evaluation value is determined as the target protection measure corresponding to the current spatial assessment unit. Alternatively, black soil protection measures with multi-objective evaluation values ​​greater than a preset threshold can also be determined as the target protection measures corresponding to the current spatial assessment unit. This yields the target protection measures for each spatial assessment unit.

[0101] In this embodiment, by using crop growth models and soil process models to generate economic and ecological benefit parameters, the economic and ecological benefits of black soil protection measures can be effectively quantified. By using cost-effectiveness ratio parameters, economic benefit parameters, and ecological benefit parameters, multi-objective evaluation values ​​corresponding to regional cost-effectiveness ratio targets, economic benefit targets, and ecological benefit targets are generated. Based on the multi-objective evaluation values, target protection measures are determined. The optimal target protection measures can be selected by comprehensively considering regional cost-effectiveness ratio, economic benefits, and ecological benefits, taking into account both economic feasibility and ecological sustainability.

[0102] In one exemplary embodiment, such as Figure 3 As shown, step S210 may include steps S302 to S310. Wherein:

[0103] Step S302: Determine the first weight coefficient corresponding to the current configured regional cost-effectiveness ratio target, the second weight coefficient corresponding to the economic benefit target, and the third weight coefficient corresponding to the ecological benefit target.

[0104] For example, the server can read from the local database the first weighting coefficient corresponding to the regional cost-effectiveness target, the second weighting coefficient corresponding to the economic benefit target, and the third weighting coefficient corresponding to the ecological benefit target. The server can perform the following steps S304 to S310 for each spatial assessment unit.

[0105] Step S304: Determine the first target value under the regional cost-effectiveness target using the cost-effectiveness ratio parameter, and obtain the first evaluation value by weighting the first target value using the first weighting coefficient.

[0106] For example, the server can score the cost-effectiveness parameters of various black soil protection measures in the current spatial assessment unit using a regional cost-effectiveness target that maximizes the regional cost-effectiveness ratio. For instance, based on the magnitude of the regional cost-effectiveness parameter, a first target value of 1 is assigned to the black soil protection measure with the largest regional cost-effectiveness parameter, and a first target value of 0.1 is assigned to the black soil protection measure with the smallest regional cost-effectiveness parameter, thus obtaining the first target value for each black soil protection measure in the current spatial assessment unit. The first target value is then multiplied by a first weighting coefficient, and the product is used as the weighted first assessment value.

[0107] Step S306: Determine the second target value under the economic benefit objective using economic benefit parameters, and obtain the second evaluation value by weighting the second target value using the second weighting coefficient.

[0108] Step S308: Determine the third target value under the ecological benefit target using ecological benefit parameters, and obtain the third evaluation value by weighting the third target value using the third weighting coefficient.

[0109] For example, the server can refer to the method for obtaining the first evaluation value described above, and use the economic benefit objective of maximizing economic benefits to score the economic benefit parameters of various black soil protection measures in the current spatial evaluation unit to obtain a second target value. The second weighting coefficient is then multiplied by the second target value to obtain the second evaluation value. Similarly, the ecological benefit parameters of various black soil protection measures in the current spatial evaluation unit are scored using the ecological benefit parameters of maximizing ecological benefits to obtain a third target value. The third weighting coefficient is then multiplied by the third target value to obtain the third evaluation value.

[0110] Step S310: Add the first evaluation value, the second evaluation value, and the third evaluation value to obtain the multi-objective evaluation value.

[0111] For example, the server can add up the first evaluation value, the second evaluation value, and the third evaluation value of various black soil protection measures in the current spatial evaluation unit to obtain the multi-objective evaluation value of various black soil protection measures in the current spatial evaluation unit.

[0112] In this embodiment, by determining the first weight coefficient corresponding to the current configured regional cost-effectiveness ratio target, the second weight coefficient corresponding to the economic benefit target, and the third weight coefficient corresponding to the ecological benefit target, the corresponding target values ​​are weighted and summed using the first weight coefficient, the second weight coefficient, and the third weight coefficient respectively to obtain the multi-objective evaluation value. This can transform the multi-objective optimization solution into a single-objective solution, balance the influence of different targets on the selection of the final optimal protection measures, and improve the accuracy of the multi-objective evaluation value.

[0113] In one exemplary embodiment, such as Figure 4 As shown, step S302 may include steps S402 to S406. Wherein:

[0114] Step S402: Obtain a variety of preset weight combinations.

[0115] Step S404: Read the corresponding first weight coefficient, second weight coefficient and third weight coefficient from each weight combination.

[0116] Step S406: Based on the multi-objective evaluation value corresponding to each weight combination, determine the target protection measures corresponding to each spatial evaluation unit and each weight combination.

[0117] For example, the server can also store multiple preset weight combinations. Each weight combination includes a corresponding set of first weight coefficients, second weight coefficients, and third weight coefficients. The server can actively iterate through each weight combination and read the corresponding first weight coefficient, second weight coefficient, and third weight coefficient from each weight combination. Alternatively, the server can send the currently configured weight combination of first weight coefficients, second weight coefficients, and third weight coefficients to the front-end component for display, and obtain the new weight combination in response to the configuration update operation for weight coefficients triggered by the front-end component.

[0118] The first target value is weighted using the first weight coefficient of each weight combination, the second target value is weighted using the second weight coefficient, and the third target value is weighted using the third weight coefficient. The summation of these weights yields the multi-objective evaluation value corresponding to each weight combination. The black soil protection measure with the highest multi-objective evaluation value under each weight combination is selected as the target protection measure corresponding to that weight combination. This yields the target protection measures for each spatial evaluation unit and each weight combination.

[0119] Optionally, in some implementations, if short-term economic returns are prioritized, a weight combination where the second weighting coefficient is greater than the third weighting coefficient can be used. If long-term ecological benefits are prioritized, a weight combination where the third weighting coefficient is greater than the second weighting coefficient can be used.

[0120] In this embodiment, by using different types of weight combinations to weight and fuse the first target value under the regional cost-effectiveness target, the second target value under the economic benefit target, and the third target value under the ecological benefit target, the optimal target protection measures without weight preference can be quickly obtained, thereby improving the flexibility of resource data processing in the black soil region.

[0121] In one exemplary embodiment, such as Figure 5 As shown, step S208 may include steps S502 to S510. Wherein:

[0122] Step S502: Add the economic benefit parameters and the ecological benefit parameters to obtain the total benefit parameters.

[0123] Step S504: Determine the net present value using the difference between the total benefit parameter and the cost parameter.

[0124] Step S506: Determine the payback period based on net present value.

[0125] Step S508: Determine the benefit-cost ratio of various black soil protection measures using the ratio of the total benefit parameter to the cost parameter.

[0126] Step S510: Calculate the dynamic cost-effectiveness ratio using net present value, payback period, and benefit-cost ratio to obtain the cost-effectiveness ratio parameter.

[0127] For example, the server can construct a dynamic cost-effectiveness ratio index to comprehensively consider data such as the net present value (NPV), payback period, and benefit-cost ratio (BCR) of various black soil protection measures implemented in the current spatial assessment unit, thereby evaluating the comprehensive benefits of the current spatial assessment unit under the implementation of various black soil protection measures within a specific planning period (such as one month, six months, three years, etc.).

[0128] For each black soil protection measure implemented within the current spatial assessment unit, the following steps are performed: Add the economic and ecological benefit parameters for the specific planning period corresponding to the current black soil protection measure to obtain the total benefit parameter. Subtract the cost parameter for the specific planning period from the total benefit parameter to obtain the net present value (NPV) for that specific planning period. Calculate the investment payback period from the start of black soil protection measures until the NPV exceeds 0. Use the ratio of the total benefit parameter to the cost parameter as the benefit-cost ratio. Combine the NPV, investment payback period, and benefit-cost ratio to calculate the dynamic cost-effectiveness ratio. For example, the NPV, investment payback period, and benefit-cost ratio can all be mapped to scores within [0,1] and then summed. Alternatively, the weighting coefficients corresponding to the NPV, investment payback period, and benefit-cost ratio can be set according to the above method for obtaining multi-objective assessment values, and a weighted summation method can be used to obtain the cost-effectiveness ratio parameters for each black soil protection measure in each spatial assessment unit.

[0129] In this embodiment, by monetizing long-term benefits and combining them with short-term economic costs, a comprehensive and quantitative assessment of cost-effectiveness ratio is achieved. This addresses the one-sidedness of traditional assessment methods that focus on a single economic or ecological benefit and improves the accuracy of cost-effectiveness ratio parameter calculation.

[0130] In an exemplary embodiment, before performing step S108, the method may further include: evaluating the implementation of various black soil protection measures in each spatial assessment unit using spatial constraints and time constraints; and filtering out black soil protection measures that do not meet the spatial constraints and time constraints.

[0131] Optionally, in some implementations, the server may pre-store spatial constraints and temporal constraints. The spatial constraints may be set based on the cost advantages of implementing black soil protection measures across multiple adjacent spatial assessment units. The temporal constraints may be set based on the time lag in the benefits generated after implementing the black soil protection measures. The server can introduce spatial and temporal constraints to constrain the black soil protection measures configured within each spatial assessment unit. Black soil protection measures that do not meet the spatial constraints or the temporal constraints within the current spatial assessment unit are filtered out, so that subsequent searches only target black soil protection measures that simultaneously meet both spatial and temporal constraints to find the optimal target protection measure.

[0132] Alternatively, in other implementations, spatial constraints and temporal constraints corresponding to terrain slope, soil type, or land use pattern may be stored to filter out black soil protection measures that do not match the terrain slope, soil type, or land use pattern of the current spatial assessment unit.

[0133] In this embodiment, by setting spatial and temporal constraints to filter out black soil protection measures that do not meet the conditions, it is possible not only to reduce the amount of data computation required to determine the target protection measures in subsequent multi-objective optimization, thereby improving the efficiency of determining the target protection measures, but also to fully consider the benefits of implementing black soil protection measures in the temporal and spatial dimensions, thereby improving the accuracy of determining the target protection measures.

[0134] Alternatively, in some implementations, such as Figure 6 As shown, step S104 may further include steps S602 to S612. Wherein:

[0135] Step S602: Obtain the digital elevation model of the black soil region. The digital elevation model is a raster layer.

[0136] Step S604: The terrain slope is rasterized to obtain slope pixel values, and the slope pixel values ​​are stored in the corresponding pixel positions in the raster layer to form a slope raster map.

[0137] For example, the server can obtain a Digital Elevation Model (DEM) of the black soil region. The DEM is a raster layer, where each pixel location stores corresponding elevation data. Based on a preset mapping relationship between terrain slope and pixel value, the terrain slope is rasterized to obtain slope pixel values. For example, when the terrain slope is "gentle," the slope pixel value is 1; when the terrain slope is "steep," the slope pixel value is 5. The slope pixel value corresponding to each pixel location is stored in the corresponding pixel location in the raster layer, forming a slope raster map that stores slope pixel values.

[0138] Step S606: Rasterize the soil type to obtain type pixel values, and store the type pixel values ​​at the corresponding pixel positions in the raster layer to form a type raster map.

[0139] Step S608: The land use pattern is rasterized to obtain the pattern pixel value, and the pattern pixel value is stored in the corresponding pixel position in the raster layer to form a pattern raster map.

[0140] For example, referring to the above method for generating slope raster maps, type pixel values ​​can be determined based on a preset mapping relationship between soil type and pixel values, and the type pixel values ​​can be stored in the corresponding pixel positions in the raster layer to form a type raster map. Similarly, method pixel values ​​can be determined based on a preset mapping relationship between utilization method and pixel values, and the method pixel values ​​can be stored in the corresponding pixel positions in the raster layer to form a method raster map.

[0141] Step S610: Overlay the slope raster, type raster, and mode raster to obtain an overlay raster. The overlay raster includes several initial relatively homogeneous units, and the slope pixel value, type pixel value, and mode pixel value are the same at each pixel position in each relatively homogeneous unit.

[0142] For example, the server can overlay a slope raster, a type raster, and a mode raster, and combine and match the slope pixel value, type pixel value, and mode pixel value at each pixel location one by one to obtain several initial relatively homogeneous units with the same slope pixel value, type pixel value, and mode pixel value, thus forming an overlaid raster.

[0143] Step S612: If the area of ​​a relatively homogeneous unit is less than a preset area threshold, determine the relatively homogeneous units adjacent to the relatively homogeneous units in the overlay raster diagram, and merge the relatively homogeneous units with the adjacent relatively homogeneous units to obtain a spatial evaluation unit.

[0144] For example, the server may store a preset area threshold. The area of ​​the initial relatively homogeneous unit is compared with the preset area threshold. If the area of ​​the relatively homogeneous unit is less than the preset area threshold, relatively homogeneous units adjacent to the relatively homogeneous unit in the overlay raster (sharing a common boundary or edge in the raster) are identified. If the difference between the slope, type, and mode pixel values ​​of the adjacent relatively homogeneous units and the current relatively homogeneous unit is less than a preset difference threshold, the relatively homogeneous unit is merged with its adjacent relatively homogeneous units to form a new relatively homogeneous unit. If the area of ​​the merged new relatively homogeneous unit is still less than the preset area threshold, the above operation of identifying and merging adjacent relatively homogeneous units is repeated until the area of ​​all relatively homogeneous units is greater than or equal to the preset area threshold. At this point, each relatively homogeneous unit is considered a spatial evaluation unit.

[0145] In this embodiment, by combining digital elevation model and geographic information coefficient technology for information rasterization storage and overlay, relatively homogeneous spatial evaluation units are obtained, which can improve the accuracy of spatial evaluation unit division.

[0146] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0147] Based on the same inventive concept, this application also provides a geographic information system-based black soil region resource data processing device for implementing the aforementioned geographic information system-based black soil region resource data processing method. The solution provided by this device is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more geographic information system-based black soil region resource data processing device embodiments provided below can be found in the limitations of the geographic information system-based black soil region resource data processing method described above, and will not be repeated here.

[0148] In one exemplary embodiment, such as Figure 7 As shown, a black soil region resource data processing device 700 based on a geographic information system is provided, including: a data acquisition module 702, a data analysis module 704, a unit division module 706, a measure decision-making module 708, and a scheme formation module 710, wherein:

[0149] The data acquisition module 702 is used to acquire remote sensing image data, soil attribute data, and land use data of the black soil region.

[0150] Data analysis module 704 is used to determine the terrain slope using remote sensing image data, determine the soil type using soil property data, and determine the land use pattern using land use data.

[0151] The unit division module 706 is used to divide the black soil region into several relatively homogeneous spatial assessment units based on soil type, topographic slope and land use.

[0152] The measure decision module 708 is used to evaluate the implementation of various black soil protection measures in each spatial assessment unit using preset regional cost-effectiveness targets, economic benefit targets, and ecological benefit targets, and to determine the target protection measures corresponding to each spatial assessment unit.

[0153] The scheme formation module 710 is used to configure corresponding target protection measures in each spatial assessment unit to form a spatial configuration scheme corresponding to the black soil region.

[0154] In an exemplary embodiment, the measure decision module 708 includes a parameter determination unit, used to determine the cost parameters of various black soil protection measures in each spatial assessment unit; predict the economic benefit parameters of various black soil protection measures in each spatial assessment unit based on a crop growth model; simulate the changes in ecological indicators of various black soil protection measures in each spatial assessment unit using a soil process model to generate corresponding ecological benefit parameters; generate cost-effectiveness ratio parameters using cost parameters, economic benefit parameters, and ecological benefit parameters; and a target assessment unit, used to generate multi-objective assessment values ​​of various black soil protection measures in each spatial assessment unit corresponding to regional cost-effectiveness ratio targets, economic benefit targets, and ecological benefit targets using cost-effectiveness ratio parameters, economic benefit parameters, and ecological benefit parameters; and determine the target protection measures corresponding to each spatial assessment unit based on the multi-objective assessment values.

[0155] In an exemplary embodiment, the target evaluation unit is further configured to determine a first weighting coefficient corresponding to the currently configured regional cost-effectiveness target, a second weighting coefficient corresponding to the economic benefit target, and a third weighting coefficient corresponding to the ecological benefit target; determine a first target value under the regional cost-effectiveness target using cost-effectiveness parameters, and weight the first target value using the first weighting coefficient to obtain a first evaluation value; determine a second target value under the economic benefit target using economic benefit parameters, and weight the second target value using the second weighting coefficient to obtain a second evaluation value; determine a third target value under the ecological benefit target using ecological benefit parameters, and weight the third target value using the third weighting coefficient to obtain a third evaluation value; and add the first evaluation value, the second evaluation value, and the third evaluation value to obtain a multi-target evaluation value.

[0156] In an exemplary embodiment, the target evaluation unit is further configured to obtain a variety of preset weight combinations; and to read the corresponding first weight coefficient, second weight coefficient, and third weight coefficient from each weight combination. Based on the multi-target evaluation value corresponding to each weight combination, the target protection measures corresponding to each spatial evaluation unit and each weight combination are determined respectively.

[0157] In an exemplary embodiment, the parameter determination unit is further configured to add the economic benefit parameter and the ecological benefit parameter to obtain the total benefit parameter; determine the net present value using the difference between the total benefit parameter and the cost parameter; determine the investment payback period based on the net present value; determine the benefit-cost ratio corresponding to various black soil protection measures using the ratio of the total benefit parameter to the cost parameter; and calculate the dynamic cost-effectiveness ratio using the net present value, the investment payback period, and the benefit-cost ratio to obtain the cost-effectiveness ratio parameter.

[0158] In an exemplary embodiment, the measure decision module 708 is further configured to evaluate the implementation status of various black soil protection measures in each spatial assessment unit using spatial constraints and time constraints; and filter out black soil protection measures that do not meet spatial constraints and black soil protection measures that do not meet time constraints.

[0159] In an exemplary embodiment, the unit partitioning module 706 is further configured to acquire a digital elevation model of the black soil region, wherein the digital elevation model is a raster layer; rasterize the terrain slope to obtain slope pixel values, and store the slope pixel values ​​at the corresponding pixel positions in the raster layer to form a slope raster map; rasterize the soil type to obtain type pixel values, and store the type pixel values ​​at the corresponding pixel positions in the raster layer to form a type raster map; and rasterize the land use pattern to obtain pattern pixel values, and store the pattern pixel values ​​in the raster map. At the corresponding pixel locations in the layer, a mode raster is formed. The slope raster, type raster, and mode raster are superimposed to obtain a superimposed raster. The superimposed raster includes several initial relatively homogeneous units. The slope pixel value, type pixel value, and mode pixel value are the same at each pixel location in each relatively homogeneous unit. If the area of ​​the relatively homogeneous unit is less than a preset area threshold, the relatively homogeneous units adjacent to the relatively homogeneous units in the superimposed raster are determined. The relatively homogeneous units are merged with the adjacent relatively homogeneous units to obtain a spatial evaluation unit.

[0160] Each module in the aforementioned geographic information system-based black soil region resource data processing device 700 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0161] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores remote sensing image data, soil attribute data, land use data, topographic slope, soil type, land use patterns, spatial assessment units, regional cost-effectiveness targets, economic benefit targets, ecological benefit targets, target protection measures, and spatial configuration schemes. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for processing resource data in black soil areas based on a geographic information system.

[0162] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0163] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0164] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0165] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0166] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0167] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0168] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for processing resource data in black soil areas based on geographic information systems, characterized in that, The method comprises: acquiring remote sensing image data, soil attribute data and land use data of a black soil area; determining terrain slope using the remote sensing image data, determining soil type using the soil attribute data, and determining land use mode using the land use data; dividing the black soil area into a plurality of relatively homogeneous spatial evaluation units based on the terrain slope, the soil type and the land use mode; evaluating the implementation of various black soil protection measures in each of the spatial evaluation units using preset regional cost-effectiveness targets, economic benefit targets and ecological benefit targets to determine target protection measures corresponding to each of the spatial evaluation units; configuring corresponding target protection measures in each of the spatial evaluation units to form a spatial configuration scheme corresponding to the black soil area; wherein the dividing of the black soil area into a plurality of relatively homogeneous spatial evaluation units based on the terrain slope, the soil type and the land use mode comprises: acquiring a digital elevation model of the black soil area, the digital elevation model being a raster layer; performing rasterization processing on the terrain slope to obtain slope pixel values, and storing the slope pixel values in corresponding pixel positions in the raster layer to form a slope raster map; performing rasterization processing on the soil type to obtain type pixel values, and storing the type pixel values in corresponding pixel positions in the raster layer to form a type raster map; performing rasterization processing on the land use mode to obtain mode pixel values, and storing the mode pixel values in corresponding pixel positions in the raster layer to form a mode raster map; superimposing the slope raster map, the type raster map and the mode raster map to obtain a superimposed raster map, the superimposed raster map comprising a plurality of initial relatively homogeneous units, the slope pixel values, the type pixel values and the mode pixel values at each of the pixel positions in each of the relatively homogeneous units being the same; in a case where the area of the relatively homogeneous unit is less than a preset area threshold, determining a relatively homogeneous unit adjacent to the relatively homogeneous unit in the superimposed raster map, and merging the relatively homogeneous unit and the adjacent relatively homogeneous unit to obtain the spatial evaluation unit.

2. The method of claim 1, wherein, The evaluation of the implementation of various black soil protection measures in each of the spatial evaluation units using preset regional cost-effectiveness targets, economic benefit targets and ecological benefit targets to determine target protection measures corresponding to each of the spatial evaluation units comprises: determining cost parameters of various black soil protection measures in each of the spatial evaluation units; predicting economic benefit parameters of various black soil protection measures in each of the spatial evaluation units based on a crop growth model; simulating changes in ecological indicators of various black soil protection measures in each of the spatial evaluation units using a soil process model to generate corresponding ecological benefit parameters; generating cost-effectiveness parameters using the cost parameters, the economic benefit parameters and the ecological benefit parameters; The cost-benefit ratio parameter, the economic benefit parameter, and the ecological benefit parameter are used to generate multi-target evaluation values of various black soil protection measures in each spatial evaluation unit corresponding to the regional cost-benefit ratio target, the economic benefit target, and the ecological benefit target; Based on the multi-target evaluation values, the target protection measures corresponding to each spatial evaluation unit are determined.

3. The method of claim 2, wherein, The use of the cost-benefit ratio parameter, the economic benefit parameter, and the ecological benefit parameter to generate multi-target evaluation values of various black soil protection measures in each spatial evaluation unit corresponding to the regional cost-benefit ratio target, the economic benefit target, and the ecological benefit target includes: Determine the first weight coefficient corresponding to the regional cost-benefit ratio target, the second weight coefficient corresponding to the economic benefit target, and the third weight coefficient corresponding to the ecological benefit target of the current configuration; Determine the first target value under the regional cost-benefit ratio target using the cost-benefit ratio parameter, and obtain the first evaluation value by weighting the first target value using the first weight coefficient; Determine the second target value under the economic benefit target using the economic benefit parameter, and obtain the second evaluation value by weighting the second target value using the second weight coefficient; Determine the third target value under the ecological benefit target using the ecological benefit parameter, and obtain the third evaluation value by weighting the third target value using the third weight coefficient; Add the first evaluation value, the second evaluation value, and the third evaluation value to obtain the multi-target evaluation value.

4. The method of claim 3, wherein, The determination of the first weight coefficient corresponding to the regional cost-benefit ratio target, the second weight coefficient corresponding to the economic benefit target, and the third weight coefficient corresponding to the ecological benefit target of the current configuration includes: Obtain a plurality of preset weight combinations; Respectively read the corresponding first weight coefficient, second weight coefficient, and third weight coefficient from each weight combination; Based on the multi-target evaluation values, the target protection measures corresponding to each spatial evaluation unit are determined. Based on the multi-target evaluation values corresponding to each weight combination, the target protection measures corresponding to each spatial evaluation unit and each weight combination are respectively determined.

5. The method of claim 2, wherein, The use of the cost parameter, the economic benefit parameter, and the ecological benefit parameter to generate a cost-benefit ratio parameter includes: Add the economic benefit parameter and the ecological benefit parameter to obtain a total benefit parameter; Determine the net present value using the difference between the total benefit parameter and the cost parameter; Determine the investment recovery period based on the net present value; Determine the benefit-cost ratio corresponding to various black soil protection measures using the ratio of the total benefit parameter to the cost parameter; Calculate the dynamic cost-benefit ratio using the net present value, the investment recovery period, and the benefit-cost ratio to obtain the cost-benefit ratio parameter.

6. The method of claim 1, wherein, Before the adopting the preset regional cost-effectiveness ratio target, economic benefit target and ecological benefit target to evaluate the implementation of various black soil protection measures in each of the spatial evaluation units, the method further comprises: Evaluating the implementation of various black soil protection measures in each of the spatial evaluation units by using spatial constraint conditions and time constraint conditions; Filtering black soil protection measures that do not meet the spatial constraint conditions and black soil protection measures that do not meet the time constraint conditions.

7. A resource data processing device for black soil regions based on a geographic information system, characterized in that, The device comprises: a data acquisition module configured to acquire remote sensing image data, soil attribute data and land use data of a black soil region; a data analysis module configured to determine a terrain slope by using the remote sensing image data, determine a soil type by using the soil attribute data, and determine a land use mode by using the land use data; a unit division module configured to divide the black soil region into a plurality of relatively homogeneous spatial evaluation units based on the soil type, the terrain slope and the land use mode; a measure decision module configured to adopt a preset regional cost-effectiveness ratio target, an economic benefit target and an ecological benefit target to evaluate the implementation of various black soil protection measures in each of the spatial evaluation units, and determine a target protection measure corresponding to each of the spatial evaluation units; a scheme forming module configured to configure a corresponding target protection measure in each of the spatial evaluation units, and form a spatial configuration scheme corresponding to the black soil region; The unit division module is further configured to: acquire a digital elevation model of the black soil region, the digital elevation model being a raster layer; perform rasterization processing on the terrain slope to obtain a slope pixel value, and store the slope pixel value at a corresponding pixel position in the raster layer to form a slope raster map; perform rasterization processing on the soil type to obtain a type pixel value, and store the type pixel value at a corresponding pixel position in the raster layer to form a type raster map; perform rasterization processing on the land use mode to obtain a mode pixel value, and store the mode pixel value at a corresponding pixel position in the raster layer to form a mode raster map; superimpose the slope raster map, the type raster map and the mode raster map to obtain a superimposed raster map, the superimposed raster map comprising a plurality of initial relatively homogeneous units, the slope pixel value, the type pixel value and the mode pixel value at each pixel position in each of the relatively homogeneous units being the same; in a case where the area of the relatively homogeneous unit is smaller than a preset area threshold, determine a relatively homogeneous unit adjacent to the relatively homogeneous unit in the superimposed raster map, and combine the relatively homogeneous unit and the adjacent relatively homogeneous unit to obtain the spatial evaluation unit.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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