Underground reservoir multi-source joint allocation optimization method

Abstract

This invention discloses a method for joint allocation and optimization of multiple water sources in underground reservoirs, specifically relating to the field of water resource allocation technology. The method includes basic parameter acquisition and spatial coordinate alignment, grid cell dual-accessibility attribute labeling, grid-based reservoir capacity effectiveness classification and twin mapping construction, full-cycle supply and demand parameter collection and time-period segmentation, and time-period supply and demand matching and allocation scheme generation. This invention unifies the spatial benchmark of multi-source parameters through cross-data source spatial coordinate matching and alignment, achieving precise spatial correspondence between grid-level hydrogeological and engineering node parameters. It links the spatial matching of single grid cells with recharge and extraction nodes to aquifer parameters for accessibility labeling, classifies reservoir capacity effectiveness based on dual thresholds, constructs a reservoir capacity mapping with dual constraints, segments the entire cycle into equal-length time periods for matching supply and demand parameters, completes time-period supply and demand matching and reservoir capacity iterative updates, and achieves temporal linkage between recharge and extraction, accurately matching the spatiotemporal distribution of multi-source water supply and irrigation water demand.

Description

Technical Field

[0001] This invention relates to the field of water resource allocation technology, and in particular to an optimized method for the joint allocation of multiple water sources in an underground reservoir. Background Technology

[0002] The field of water resource allocation technology encompasses sub-fields such as multi-source joint scheduling, spatiotemporal optimization of water resource allocation, agricultural irrigation water management, and groundwater storage and utilization. It is a core technological system for coordinating regional water supply and demand balance, alleviating water supply-demand contradictions, improving water resource utilization efficiency, and ensuring regional water security. This technological field focuses on the spatiotemporal distribution characteristics of different types of water sources within a region, addressing the matching problem between water user demand and water inflow processes. It involves related technical research and engineering applications, including water source supply capacity calculation, water demand verification, water transmission and distribution path planning, and water volume scheduling scheme formulation. It is widely applied in water resource management across various scenarios such as agricultural irrigation, industrial production, domestic water use, and ecological water replenishment, and serves as the core technological support for achieving joint scheduling of surface water and groundwater, optimized allocation of external water transfers, and local water resources.

[0003] The multi-source joint allocation optimization method for underground reservoirs refers to the technical approach for formulating joint allocation and scheduling schemes for multiple water sources based on the storage capacity of underground reservoirs and the supply conditions of multiple water sources, targeting agricultural irrigation water demand. This method addresses technical matters including the verification of underground reservoir storage capacity, analysis of multi-source supply characteristics, research on agricultural irrigation water demand patterns, and the development of multi-source replenishment and extraction schemes. First, the characteristic water levels and corresponding storage capacities of the underground reservoir are delineated based on the hydrogeological survey results of the reservoir area, and a water level-storage capacity correspondence is established. Then, the water diversion parameters of various external water sources and the infiltration and replenishment capacity of rivers, canals, and ponds in the reservoir area are statistically analyzed. Combined with the crop planting structure and irrigation system of the reservoir area, the agricultural irrigation water demand in different time periods is determined. Finally, based on the natural replenishment patterns and storage operation principles of the reservoir area, the operation stages of the underground reservoir are divided, and corresponding water diversion, replenishment, and extraction water allocation schemes are formulated for each stage.

[0004] Existing technologies adopt an integrated reservoir capacity accounting model, which only delineates the overall characteristic water level and corresponding reservoir capacity, without characterizing the spatial heterogeneity of reservoir capacity units. This makes it impossible to identify the differences in replenishment and extraction capacity of reservoir capacity units at different locations, resulting in poor adaptability of the assessed regulation and storage capacity to actual engineering conditions. The model of independently formulating replenishment and extraction plans in stages fails to achieve real-time linkage between replenishment and extraction and reservoir capacity changes, making it unable to adapt to the temporal changes in irrigation water demand. This can easily lead to water supply gaps and reservoir capacity idleness. Static plans cannot smooth out reservoir capacity fluctuations, affecting the hydrogeological environment of the reservoir area. Summary of the Invention

[0005] The main objective of this invention is to provide a method for the joint allocation and optimization of multiple water sources in underground reservoirs, which can effectively solve the problems mentioned in the background.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The method for optimizing the joint allocation of multiple water sources in underground reservoirs includes the following steps: S1. Obtain the corresponding parameters of underground reservoir hydrogeological grid, recharge node and extraction well, associate the spatial coordinates of all parameters, and generate a standardized basic ledger of hydrogeological grid unit. S2. Call the standardized basic ledger of hydrogeological grid units, filter the nearest node of the grid, associate the aquifer parameters, mark the grid accessibility attributes, and obtain the grid unit supply-exploitation accessibility attribute ledger. S3. Call the grid cell supply-extraction accessibility attribute ledger, obtain the supply and extraction accessibility thresholds, combine the underground water reservoir regulation and storage function zones, filter grid attribute matching items, mark the reservoir capacity classification, associate the full grid parameters, and establish a dual-constraint reservoir capacity digital twin mapping body. S4. Call the dual-constraint reservoir capacity digital twin mapping body to obtain the corresponding parameters of multiple water sources and irrigation water demand throughout the entire cycle, filter the time periods according to the preset rules, and obtain the list of supply and demand matching time periods throughout the entire cycle; S5. Call the full-cycle supply and demand matching time period list and the dual-constraint reservoir capacity digital twin mapping body, combine the irrigation district water distribution control unit, filter supply and demand matching items, associate allocation items, and generate a multi-source joint allocation optimization scheme for underground reservoirs.

[0007] Preferably, the associated spatial coordinates of all parameters in S1 are specifically as follows: A unified planar rectangular coordinate system is adopted to perform one-to-one matching and alignment of the spatial coordinates of hydrogeological grid units, surface infiltration recharge nodes, and groundwater extraction wells, thereby completing the spatial dimension normalization of all parameters.

[0008] Preferably, S2 specifically includes: For a single grid cell, the nearest horizontally located surface infiltration recharge node and groundwater extraction well are matched, and the aquifer permeability coefficient and hydraulic gradient parameters of the corresponding connected path are associated to complete the independent attribute labeling of grid cell recharge accessibility and extraction accessibility.

[0009] Preferably, S3 specifically includes: Matching and judging the replenishment accessibility attribute and extraction accessibility attribute of a single grid cell with the corresponding threshold is performed to complete the hierarchical labeling of the reservoir capacity effectiveness of the grid cell. The hierarchical labeling results are bound one by one with the spatial coordinates, water storage volume and formation parameters of the grid cell to construct a reservoir capacity digital twin mapping body with replenishment-extraction dual constraint attributes.

[0010] Preferably, after establishing the dual-constraint reservoir capacity digital twin mapping body, the method further includes: setting the grid cell data marked as dual effective storage capacity in the mapping body as the core constraint of multi-source joint allocation operation, and setting the grid cell data of other classified reservoir capacities as the boundary constraints of multi-source joint allocation operation.

[0011] Preferably, S4 specifically comprises: Based on the growth period of irrigated crops, the entire irrigation cycle is divided into continuous calculation periods of equal length. The available water supply parameters and irrigation water demand parameters of each period are matched to form a list of supply and demand matching periods for the entire cycle.

[0012] Preferably, S5 specifically includes: Taking a single calculation period as a unit, the system performs matching judgment between irrigation water demand and reservoir supply, completes the allocation of replenishment and extraction volume for the corresponding period, synchronously updates the reservoir capacity status of the dual-constraint reservoir digital twin mapping body, completes iterative calculations for all periods of the entire cycle in chronological order, and generates a multi-source joint allocation optimization scheme.

[0013] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves precise spatial correspondence between raster-level hydrogeological and engineering node parameters by unifying the spatial benchmark of multi-source parameters through cross-data source spatial coordinate matching and alignment. It also enables the spatial matching and linkage of single raster and recharge / exploitation node spatial parameters to complete the accessibility marking of aquifer parameters, completes the reservoir capacity effectiveness classification based on dual thresholds, constructs a reservoir capacity mapping body with dual constraints, and segments the supply and demand parameters for matching in equal time periods throughout the entire cycle. It completes the supply and demand matching and reservoir capacity iterative update in each time period, realizes the temporal linkage of recharge and extraction, and accurately matches the spatiotemporal distribution of multiple water source supply and irrigation water demand. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall process of the present invention. Detailed Implementation

[0015] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0016] Example 1, as Figure 1 As shown, the method for optimizing the joint allocation of multiple water sources in an underground reservoir includes the following steps: S1. Basic parameter acquisition and spatial coordinate alignment: Obtain the spatial coordinates of the underground reservoir hydrogeological grid unit, aquifer permeability coefficient, water yield, top elevation, and bottom elevation; obtain the spatial coordinates of surface infiltration recharge nodes and groundwater extraction wells; complete cross-data source coordinate matching and alignment for the spatial identifiers of all parameters; and generate a standardized basic ledger of hydrogeological grid units. S2. Raster Unit Dual Accessibility Attribute Marking: Call the standardized basic ledger of hydrogeological raster units, and for the location data of raster and node, filter out the nearest supply node and mining node corresponding to each raster. For the aquifer parameters of the area corresponding to the node, associate the permeability coefficient and hydraulic gradient data, mark the accessibility attribute of the raster, and obtain the raster unit supply-mining accessibility attribute ledger. S3. Raster capacity effectiveness classification and twin mapping construction: Call the raster unit replenishment-extraction accessibility attribute ledger to obtain replenishment accessibility threshold and extraction accessibility threshold. For the raster attribute data, combine the underground water reservoir regulation and storage function zoning, filter the attribute matching items of a single raster according to the threshold data, mark the raster capacity classification, associate all raster parameters, and establish a dual-constraint digital twin mapping of the capacity. S4. Collection of supply and demand parameters and time period segmentation throughout the entire cycle: Call the dual-constraint reservoir capacity digital twin mapping body to obtain the available water volume, water diversion cost, total irrigation water demand and spatial distribution data of water demand throughout the entire irrigation cycle. According to the preset time period rules, select the time period segments that meet the requirements and combine them into time period records that match supply and demand to obtain a list of time periods that match supply and demand throughout the entire cycle. S5. Generation of supply and demand matching and allocation schemes for each time period: Call the full-cycle supply and demand matching time period list, call the dual-constraint reservoir capacity digital twin mapping body, combine irrigation district water distribution control units for the supply and demand data of each time period, filter the matching items for a single time period based on the supply and demand data, associate the allocation items of replenishment and extraction, mark the reservoir capacity status, and generate a multi-source joint allocation optimization scheme for underground water reservoirs.

[0017] The spatial coordinates associated with all parameters in S1 are specifically as follows: A unified planar rectangular coordinate system is adopted to perform one-to-one matching and alignment of the spatial coordinates of hydrogeological grid units, surface infiltration recharge nodes, and groundwater extraction wells, thereby completing the spatial dimension normalization of all parameters.

[0018] Specifically, a well-irrigated underground reservoir in the North China Plain was selected as the implementation scenario, with a total reservoir area of ​​120 km². 2The dataset of 100m×100m hydrogeological raster cells within the reservoir area was used to extract the Gauss-Kruger 3-degree zone projection Cartesian coordinates corresponding to the center point of each raster cell. The X-axis value is in meters, the Y-axis value is in meters, the projection zone number is 39, and the central meridian longitude is 117 degrees. The dataset of 20 surface infiltration recharge nodes within the reservoir area was used to extract the Cartesian coordinates of the center point of the infiltration surface of each infiltration recharge node corresponding to the same projection system. The dataset of 860 agricultural irrigation groundwater extraction wells within the reservoir area was used to extract the Cartesian coordinates of the center point of each extraction well. A projection system consistency check was performed on all extracted coordinate data, and 3-degree zone projections with inconsistent projection zone numbers were filtered out. For the coordinate data of the wellhead, the Gaussian projection forward and inverse calculation formulas are used to perform projection zone transformation operations on the mismatched coordinates. In the formula, B is the geodetic latitude of the corresponding point in degrees, L is the geodetic longitude of the corresponding point in degrees, L0 is the longitude of the central meridian of the target projection zone in degrees, X is the transformed vertical axis coordinate in meters, and Y is the transformed horizontal axis coordinate in meters. After the transformation, the projection datum, central meridian, and zone number of all coordinate data are completely consistent. The coordinate matching tolerance value is set to 0.5 meters. For the coordinate data of each grid cell, planar position matching with the coordinate data of all supply nodes and wellheads is performed to complete the spatial position association and correspondence of all parameters and complete the spatial dimension normalization processing of all parameters.

[0019] S2 specifically refers to: For a single grid cell, the nearest horizontally located surface infiltration recharge node and groundwater extraction well are matched, and the aquifer permeability coefficient and hydraulic gradient parameters of the corresponding connected path are associated to complete the independent attribute labeling of grid cell recharge accessibility and extraction accessibility.

[0020] Furthermore, the aforementioned 120km was selected. 2Using a single hydrogeological grid unit of a well-irrigated underground reservoir as the implementation object, the plane coordinates of the grid unit's center point are X=4325600m, Y=39521300m. The standardized basic ledger data of this grid unit is retrieved to extract the aquifer permeability coefficient, specific yield, top elevation, and bottom elevation parameters corresponding to the grid unit. The plane coordinate data of all surface infiltration recharge nodes in the reservoir area are retrieved, and the horizontal distance between this grid unit and each recharge node is calculated. The recharge node with the smallest horizontal distance is selected; its plane coordinates are X=4325100m, Y=39522100m, corresponding to a horizontal distance of 943.4m. The aquifer permeability coefficient data of the continuous grids between this grid unit and the corresponding recharge node are retrieved, and the average permeability coefficient within the connected path is extracted to be 8.2m / d. The data for this connected path is then retrieved. The hydraulic gradient data corresponding to the path is used to extract an average hydraulic gradient of 0.0021. Matching and tagging the recharge accessibility attribute of this grid cell is then performed. The planar coordinate data of all groundwater extraction wells in the reservoir area is retrieved, and the horizontal distance between this grid cell and each extraction well is calculated. The extraction well with the smallest horizontal distance is selected. The planar coordinates of this well are X=4326200m and Y=39520800m, corresponding to a horizontal distance of 781.0m. The aquifer permeability coefficient data of the continuous grid between this grid cell and the corresponding extraction well is retrieved, and the average permeability coefficient within the connected path is extracted to be 7.6m / d. The hydraulic gradient data corresponding to this connected path is retrieved, and the average hydraulic gradient is extracted to be 0.0018. Matching and tagging the extraction accessibility attribute of this grid cell is then performed, completing the independent attribute tagging of the grid cell's recharge accessibility and extraction accessibility.

[0021] S3 specifically refers to: Matching and judging the replenishment accessibility attribute and extraction accessibility attribute of a single grid cell with the corresponding threshold is performed to complete the hierarchical labeling of the reservoir capacity effectiveness of the grid cell. The hierarchical labeling results are bound one by one with the spatial coordinates, water storage volume and formation parameters of the grid cell to construct a reservoir capacity digital twin mapping body with replenishment-extraction dual constraint attributes.

[0022] Select the aforementioned 120km 2Using all grid cells of a well-irrigated underground reservoir as the implementation object, the recharge accessibility and extraction accessibility attribute data of all grid cells are called. Referring to the "Specifications for Hydrological Calculation of Water Conservancy and Hydropower Projects," the recharge accessibility threshold and extraction accessibility threshold are set to 0.6. For a single grid cell, a matching judgment is performed between the cell's recharge accessibility attribute value and the recharge accessibility threshold, and a matching judgment is performed between the cell's extraction accessibility attribute value and the extraction accessibility threshold. When both attribute values ​​are greater than or equal to the corresponding threshold, the grid cell's effective storage capacity is classified and marked. When the recharge accessibility attribute value is greater than or equal to the corresponding threshold and the extraction accessibility attribute value is less than the corresponding threshold, the following is executed: The grid cell is marked as having only available replenishment capacity. When the replenishment accessibility attribute value is less than the corresponding threshold and the extraction accessibility attribute value is greater than or equal to the corresponding threshold, the grid cell is marked as having only available extraction capacity. When both attribute values ​​are less than the corresponding threshold, the grid cell is marked as having double invalid capacity. After marking all 12,000 grid cells, the spatial coordinate data, water storage volume data, aquifer permeability coefficient data, water yield data, top elevation data, and bottom elevation data of each grid cell are called. The marking results of each grid cell are bound one-to-one with the aforementioned full parameters to construct a digital twin mapping of reservoir capacity with replenishment-extraction dual constraint attributes.

[0023] After establishing the dual-constraint reservoir capacity digital twin mapping body, it also includes: setting the grid cell data marked as dual effective storage capacity in the mapping body as the core constraint of multi-source joint allocation operation, and setting the grid cell data of other classified reservoir capacities as the boundary constraint of multi-source joint allocation operation.

[0024] S4 specifically refers to: Based on the growth period of irrigated crops, the entire irrigation cycle is divided into continuous calculation periods of equal length. The available water supply parameters and irrigation water demand parameters of each period are matched to form a list of supply and demand matching periods for the entire cycle.

[0025] Furthermore, the aforementioned 120km was selected. 2The winter wheat planting area corresponding to the well-irrigated underground reservoir was selected as the implementation target. The entire growth period of winter wheat in the irrigation area was set from October 8th of each year to June 5th of the following year, with a total duration of 242 days. Based on the entire growth period of winter wheat, the entire irrigation cycle was divided into continuous calculation periods of equal length, with the duration of each calculation period set to 1 day. A total of 242 continuous calculation periods were generated throughout the cycle. For each continuous calculation period, the multi-source water supply parameter dataset of the reservoir area was called to extract the corresponding surface water diversion volume, reclaimed water supply volume, etc. The available floodwater volume, water diversion cost, and replenishment accessibility parameters of the corresponding replenishment grid cells for each water diversion node are considered. For each continuous calculation period, the irrigation water demand parameter dataset of the irrigation district is called to extract the total irrigation water demand, spatial distribution of water demand, and extraction accessibility parameters of the corresponding grid cells for that period. Each calculation period is matched and bound one-to-one with the corresponding multi-source supply parameters and irrigation water demand parameters. The winter wheat greening period corresponds to the 120th to 142nd calculation period, and the total irrigation water demand for a single period is set to 12,000 m³. 3 The jointing stage corresponds to the 143rd to 168th calculation periods, with the total irrigation water demand for a single period set at 28,000 m³. 3 The grouting period corresponds to the 169th to 212th calculation periods, with the total irrigation water demand for a single period set at 22,000 m³. 3 The maximum allowable surface water diversion volume for a single period throughout the entire cycle is set at 50,000 m³. 3 The maximum supply capacity of reclaimed water per period is set at 0.8 million m³. 3 Complete the matching of supply and demand parameters for all time periods throughout the entire cycle, and form a list of supply and demand matching time periods for the entire cycle.

[0026] S5 specifically refers to: Taking a single calculation period as a unit, the system performs matching judgment between irrigation water demand and reservoir supply, completes the allocation of replenishment and extraction volume for the corresponding period, synchronously updates the reservoir capacity status of the dual-constraint reservoir digital twin mapping body, completes iterative calculations for all periods of the entire cycle in chronological order, and generates a multi-source joint allocation optimization scheme.

[0027] The entire irrigation cycle, consisting of the aforementioned 242 consecutive calculation periods, was selected as the implementation object. The full-cycle supply-demand matching period list and the dual-constraint reservoir capacity digital twin mapping were invoked. The initial dual-effective storage capacity data of the first calculation period of the entire cycle was used as the starting point for calculation, with the initial dual-effective storage capacity set at 9.2 million m³. 3Taking a single operation period as the unit, the system calls the total irrigation water demand, spatial distribution of water demand, and exploitability level parameters of the corresponding grid cells of the exploitation nodes for that period. It then performs spatial allocation of the total irrigation water demand for that period to the corresponding exploitability level grid cells, obtaining the target exploitation volume data for each exploitation grid cell in that period. Next, it calls the remaining available storage capacity data of the corresponding exploitation grid cell in the dual-constraint storage capacity digital twin mapping, and performs a size matching judgment between the total target exploitation volume and the total remaining available storage capacity data for that period. If the total target exploitation volume is greater than the total remaining available storage capacity, it executes the operation of the storage capacity gap value for that period. The calculation involves calling the multi-source water supply parameter dataset corresponding to the specified time period, spatially allocating the reservoir capacity gap value to the corresponding replenishment reach level grid cells, obtaining the water diversion and replenishment data for each water source during the specified time period, mapping the water diversion and replenishment data to the corresponding grid cells in the dual-constraint reservoir capacity digital twin mapping body, and updating the remaining reservoir capacity of the corresponding grid cells. When the total target extraction volume is less than or equal to the total remaining supplyable reservoir capacity, the calculation involves calling the total remaining reservoir capacity data for the entire reservoir area from the dual-constraint reservoir capacity digital twin mapping body, performing a size matching judgment between the total remaining reservoir capacity and the reservoir capacity safety upper limit value, which is set at 12.8 million m³. 3 When the total remaining reservoir capacity exceeds the safe upper limit, the redundant reservoir capacity value for that period is calculated, and the incremental allocation of irrigation extraction for that period is processed. After the allocation of replenishment and extraction for that period is completed, the remaining reservoir capacity data of the full grid cell of the dual-constraint reservoir digital twin mapping body is updated. The updated reservoir capacity data is set as the calculation benchmark for the next adjacent calculation period. The aforementioned matching, allocation, and update processes are repeated in chronological order until all iterative calculations for the entire cycle of 242 calculation periods are completed, generating a multi-source joint allocation optimization scheme.

[0028] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended technical solutions and their equivalents.

Claims

1. A method for optimizing the joint allocation of multiple water sources in an underground reservoir, characterized in that, The method includes the following steps: S1. Obtain the corresponding parameters of underground reservoir hydrogeological grid, recharge node and extraction well, associate the spatial coordinates of all parameters, and generate a standardized basic ledger of hydrogeological grid unit. S2. Call the standardized basic ledger of hydrogeological grid units, filter the nearest node of the grid, associate the aquifer parameters, mark the grid accessibility attributes, and obtain the grid unit supply-exploitation accessibility attribute ledger. S3. Call the grid cell supply-extraction accessibility attribute ledger, obtain the supply and extraction accessibility thresholds, combine the underground water reservoir regulation and storage function zones, filter grid attribute matching items, mark the reservoir capacity classification, associate the full grid parameters, and establish a dual-constraint reservoir capacity digital twin mapping body. S4. Call the dual-constraint reservoir capacity digital twin mapping body to obtain the corresponding parameters of multiple water sources and irrigation water demand throughout the entire cycle, filter the time periods according to the preset rules, and obtain the list of supply and demand matching time periods throughout the entire cycle; S5. Call the full-cycle supply and demand matching time period list and the dual-constraint reservoir capacity digital twin mapping body, combine the irrigation district water distribution control unit, filter supply and demand matching items, associate allocation items, and generate a multi-source joint allocation optimization scheme for underground reservoirs.

2. The method for optimizing the joint allocation of multiple water sources in underground reservoirs according to claim 1, characterized in that: The spatial coordinates of all associated parameters in S1 are specifically as follows: A unified planar rectangular coordinate system is adopted to perform one-to-one matching and alignment of the spatial coordinates of hydrogeological grid units, surface infiltration recharge nodes, and groundwater extraction wells, thereby completing the spatial dimension normalization of all parameters.

3. The method for optimizing the joint allocation of multiple water sources in an underground reservoir according to claim 1, characterized in that: Specifically, S2 is: For a single grid cell, the nearest horizontally located surface infiltration recharge node and groundwater extraction well are matched, and the aquifer permeability coefficient and hydraulic gradient parameters of the corresponding connected path are associated to complete the independent attribute labeling of grid cell recharge accessibility and extraction accessibility.

4. The method for optimizing the joint allocation of multiple water sources in underground reservoirs according to claim 1, characterized in that: Specifically, S3 is: Matching and judging the replenishment accessibility attribute and extraction accessibility attribute of a single grid cell with the corresponding threshold is performed to complete the hierarchical labeling of the reservoir capacity effectiveness of the grid cell. The hierarchical labeling results are bound one by one with the spatial coordinates, water storage volume and formation parameters of the grid cell to construct a reservoir capacity digital twin mapping body with replenishment-extraction dual constraint attributes.

5. The method for optimizing the joint allocation of multiple water sources in an underground reservoir according to claim 4, characterized in that: After establishing the dual-constraint reservoir capacity digital twin mapping body, the method further includes: setting the grid cell data marked as dual effective storage capacity in the mapping body as the core constraint of multi-source joint allocation operation, and setting the grid cell data of other classified reservoir capacities as the boundary constraint of multi-source joint allocation operation.

6. The method for optimizing the joint allocation of multiple water sources in an underground reservoir according to claim 1, characterized in that: Specifically, S4 is: Based on the growth period of irrigated crops, the entire irrigation cycle is divided into continuous calculation periods of equal length. The available water supply parameters and irrigation water demand parameters of each period are matched to form a list of supply and demand matching periods for the entire cycle.

7. The method for optimizing the joint allocation of multiple water sources in an underground reservoir according to claim 1, characterized in that: Specifically, S5 is: Taking a single calculation period as a unit, the system performs matching judgment between irrigation water demand and reservoir supply, completes the allocation of replenishment and extraction volume for the corresponding period, synchronously updates the reservoir capacity status of the dual-constraint reservoir digital twin mapping body, completes iterative calculations for all periods of the entire cycle in chronological order, and generates a multi-source joint allocation optimization scheme.

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