Multi-scale water resource optimal configuration and efficiency evaluation analysis method for arid region

Abstract

The invention discloses a multi-scale water resource optimal configuration and efficiency evaluation analysis method for an arid region, and relates to the technical field of water resource management and sustainable utilization. The method comprises the steps of collecting and processing comprehensive water resource management data of an arid region to form an arid region water-ecology-society comprehensive data set, and performing scale identification processing on the arid region water-ecology-society comprehensive data set to form a basic scale data set; and performing scale hierarchy decomposition on the basic scale data set to form upper-layer scale data and lower-layer scale data. According to the invention, by collecting and processing comprehensive water resource management data, performing scale identification and decomposing the data into the upper-layer scale data set and the lower-layer scale data set, water resource optimal configuration and efficiency evaluation in the arid region are realized, a system is helped to identify and process spatial distribution and demand change, and more accurate and effective resource management is realized.

Description

Technical Field

[0001] This invention belongs to the field of water resource management and sustainable utilization technology, and in particular relates to a method for multi-scale water resource optimization and efficiency evaluation analysis in arid regions. Background Technology

[0002] Arid regions are characterized by extreme water scarcity and fragile ecosystems, posing severe challenges to water resource management, including acute supply-demand imbalances and intense competition for water use. Current research on water resource allocation often focuses on a single scale or objective, such as allocating water resources only at the administrative level or pursuing maximum economic benefits. These methods sever the multi-level connections between the natural hydrological cycle and the social water use system, failing to accurately reflect the complex flow and transformation of water resources within the "nature-society" dual system.

[0003] Existing technologies, when dealing with multi-scale coupled problems, often employ simple top-down or static aggregation methods, lacking dynamic feedback mechanisms across scales. This results in poor adaptability of configuration schemes in practical implementation. Furthermore, current efficiency assessments often rely on single economic or engineering indicators, neglecting the unique ecological value and social equity attributes of water resources in arid regions. The assessment results are one-sided and cannot support overall sustainability decision-making. These problems make existing methods insufficient to meet the needs of arid regions for systematic, adaptive, and multi-objective balanced refined water resource management. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for multi-scale water resource optimization and efficiency evaluation in arid regions. The technical problem this invention aims to solve is: how to address the issues of lack of dynamic feedback, poor configuration adaptability, and one-sided efficiency evaluation in existing technologies through multi-scale data hierarchical decomposition, bidirectional coupling dynamic feedback mechanism, and ecological-economic-social integrated efficiency evaluation and multi-objective optimization process.

[0005] This invention provides a method for multi-scale water resource optimization and efficiency evaluation analysis in arid regions, comprising: S1. Collect and process comprehensive water resources management data in arid areas to form a comprehensive water-ecology-society dataset for arid areas, and perform scale identification processing on the comprehensive water-ecology-society dataset for arid areas to form a basic scale dataset; S2. Perform scale-level decomposition on the basic scale dataset to form upper-level scale data and lower-level scale data; S3. Perform bidirectional coupling processing on the upper-scale data and the lower-scale data to form a cross-scale dynamic feedback dataset; S4. Extract the parameter data from the arid region water-ecology-society integrated dataset, and perform a weighted summation of the parameter data to form a comprehensive efficiency value; S5. Perform multi-objective optimization on the cross-scale dynamic feedback dataset and the comprehensive efficiency value to form a multi-scale water resource allocation scheme for arid areas; S6. The implementation results of the multi-scale water resource allocation scheme in the arid area are evaluated and processed to form adaptive detection results, and the adaptive detection results are fed back as feedback information to the bidirectional coupling process to form closed-loop control.

[0006] The present invention is further configured such that the integrated water resources management data includes hydrological observation data, ecological environment parameters and social demand data, and the scale identification processing includes spatial partitioning and water demand type classification of the integrated water-ecology-society dataset of the arid area.

[0007] The present invention is further configured such that the scale-level decomposition includes spatial aggregation processing of the spatial coordinates of the basic scale dataset to form spatial unit data. The spatial aggregation processing aggregates discrete data points in the basic scale dataset to corresponding spatial grid units, merges spatially adjacent spatial unit data with the same administrative division to form the upper-level scale data, and uses spatial unit data that does not participate in the merging as the lower-level scale data.

[0008] The present invention is further configured such that there is a bidirectional association between the upper-level scale data and the lower-level scale data, wherein the bidirectional association includes the upper-level scale data decomposing target constraints into lower-level execution constraints to the lower-level scale data, and the lower-level scale data feeding back state changes to the upper-level scale data to form upper-level state updates.

[0009] The present invention is further configured such that the decomposition of target constraints includes matching and allocating target constraints in the upper-level scale data to form the lower-level execution constraints, the matching and allocating process is based on the attributes of the lower-level scale data, and the feedback of state changes includes aggregating the changes in the lower-level scale data during the execution of the lower-level execution constraints, and using the aggregated data as feedback of the state changes of the upper-level scale data to form the upper-level state update, the attributes include water demand attributes, ecological sensitivity attributes, and spatial affiliation attributes, and the changes include water consumption changes, water supply changes, and ecological water replenishment changes.

[0010] The present invention is further configured such that the bidirectional coupling processing includes performing consistency verification processing on the difference between the lower-level execution constraints and the upper-level state update to form the cross-scale dynamic feedback dataset. The consistency verification processing includes comparing the execution amount of the lower-level execution constraints with the actual water usage change of the lower-level scale data to form a comparison result, performing deviation marking processing on the lower-level scale data whose comparison result exceeds a preset dynamic deviation threshold to form a marking result, and performing weighted processing on the marking result, the comparison result, and the upper-level state update to form the cross-scale dynamic feedback dataset.

[0011] The present invention is further configured such that the parameter data includes ecological benefit parameters, economic benefit parameters, and social equity parameters.

[0012] The present invention is further configured such that the formula for calculating the weighted sum is: .

[0013] in, The overall efficiency value is dimensionless. The ecological benefit parameter is dimensionless. The economic benefit parameter is dimensionless. Let the social equity parameter be dimensionless. The ecological benefit weight is dimensionless. Economic benefit weight, dimensionless. For social fairness weight, dimensionless, satisfying .

[0014] The present invention is further configured such that the multi-objective optimization includes sorting the lower-scale data according to the comprehensive efficiency value, wherein the sorting order is to prioritize the allocation of water to the lower-scale data with higher comprehensive efficiency values, determining the range of allocable water for the lower-scale data according to the lower-level execution constraints in the cross-scale dynamic feedback dataset, and, under the premise of satisfying the water change trend indicated by the upper-level state update, determining the allocated water within the allocable range according to the sorting order to form the multi-scale water resource allocation scheme for the arid region.

[0015] The present invention is further configured such that the evaluation indicators of the evaluation process include the degree of water supply satisfaction, the degree of completion of ecological water replenishment, and the degree of social water security.

[0016] The beneficial effects of this invention are as follows: By collecting and processing comprehensive water resource management data, performing scale identification, and decomposing the data into upper and lower scale datasets, this invention achieves optimized allocation and efficiency assessment of water resources in arid areas, helps the system identify and process spatial distribution and demand changes, and realizes more accurate and effective resource management.

[0017] By employing a two-way coupling method involving data at both upper and lower scales, a dynamic feedback mechanism is established to adjust resource allocation in real time. This two-way coupling ensures consistency between enforced constraints and actual water use changes, improving the rational allocation and adaptive management of water resources based on ecological benefits and socio-economic factors. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0019] Figure 1 This is a schematic diagram of the multi-scale water resource optimization allocation system architecture of the present invention.

[0020] Figure 2 This is a flowchart of the scale-level decomposition and bidirectional association of the present invention.

[0021] Figure 3 This is a flowchart illustrating the calculation of the overall efficiency value of the present invention.

[0022] Figure 4 A flowchart for generating the multi-objective optimization and configuration scheme of the present invention is provided. Detailed Implementation

[0023] The technical solutions of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] Example 1 Please see Figures 1-4 This invention provides a method for multi-scale water resource optimization and efficiency evaluation analysis in arid regions, comprising: S1. Collect and process comprehensive water resources management data in arid regions to form a comprehensive water-ecology-society dataset for arid regions. Perform scale identification processing on the comprehensive water-ecology-society dataset for arid regions to form a basic scale dataset. Comprehensive water resources management data includes hydrological observation data, ecological environment parameters, and social demand data. Scale identification processing includes spatial partitioning and water demand type classification of the comprehensive water-ecology-society dataset for arid regions.

[0025] S2. Scale-level decomposition is performed on the base-scale dataset to form upper-level and lower-level scale data. Scale-level decomposition includes spatial aggregation of the spatial coordinates of the base-scale dataset to form spatial cell data. Spatial aggregation aggregates discrete data points in the base-scale dataset into corresponding spatial grid cells. Spatial cells that are spatially adjacent and have the same administrative division are merged to form upper-level scale data, while spatial cells not involved in merging are used as lower-level scale data. There is a bidirectional relationship between the upper-level and lower-level scale data. This bidirectional relationship includes the upper-level scale data decomposing target constraints into lower-level scale data to form lower-level execution constraints, and the lower-level scale data feeding back state changes to the upper-level scale data to form upper-level state updates. Decomposing target constraints involves matching and allocating target constraints in upper-level scale data to form lower-level execution constraints. The matching and allocation process is based on the attributes of the lower-level scale data. Feedback on state changes involves aggregating the changes in lower-level scale data during the execution of lower-level execution constraints. The aggregated data is used as feedback on the state changes of upper-level scale data to form upper-level state updates. Attributes include water demand attributes, ecological sensitivity attributes, and spatial affiliation attributes. Changes include changes in water consumption, water supply, and ecological water replenishment.

[0026] S3. Perform bidirectional coupling processing on upper-scale and lower-scale data to form a cross-scale dynamic feedback dataset. The bidirectional coupling processing includes consistency verification processing of the differences between lower-scale execution constraints and upper-scale state updates to form a cross-scale dynamic feedback dataset. The consistency verification processing includes comparing the execution amount of lower-scale execution constraints with the actual water usage changes in lower-scale data to form a comparison result. Lower-scale data whose comparison results exceed a preset dynamic deviation threshold are marked with deviations to form a marking result. The marking result, comparison result, and upper-scale state update are weighted to form a cross-scale dynamic feedback dataset.

[0027] S4. Extract parameter data from the arid region's integrated water-ecology-society dataset, and perform a weighted summation of the parameter data to form a comprehensive efficiency value. The parameter data includes ecological benefit parameters, economic benefit parameters, and social equity parameters. The formula for the weighted summation is: .

[0028] in, This is the overall efficiency value, dimensionless. For ecological benefit parameters, dimensionless. For economic benefit parameters, dimensionless. For social equity parameters, dimensionless. The ecological benefit weight is dimensionless. Economic benefit weight, dimensionless. For social fairness weight, dimensionless, satisfying .

[0029] S5. A multi-objective optimization process is performed on the cross-scale dynamic feedback dataset and the comprehensive efficiency value to form a multi-scale water resource allocation scheme for arid regions. The multi-objective optimization includes sorting the lower-level scale data according to the comprehensive efficiency value, prioritizing water allocation to lower-level scale data with higher comprehensive efficiency values. The range of allocable water for lower-level scale data is determined based on the lower-level execution constraints in the cross-scale dynamic feedback dataset. Under the premise of satisfying the water change trend indicated by the upper-level state update, the allocated water is determined sequentially within the allocable range according to the sorting order to form a multi-scale water resource allocation scheme for arid regions.

[0030] S6. The implementation results of multi-scale water resource allocation schemes in arid areas are evaluated and processed to form adaptive monitoring results. These results are then used as feedback information to form a closed-loop control system through a two-way coupling process. The evaluation indicators include the degree of water supply sufficiency, the degree of completion of ecological water replenishment, and the degree of social water security.

[0031] This invention improves the accuracy and adaptability of water resource management through multi-scale data analysis and feedback mechanisms, enabling precise identification and response to problems in water resource allocation within complex arid environments. By integrating ecological, economic, and social equity factors, a comprehensive efficiency value is generated, achieving a multi-dimensional balance in water resource allocation. The bidirectional coupling processing of cross-scale dynamic feedback datasets makes water resource management more flexible, allowing for timely adjustments based on discrepancies between actual water consumption and planned allocations, ensuring that water resource demands at different levels are reasonably met.

[0032] Through multi-objective optimization, water resources are prioritized for allocation, with a focus on supporting areas with higher economic benefits, while ensuring the availability of water resources necessary for ecological and environmental needs, thereby improving the overall utilization efficiency of water resources in arid regions. An adaptive monitoring mechanism regularly evaluates water resource allocation plans and uses closed-loop control to provide feedback, continuously optimizing management plans and ensuring continuous improvement and efficient execution of water resource management during implementation.

[0033] Example 2 Please see Figure 2 Based on Example 1, a multi-scale water resource optimization allocation method is used to rationally allocate water resources in arid areas, promote the coordination of ecological protection and social needs, and achieve the sustainable use of water resources. The specific implementation method is as follows: 1. Data Sources and Basis This embodiment uses monitoring data from Ningxia Hui Autonomous Region in 2022 to optimize water resource allocation. Arid regions have extremely low annual precipitation and water resources are scarce; therefore, the scientific and rational allocation of water resources is crucial.

[0034] Hydrological observation data: The hydrological observation data comes from precipitation and evaporation data provided by the China Meteorological Administration and the Ningxia Hydrological Monitoring Station.

[0035] Based on the annual precipitation data for each sub-region in 2022, the following data were obtained: The annual precipitation in sub-region A is 48 mm, in sub-region B it is 55 mm, and in sub-region C it is 53 mm.

[0036] Ecological and environmental parameters: The ecological and environmental parameters are derived from the ecological sensitivity report and remote sensing monitoring data released by the Department of Ecology and Environment of Ningxia Hui Autonomous Region.

[0037] Data basis: The ecological sensitivity of each sub-region was assessed using an ecological sensitivity index model. Specific assessment results are as follows: The ecological sensitivity of sub-region A is 0.70, that of sub-region B is 0.75, and that of sub-region C is 0.72.

[0038] Social demand data: The social demand data comes from the Ningxia Water Resources Bureau, the Agriculture Bureau, and government population census data.

[0039] Based on annual water demand data released by local governments and 2022 census data, the following data was obtained: Sub-region A has an annual water demand of 900,000 cubic meters and a population of 450,000; Sub-region B has an annual water demand of 1,100,000 cubic meters and a population of 600,000; Sub-region C has an annual water demand of 1,000,000 cubic meters and a population of 550,000.

[0040] Based on the above data, the basic dataset is formed: Table 1: Basic Dataset Table.

[0041] 2. Spatial aggregation processing In the spatial aggregation processing stage, the various sub-regions in the basic dataset are spatially aggregated according to watershed or administrative division to form more representative regional data, which is suitable for regional water resource management.

[0042] calculate: Rainfall in the water resources management area: Total water demand in the water resources management area: Average ecological sensitivity of water resource management areas: Population of the water resources management area: 3. Upper-scale data and lower-scale data Upper-scale data: Based on the above aggregation results, the following upper-level data is derived, representing the overall water resource situation in the water resource management area: Table 2: Upper-level scale data table.

[0043] Lower-scale data: Lower-scale data refers to data for each sub-region, further refining water resource allocation and implementation.

[0044] 4. Two-way association Passing constraints from upper to lower layers: Based on the total water demand of 3 million cubic meters for the comprehensive water resources zone, water resources are allocated according to the population ratio of each sub-region.

[0045] Water demand of sub-region A: Water demand of sub-region B: Water demand of sub-region C: Lower layers feed back state changes to upper layers: In practical applications, when the actual water consumption of sub-region A is 950,000 cubic meters, exceeding the original target of 843,800 cubic meters by 106,200 cubic meters, this will be fed back to the upper-level data, leading to an adjustment in the overall water resource demand.

[0046] Updated upper-level data: 5. Matching and Assignment Ecological water replenishment is allocated based on ecological sensitivity. Areas with higher ecological sensitivity receive higher priority for water resource replenishment.

[0047] Based on the regional ecological water demand assessment results, the ecological water replenishment ratio coefficient is controlled within the range of 5%-15%. In this embodiment, 0.10 is selected for calculation. Ecological water replenishment for sub-region A: Ecological water replenishment for sub-region B: Ecological water replenishment for sub-region C: 6. State Change Convergence Processing If, during implementation, the actual water supply to sub-region A decreases by 50,000 cubic meters and the ecological water replenishment decreases by 20,000 cubic meters, the data will be fed back to the upper level for adjustment.

[0048] Updated upper-level data: Total annual water demand: Ecological water replenishment: In summary, this embodiment implemented multi-scale optimized allocation of water resources in arid areas, improved the utilization efficiency of water resources in arid areas, provided a scientific basis for water resource management, and helped to meet the needs of social and economic development under the premise of ecological environmental protection.

[0049] Example 3 Please see Figures 3-4 Based on Examples 1 and 2, this paper proposes a multi-scale water resource optimization allocation method to balance the needs of ecological protection, economic development, and social equity, thereby improving the efficiency of water resource utilization in arid areas of Ningxia Hui Autonomous Region. The specific implementation method is as follows: 1. Ecological benefit parameters Ecological benefit parameters reflect the impact of water resource allocation on the ecological environment, including water quality improvement, wetland restoration, and species protection. Ecological benefits are typically measured by the amount of water used in the ecological restoration area and the effectiveness of the restoration. These are assessed by ecological and environmental monitoring agencies based on the water demand, restoration effects, and ecological restoration index of the ecological restoration project.

[0050] An assessment conducted by the Agricultural Water Resources Bureau based on the effectiveness of agricultural ecological restoration indicates that sub-region A requires an annual water demand of 150,000 cubic meters for maintaining farmland irrigation and ecological restoration. The ecological benefit parameter is 0.75.

[0051] According to the urban environmental monitoring report, sub-region B has relatively low ecological pressure, with an annual water demand of 100,000 cubic meters and an ecological benefit parameter of 0.65.

[0052] According to the ecological restoration project report from the ecological environment monitoring station, the annual ecological water replenishment demand for sub-region C is 200,000 cubic meters, and the ecological benefit parameter is 0.85.

[0053] 2. Economic benefit parameters Economic efficiency parameters reflect the contribution of water resource allocation to regional economic output, and are usually measured by the economic output per unit of water. Determining economic efficiency parameters requires calculating the economic output of various types of water resource use.

[0054] Based on the farmland irrigation efficiency calculations conducted by the Ningxia agricultural department, the annual agricultural output value of sub-region A is 8 million yuan, the annual water demand is 150,000 cubic meters, and the economic benefit parameter is 0.80. The calculation formula is: Economic benefit = Agricultural output value ÷ Water consumption.

[0055] The industrial water usage report shows that sub-region B has an annual economic output of 6 million yuan, an annual water demand of 80,000 cubic meters, and an economic benefit parameter of 0.70.

[0056] According to the local economic census, the annual economic output of sub-region C is 2 million yuan, the annual water demand is 30,000 cubic meters, and the economic benefit parameter is 0.60.

[0057] 3. Social equity parameters Social equity parameters measure the fairness of water resource allocation in meeting the needs of different social groups. Social equity is usually determined by the water needs of those with basic drinking water security, their security status, and the balance of water resource allocation.

[0058] Based on relevant social development statistics, the water resource security needs of households guaranteed basic domestic water use were calculated, and the proportion of people in sub-region A guaranteed basic domestic water use was found to be 25%, with a social equity parameter of 0.80.

[0059] According to the social development report, the proportion of people in sub-region B with guaranteed basic drinking water is 12%, and the social equity parameter is 0.70.

[0060] According to the Social Statistics Yearbook, the proportion of people in sub-region C with guaranteed basic drinking water is relatively low, and the social equity parameter is 0.75.

[0061] 4. Weight Allocation Weights are assigned to water resource management objectives based on the relative importance of each indicator. The specific weights are usually determined by the government and relevant departments based on regional development priorities, actual needs, and policy orientation.

[0062] Ecological benefit weight: Ecological benefits typically play a crucial role in water resource management in arid regions. The weighting of ecological benefits is derived from the policy priorities of the Ningxia Ecological and Environmental Protection Department, reflecting the need for ecological restoration and protection.

[0063] Economic benefit weight: Based on the Ningxia Agricultural Economic Report and local economic development policies, the economic benefits of agricultural and industrial water use have a significant impact on the region's social development.

[0064] Social equity weight: Social equity is an important goal, but in Ningxia's water resource management, it is primarily used to ensure the basic water needs of vulnerable groups, hence its relatively low weight. The weighting of social equity was determined with reference to relevant policies on regional basic public service guarantees and urban and rural water supply security.

[0065] 5. Calculation of overall efficiency value Based on the ecological benefit parameters, economic benefit parameters, social equity parameters, and their weights determined above, the comprehensive efficiency value of each sub-region is calculated by weighted summation. The formula for weighted summation is: .

[0066] in, This is the overall efficiency value, dimensionless. For ecological benefit parameters, dimensionless. For economic benefit parameters, dimensionless. For social equity parameters, dimensionless. The ecological benefit weight is dimensionless. Economic benefit weight, dimensionless. For social fairness weight, dimensionless, satisfying .

[0067] Sub-area A: Subregion B: Subregion C: 6. Water allocation Based on the overall efficiency value, water resources are preferentially allocated to sub-regions with higher overall efficiency values.

[0068] Sub-region A: Due to its higher overall efficiency value, sub-region A will be given priority in water resource allocation, with an allocation of 1 million cubic meters.

[0069] Sub-region C: With higher requirements for ecological restoration and social equity, the allocated water volume is 800,000 cubic meters.

[0070] Sub-region B: The economic benefits are relatively low, but the water resource needs of the population with basic living water security are more urgent, so the allocation is 400,000 cubic meters.

[0071] 7. Result Evaluation The effectiveness of water resource allocation is verified by assessing the degree of water supply satisfaction, the degree of completion of ecological water replenishment, and the degree of social water security.

[0072] Sub-region A: The water demand of the agricultural and basic domestic water security areas is guaranteed, and the water supply satisfaction rate is 100%.

[0073] Sub-region C: The ecological restoration project receives sufficient water support, with a water supply satisfaction rate of 90%.

[0074] Sub-region B: The water needs of the population with basic living water security are guaranteed, and the water supply satisfaction rate is 85%.

[0075] Through the above steps, effective coordination between ecological restoration, agricultural irrigation, and social equity has been achieved. In water resource allocation, the water needs of ecological restoration areas, agricultural water use, and basic domestic water security areas have been fully guaranteed, ensuring the rational allocation and efficient use of water resources. The implementation of this plan has promoted the sustainable management of water resources in arid areas of Ningxia, improved the fairness and rationality of water resource allocation, and is in line with regional development goals.

[0076] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. Methods for multi-scale water resource optimization and efficiency evaluation in arid regions. Includes, characterized in that: S1. Collect and process comprehensive water resources management data in arid areas to form a comprehensive water-ecology-society dataset for arid areas, and perform scale identification processing on the comprehensive water-ecology-society dataset for arid areas to form a basic scale dataset; S2. Perform scale-level decomposition on the basic scale dataset to form upper-level scale data and lower-level scale data; S3. Perform bidirectional coupling processing on the upper-scale data and the lower-scale data to form a cross-scale dynamic feedback dataset; S4. Extract the parameter data from the arid region water-ecology-society integrated dataset, and perform a weighted summation of the parameter data to form a comprehensive efficiency value; S5. Perform multi-objective optimization on the cross-scale dynamic feedback dataset and the comprehensive efficiency value to form a multi-scale water resource allocation scheme for arid areas; S6. The implementation results of the multi-scale water resource allocation scheme in the arid area are evaluated and processed to form adaptive detection results, and the adaptive detection results are fed back as feedback information to the bidirectional coupling process to form closed-loop control.

2. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 1, characterized in that: The integrated water resources management data includes hydrological observation data, ecological environment parameters, and social demand data. The scale identification processing includes spatial partitioning and water demand type classification of the integrated water-ecology-society dataset of the arid region.

3. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 1, characterized in that: The scale-level decomposition includes spatial aggregation processing of the spatial coordinates of the basic scale dataset to form spatial unit data. The spatial aggregation processing aggregates discrete data points in the basic scale dataset into corresponding spatial grid units, merges spatially adjacent spatial unit data with the same administrative division to form the upper-level scale data, and uses spatial unit data that does not participate in the merging as the lower-level scale data.

4. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 1, characterized in that: The upper-level scale data and the lower-level scale data have a bidirectional correlation. The bidirectional correlation includes the upper-level scale data decomposing target constraints into lower-level scale data to form lower-level execution constraints, and the lower-level scale data feeding back state changes to the upper-level scale data to form upper-level state updates.

5. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 4, characterized in that: The decomposition of target constraints includes matching and allocating target constraints in the upper-level scale data to form the lower-level execution constraints. The matching and allocating process is based on the attributes of the lower-level scale data. The feedback of state changes includes aggregating the changes in the lower-level scale data during the execution of the lower-level execution constraints. The aggregated data is used as feedback of the state changes in the upper-level scale data to form the upper-level state update. The attributes include water demand attributes, ecological sensitivity attributes, and spatial affiliation attributes. The changes include changes in water consumption, water supply, and ecological water replenishment.

6. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 4, characterized in that: The bidirectional coupling process includes performing consistency verification processing on the differences between the lower-level execution constraints and the upper-level state updates to form the cross-scale dynamic feedback dataset. The consistency verification processing includes comparing the execution amount of the lower-level execution constraints with the actual water usage changes of the lower-level scale data to form a comparison result, performing deviation marking processing on the lower-level scale data whose comparison results exceed a preset dynamic deviation threshold to form a marking result, and performing weighted processing on the marking result, the comparison result, and the upper-level state update to form the cross-scale dynamic feedback dataset.

7. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 1, characterized in that: The parameter data includes ecological benefit parameters, economic benefit parameters, and social equity parameters.

8. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 7, characterized in that: The formula for calculating the weighted sum is as follows: ， in, The overall efficiency value is... The ecological benefit parameters are as described above. The economic benefit parameter is... For the social equity parameter, Weighting for ecological benefits, Weighted by economic benefits, For the sake of social fairness, to meet .

9. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid areas according to claim 1, characterized in that: The multi-objective optimization includes sorting the lower-scale data according to the comprehensive efficiency value. The sorting order is to prioritize allocating water to lower-scale data with higher comprehensive efficiency values. The range of allocable water for the lower-scale data is determined according to the lower-level execution constraints in the cross-scale dynamic feedback dataset. Under the premise of satisfying the water change trend indicated by the upper-level state update, the allocated water is determined sequentially within the allocable range according to the sorting order to form the multi-scale water resource allocation scheme for the arid area.

10. The method for multi-scale water resource optimization and efficiency evaluation analysis in arid regions according to claim 1, characterized in that: The evaluation indicators for the assessment process include the degree of water supply satisfaction, the degree of completion of ecological water replenishment, and the degree of social water security.

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