A regional distributed integrated optimization method for promoting agricultural sustainability

Abstract

The application discloses a regional distributed comprehensive optimization method for improving agricultural sustainability, which comprises the following steps: acquiring multi-source heterogeneous basic data of a research region, and uniformly converting the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database; simulating the growth process of main crops under preset growth conditions by using an agricultural crop production process model, acquiring crop input and output index data, and establishing a high-precision regional agricultural input and output database in time and space; constructing a multi-objective collaborative optimization model, and simultaneously solving the regional irrigation management strategy and crop planting structure by using a multi-objective optimization algorithm; meanwhile, constructing an agricultural sustainable development comprehensive index based on a standardized index, comprehensively evaluating candidate schemes obtained by solving optimization to determine an optimal scheme; generating a regional agricultural sustainable optimization result, and outputting crop planting structure redistribution data and regional scale agricultural sustainable development evaluation data.

Description

Technical Field

[0001] This invention relates to the field of agricultural resource optimization and sustainable agricultural development technology, and in particular to a regional distributed integrated optimization method for improving agricultural sustainability. Background Technology

[0002] Against the backdrop of water scarcity and climate change, agricultural production faces multiple pressures, including ensuring food security, increasing water resource constraints, and rising greenhouse gas emissions. A clear trade-off exists between resource inputs and environmental effects in agricultural production. Exploring a sustainable agricultural development path that can both guarantee food security and balance economic and environmental goals is a critical issue that urgently needs to be addressed.

[0003] Existing research primarily focuses on improving agricultural productivity through measures such as optimizing irrigation systems or adjusting crop planting structures. However, existing methods often focus on single objective variables, such as maximizing yield or minimizing water consumption, lacking means to synergistically optimize irrigation management and planting structure within a unified framework. Furthermore, existing methods struggle to systematically reflect the complex trade-offs between agricultural production, water resource utilization, and environmental effects, and lack effective comprehensive evaluation systems to quantitatively assess the overall contribution of different optimization schemes to sustainable agricultural development.

[0004] Furthermore, regional-scale agricultural systems exhibit significant spatial heterogeneity in terms of climate conditions, soil characteristics, and resources, making it difficult to directly generalize optimization results obtained from single-point experiments or local studies to regional-scale agricultural management decisions. Therefore, there is an urgent need to establish a comprehensive optimization method for regionally distributed agricultural sustainability, achieving synergistic optimization of irrigation management and crop planting structure within a unified evaluation framework. This aims to improve agricultural production efficiency, resource utilization efficiency, and environmental benefits, thereby promoting sustainable regional agricultural development. Summary of the Invention

[0005] The present invention aims to at least partially solve one of the technical problems in the related art.

[0006] To address this, this invention proposes a regional distributed integrated optimization method to enhance agricultural sustainability. It aims to overcome the shortcomings of existing methods, such as single-objective optimization and lack of a comprehensive evaluation framework, by balancing food security and emission reduction in agricultural production under the constraints of water resources and climate change. The proposed method integrates process simulation, multi-objective optimization, and a sustainable development index to achieve coordinated optimization of irrigation management and crop planting structure, thereby improving agricultural resource utilization efficiency and environmental benefits.

[0007] Another objective of this invention is to propose a regionally distributed integrated optimization device for improving agricultural sustainability.

[0008] The third objective of this invention is to provide a computer device.

[0009] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, this invention proposes a regional distributed integrated optimization method for improving agricultural sustainability, comprising:

[0011] Acquire multi-source heterogeneous basic data of the study area, and convert the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database; Based on the aforementioned regional distributed basic database, the growth process of major crops under preset growth conditions is simulated using an agricultural crop production process model to obtain crop input-output index data and establish a spatiotemporally high-precision regional agricultural input-output database. A multi-objective collaborative optimization model was constructed, and a multi-objective optimization algorithm was used to solve the regional irrigation management strategy and crop planting structure collaboratively. At the same time, a comprehensive agricultural sustainable development index was constructed based on standardized indicators including yield, water resources and environmental effects. The candidate solutions obtained from the optimization solution were comprehensively evaluated to determine the optimal solution. Based on the optimal solution, the results of regional agricultural sustainability optimization are generated, and crop planting structure redistribution data and regional-scale agricultural sustainable development evaluation data are output.

[0012] In one embodiment of the present invention, the step of uniformly converting the multi-source heterogeneous basic data into grid data with a preset spatial resolution includes: Meteorological data, soil data, crop planting area data, water resource data, and species richness data of the study area are obtained as the multi-source heterogeneous basic data; Spatial resampling processing was performed on the meteorological data, soil data, water resource data, and species richness data using a conservative first-order resampling procedure in the climate data manipulator. The resampled data is then uniformly converted to a spatial resolution of [resolution value missing]. The grid data is used to obtain the standardized grid dataset required to construct the regional distributed basic database.

[0013] In one embodiment of the present invention, the step of simulating the growth process of major crops under preset growth conditions using an agricultural crop production process model includes: The growth process of major crops under non-water stress conditions was simulated using the python-APSIM model; Crop yield, nutrition, water consumption, and global warming potential data are extracted as input-output indicators for the crops, and a high-precision spatiotemporal regional agricultural input-output database is established.

[0014] In one embodiment of the present invention, the step of constructing a multi-objective collaborative optimization model and using a multi-objective optimization algorithm to collaboratively optimize regional irrigation management strategies and crop planting structures includes: The irrigation optimization condition is set to implement irrigation when the soil moisture deficit in the 0-60 cm soil layer of all grid cells reaches a set threshold, and the irrigation water is replenished to the upper limit of soil moisture drainage. A multi-objective collaborative optimization model is constructed with the optimization objectives of minimizing the total regional global warming potential, maximizing the total crop yield, and maximizing the regional irrigation water productivity, and constraints are set for crop planting area and crop yield. The multi-objective collaborative optimization model is iteratively solved using a reference-point-based non-dominated sorting genetic algorithm to obtain a set of candidate solutions that satisfy the constraints of crop planting area and crop yield.

[0015] In one embodiment of the present invention, the construction of a comprehensive agricultural sustainable development index based on standardized indicators including dimensions of yield, water resources, and environmental effects includes: Five standardized indicators—yield gap, irrigation water demand, irrigation water productivity, global warming potential, and species richness—are integrated. The range standardization method is used to make each indicator dimensionless. A comprehensive index for sustainable agricultural development is constructed through weighted summation, which is used to quantitatively evaluate and select candidate optimization schemes.

[0016] To achieve the above objectives, another aspect of the present invention proposes a regionally distributed integrated optimization device for improving agricultural sustainability, comprising: The data acquisition and preprocessing module is used to acquire multi-source heterogeneous basic data of the study area and convert the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database. The process simulation module is used to simulate the growth process of major crops under preset growth conditions based on the regional distributed basic database and agricultural crop production process model, to obtain crop input and output index data, and to establish a spatiotemporally high-precision regional agricultural input and output database. The optimization evaluation module is used to construct a multi-objective collaborative optimization model and use a multi-objective optimization algorithm to solve the regional irrigation management strategy and crop planting structure collaboratively. At the same time, it constructs a comprehensive agricultural sustainable development index based on standardized indicators that include the dimensions of yield, water resources and environmental effects, and comprehensively evaluates the candidate solutions obtained from the optimization solution to determine the optimal solution. The results output module is used to generate regional agricultural sustainability optimization results based on the optimal scheme, and output crop planting structure redistribution data and regional-scale agricultural sustainability evaluation data.

[0017] This invention discloses a regional distributed integrated optimization method and apparatus for improving agricultural sustainability. It combines a crop production process model with a multi-objective optimization algorithm to achieve synergistic optimization of agricultural production, water resource utilization, and environmental effects, overcoming the limitations of traditional single-objective optimization methods. This invention constructs a unified comprehensive index for sustainable agricultural development, enabling quantitative comparison and selection of different optimization schemes, thus improving the scientific nature of decision-making. By performing distributed optimization calculations at a regional scale, this invention fully considers the spatial heterogeneity of climate conditions, soil characteristics, and resource endowments, enhancing the regional applicability of the optimization results. This invention can provide comprehensive decision support for water conservation, yield increase, emission reduction, and ecological protection, contributing to the promotion of sustainable regional agricultural development.

[0018] To achieve the above objectives, a third aspect of this application provides a computer device comprising a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, for implementing a regional distributed integrated optimization method for improving agricultural sustainability as described in the first aspect embodiment.

[0019] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a regional distributed integrated optimization method for improving agricultural sustainability as described in the first aspect embodiment.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a regional distributed integrated optimization method for improving agricultural sustainability according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the spatial pattern of crop yield, water consumption, and carbon emissions according to an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the time-varying changes in crop yield, water consumption, and carbon emissions according to an embodiment of the present invention. Figure 4 This is a schematic diagram illustrating the changes in crop planting patterns after optimization according to an embodiment of the present invention; Figure 5 The optimized county-level SDGs before and after according to embodiments of the present invention agriculture A diagram illustrating the scoring; Figure 6 This is a schematic diagram of a regionally distributed integrated optimization device for improving agricultural sustainability according to an embodiment of the present invention. Figure 7 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] The following description, with reference to the accompanying drawings, describes a regional distributed integrated optimization method, apparatus, equipment, and storage medium for improving agricultural sustainability according to embodiments of the present invention.

[0025] Figure 1 This is a flowchart of a regional distributed integrated optimization method for improving agricultural sustainability according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes, but is not limited to, the following steps: Acquire multi-source heterogeneous basic data of the study area, and convert the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database; Based on the aforementioned regional distributed basic database, the growth process of major crops under preset growth conditions is simulated using an agricultural crop production process model to obtain crop input-output index data and establish a spatiotemporally high-precision regional agricultural input-output database. A multi-objective collaborative optimization model was constructed, and a multi-objective optimization algorithm was used to solve the regional irrigation management strategy and crop planting structure collaboratively. At the same time, a comprehensive agricultural sustainable development index was constructed based on standardized indicators including yield, water resources and environmental effects. The candidate solutions obtained from the optimization solution were comprehensively evaluated to determine the optimal solution. Based on the optimal solution, the results of regional agricultural sustainability optimization are generated, and crop planting structure redistribution data and regional-scale agricultural sustainable development evaluation data are output.

[0026] Specifically, it includes the following steps: The first step is to acquire meteorological data, soil data, crop planting area data, water resource data, and species richness data of the study area, and then convert all types of data into grid data with a spatial resolution of 5′×5′ to construct a regional distributed basic database.

[0027] The second step involves using the Python-APSIM agricultural crop production process model, based on the regional basic data, to simulate the growth process of major crops under non-water stress conditions, obtain relevant input-output indicators such as crop yield, nutrition, water consumption, and global warming potential (GWP), and establish a high-precision spatiotemporal regional agricultural input-output database for major crops.

[0028] The third step is to establish NSGAⅢ-SDG. agriculture (A framework for an optimization-evaluation method based on a non-dominated sorting genetic algorithm for agricultural sustainability index, using a reference point-based approach) includes: An optimization method based on a reference point-dominated sorting genetic algorithm is established to coordinate the optimization of regional irrigation management and crop planting structure. Specifically: Irrigation optimization settings: In the irrigation optimization of the APSIM model, irrigation is implemented when the soil moisture deficit of the 0-60 cm soil layer of all grid cells reaches a set threshold, and the irrigation is replenished to the upper limit of soil moisture drainage. Crop planting structure optimization: A multi-objective collaborative optimization model was constructed, and the non-dominated sorting genetic algorithm NSGAⅢ based on reference points was used for iterative solution. The optimization objectives included: minimizing the total regional global warming potential (TGWP); maximizing the total crop yield (TCP); and maximizing the regional irrigation water productivity (RIWP). The model satisfies the constraints of crop planting area and crop yield.

[0029] Propose an agricultural sustainable development index (SDG) agriculture The evaluation methodology integrates five standardized indicators: yield deficit; irrigation water demand (IWR), irrigation water productivity (IWP), global warming potential (GWP), and species richness. Based on the SDGs... agriculture The index is used for comprehensive evaluation to obtain the optimal solution.

[0030] The fourth step is to obtain the results of regional agricultural sustainable optimization, and output crop planting structure redistribution maps and county-level SDGs. agriculture Scoring potential, water conservation, increased production, improved efficiency, and carbon reduction.

[0031] Taking cotton, corn, and wheat in Northwest China (Xinjiang and the Hexi Corridor) as examples, the specific steps include: The first step is to acquire meteorological data, soil data, crop planting area data, water resource data, and species richness data for the study area. The soil, climate, water resource, and species richness data are then resampled in 5-minute intervals using a conservative first-order resampling procedure in the climate data manipulator to unify the spatial resolution.

[0032] The second step, based on the aforementioned regional baseline data, utilizes the Python-APSIM agricultural crop production process model to simulate the growth processes of cotton, maize, and wheat fields under non-water stress conditions, obtaining crop yield, crop water consumption (ET), irrigation water demand (IWR), and global warming potential (GWP) indicators from 1981 to 2100.

[0033] In the formula, GWP is measured in kg CO2eq / ha.

[0034] This yields the spatial and temporal variations of crop yield, water consumption, and carbon emissions, such as... Figure 2 and Figure 3 As shown, a high-precision spatiotemporal regional agricultural input-output database for major crops is established.

[0035] The third step is to establish NSGAⅢ-SDG. agriculture (A framework for optimization and evaluation methods based on a non-dominated sorting genetic algorithm for reference points and an agricultural sustainability index).

[0036] 1. Establish an optimization method based on a non-dominated sorting genetic algorithm using reference points to collaboratively optimize regional irrigation management and crop planting structure, specifically: Irrigation optimization settings: In the irrigation optimization of the APSIM model, irrigation is implemented when the soil moisture deficit of the 0-60 cm soil layer of all grid cells reaches a set threshold, and the irrigation is replenished to the upper limit of soil moisture drainage. Crop planting structure optimization: A multi-objective collaborative optimization model was constructed, and the non-dominated sorting genetic algorithm NSGAⅢ based on reference points was used for iterative solution. The optimization objectives included: Minimize the total regional global warming potential (TGWP); Maximize total crop yield (TCP); Maximize Regional Irrigation Water Productivity (RIWP);

[0037] In the formula, A ij Y represents the planting area of ​​the i-th crop in the j-th grid. ij , I ij , and GWP ijLet $\mathbf{i}$ represent the yield, irrigation input, and GWP of the $i$-th crop in the $j$-th grid, respectively, in units of kg / ha, mm, and kg CO2eq / ha.

[0038]

[0039] The model satisfies the above constraints on crop planting area and crop yield.

[0040] 2. Propose an agricultural sustainable development index (SDG) agriculture The evaluation method integrates five standardized indicators: yield deficit, irrigation water demand (IWR), irrigation water productivity (IWP), global warming potential (GWP), and species richness.

[0041]

[0042] In the formula, x is the original data value of the selected indicator; max() and min() are the upper and lower limits, respectively; and x′ is the standardized value of the indicator.

[0043] The optimal solution was obtained through a comprehensive evaluation based on the SDGagriculture index.

[0044] The fourth step is to obtain the results of regional agricultural sustainable optimization, and output crop planting structure redistribution maps and county-level SDGs. agriculture Scoring potential, water conservation, increased production, improved efficiency, and carbon reduction, such as Figure 4 and Figure 5 As shown.

[0045] The comprehensive optimization plan can achieve: an increase in crop yield of approximately 2%; a reduction in irrigation water use of approximately 30%; a reduction in global warming potential of approximately 19%; and SDGs. agriculture Index improvement: 30%–60%. This demonstrates that the methodology effectively improves agricultural sustainability.

[0046] Through the above implementation steps, this invention constructs a regional distributed integrated optimization method to enhance agricultural sustainability. Under the background of climate change, it achieves the goals of improving agricultural sustainability, reducing water consumption, reducing carbon emissions, and increasing regional yields, providing a scientific basis for regional agricultural resource allocation and policy formulation.

[0047] To achieve the above embodiments, such as Figure 6 As shown, this embodiment also provides a regional distributed integrated optimization device 10 to improve agricultural sustainability. The device 10 includes a data acquisition and preprocessing module 100, a process simulation module 200, an optimization evaluation module 300, and a result output module 400.

[0048] The data acquisition and preprocessing module 100 is used to acquire multi-source heterogeneous basic data of the study area and convert the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database. The process simulation module 200 is used to simulate the growth process of major crops under preset growth conditions based on the regional distributed basic database and the agricultural crop production process model, to obtain crop input and output index data, and to establish a spatiotemporally high-precision regional agricultural input and output database. The optimization evaluation module 300 is used to construct a multi-objective collaborative optimization model and use a multi-objective optimization algorithm to collaboratively optimize regional irrigation management strategies and crop planting structures. At the same time, it constructs a comprehensive agricultural sustainable development index based on standardized indicators that include yield, water resources and environmental effects, and comprehensively evaluates the candidate solutions obtained from the optimization solution to determine the optimal solution. The result output module 400 is used to generate regional agricultural sustainability optimization results based on the optimal scheme, and output crop planting structure redistribution data and regional-scale agricultural sustainability evaluation data.

[0049] According to an embodiment of the present invention, a regional distributed integrated optimization device for improving agricultural sustainability achieves synergistic optimization of irrigation management and crop planting structure, effectively balancing the trade-offs between food security, water resource utilization and environmental effects; through regional distributed integrated evaluation, it significantly improves agricultural resource utilization efficiency, reduces carbon emissions and enhances the level of regional agricultural sustainable development.

[0050] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 7 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method described above.

[0051] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0052] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0053] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A regional distributed integrated optimization method for improving agricultural sustainability, characterized in that, The method includes: Acquire multi-source heterogeneous basic data of the study area, and convert the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database; Based on the aforementioned regional distributed basic database, the growth process of major crops under preset growth conditions is simulated using an agricultural crop production process model to obtain crop input-output index data and establish a spatiotemporally high-precision regional agricultural input-output database. A multi-objective collaborative optimization model was constructed, and a multi-objective optimization algorithm was used to solve the regional irrigation management strategy and crop planting structure collaboratively. At the same time, a comprehensive agricultural sustainable development index was constructed based on standardized indicators including yield, water resources and environmental effects. The candidate solutions obtained from the optimization solution were comprehensively evaluated to determine the optimal solution. Based on the optimal solution, the results of regional agricultural sustainability optimization are generated, and crop planting structure redistribution data and regional-scale agricultural sustainable development evaluation data are output.

2. The method according to claim 1, characterized in that, The step of uniformly converting the multi-source heterogeneous basic data into grid data with a preset spatial resolution includes: Meteorological data, soil data, crop planting area data, water resource data, and species richness data of the study area are obtained as the multi-source heterogeneous basic data; Spatial resampling processing was performed on the meteorological data, soil data, water resource data, and species richness data using a conservative first-order resampling procedure in the climate data manipulator. The resampled data is then uniformly converted to a spatial resolution of [resolution value missing]. The grid data is used to obtain the standardized grid dataset required to construct the regional distributed basic database.

3. The method according to claim 1, characterized in that, The simulation of the growth process of major crops under preset growth conditions using an agricultural crop production process model includes: The growth process of major crops under non-water stress conditions was simulated using the python-APSIM model; Crop yield, nutrition, water consumption, and global warming potential data are extracted as input-output indicators for the crops, and a high-precision spatiotemporal regional agricultural input-output database is established.

4. The method according to claim 1, characterized in that, The construction of a multi-objective collaborative optimization model and the application of a multi-objective optimization algorithm to collaboratively optimize regional irrigation management strategies and crop planting structures include: The irrigation optimization condition is set to implement irrigation when the soil moisture deficit in the 0-60 cm soil layer of all grid cells reaches a set threshold, and the irrigation water is replenished to the upper limit of soil moisture drainage. A multi-objective collaborative optimization model is constructed with the optimization objectives of minimizing the total regional global warming potential, maximizing the total crop yield, and maximizing the regional irrigation water productivity, and constraints are set for crop planting area and crop yield. The multi-objective collaborative optimization model is iteratively solved using a reference-point-based non-dominated sorting genetic algorithm to obtain a set of candidate solutions that satisfy the constraints of crop planting area and crop yield.

5. The method according to claim 1, characterized in that, The comprehensive agricultural sustainable development index, constructed based on standardized indicators encompassing dimensions of yield, water resources, and environmental effects, includes: Five standardized indicators—yield gap, irrigation water demand, irrigation water productivity, global warming potential, and species richness—are integrated. The range standardization method is used to make each indicator dimensionless. A comprehensive index for sustainable agricultural development is constructed through weighted summation, which is used to quantitatively evaluate and select candidate optimization schemes.

6. A regionally distributed integrated optimization device for improving agricultural sustainability, characterized in that, include: The data acquisition and preprocessing module is used to acquire multi-source heterogeneous basic data of the study area and convert the multi-source heterogeneous basic data into grid data with a preset spatial resolution to construct a regional distributed basic database. The process simulation module is used to simulate the growth process of major crops under preset growth conditions based on the regional distributed basic database and agricultural crop production process model, to obtain crop input and output index data, and to establish a spatiotemporally high-precision regional agricultural input and output database. The optimization evaluation module is used to construct a multi-objective collaborative optimization model and use a multi-objective optimization algorithm to solve the regional irrigation management strategy and crop planting structure collaboratively. At the same time, it constructs a comprehensive agricultural sustainable development index based on standardized indicators that include the dimensions of yield, water resources and environmental effects, and comprehensively evaluates the candidate solutions obtained from the optimization solution to determine the optimal solution. The results output module is used to generate regional agricultural sustainability optimization results based on the optimal scheme, and output crop planting structure redistribution data and regional-scale agricultural sustainability evaluation data.

7. The apparatus according to claim 6, characterized in that, The step of uniformly converting the multi-source heterogeneous basic data into grid data with a preset spatial resolution includes: Meteorological data, soil data, crop planting area data, water resource data, and species richness data of the study area are obtained as the multi-source heterogeneous basic data; Spatial resampling processing was performed on the meteorological data, soil data, water resource data, and species richness data using a conservative first-order resampling procedure in the climate data manipulator. The resampled data is then uniformly converted to a spatial resolution of [resolution value missing]. The grid data is used to obtain the standardized grid dataset required to construct the regional distributed basic database.

8. The apparatus according to claim 6, characterized in that, The simulation of the growth process of major crops under preset growth conditions using an agricultural crop production process model includes: The growth process of major crops under non-water stress conditions was simulated using the python-APSIM model; Crop yield, nutrition, water consumption, and global warming potential data are extracted as input-output indicators for the crops, and a high-precision spatiotemporal regional agricultural input-output database is established.

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement a regional distributed integrated optimization method for improving agricultural sustainability as described in any one of claims 1-5.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a regional distributed integrated optimization method for improving agricultural sustainability as described in any one of claims 1-5.

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