A method for dividing water unit and grid initial water right dynamic allocation for irrigation district agricultural right confirmation

Abstract

The application provides a method for dividing water use units and dynamically allocating initial water rights in an irrigation area, and relates to the technical field of water conservancy and water resources management.The application constructs a directed water network topology by acquiring remote sensing and field data of the irrigation area;divides water grid units by a spatial topology algorithm and establishes a water supply correlation relationship by taking agricultural right data as a hard constraint and taking a canal system water supply coverage area as a soft constraint;calculates irrigation water demand based on crop growth and rainfall data, calculates available water supply based on an effective catchment area and a recharge coefficient, and performs dynamic supply and demand allocation in a five-level ladder order;finally, the method takes water demand satisfaction rate and maximum economic benefit as collaborative targets, adopts a genetic algorithm optimization under the constraints of total amount and water capacity, and realizes scientific and accurate dynamic allocation of initial water rights.

Description

Technical Field

[0001] This invention relates to the field of water conservancy and water resources management technology, specifically a method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner. Background Technology

[0002] With the development of digital agriculture and smart water conservancy, refined management of irrigation district water resources has become an inevitable trend. Currently, water conservancy information technology is penetrating into agricultural land rights confirmation and water rights scheduling, hoping to achieve water network topology construction through remote sensing and GIS technologies. However, existing technologies still face technical barriers in constructing a digital foundation that deeply couples physical water flow direction with property rights boundaries. The integration of water networks and property rights is mostly limited to the shallow data display stage, making it difficult to implement digital transformation and meet the needs of precise scheduling and grid-based management in modern irrigation districts.

[0003] Existing irrigation district water rights allocation systems typically employ a fixed water allocation model based on static administrative divisions and historical data areas. Specifically, current technologies largely rely on conventional GIS to segment available water resources according to administrative boundaries such as townships and villages. At the accounting level, the static total water demand is calculated primarily by multiplying a fixed agricultural water quota by the recorded area. During allocation, water is simply transferred unidirectionally from higher-level nodes to lower-level administrative units based on this static water volume, failing to establish a true connection between the physical water network topology and the water supply relationships between lower-level water-using units.

[0004] The aforementioned existing technologies have significant shortcomings in data processing and algorithm architecture. First, the spatial computing logic relies excessively on administrative boundaries, detaching itself from the actual physical characteristics of the canal system. This makes it difficult for the system to accurately map water network data to underlying property rights plots, and fails to delineate micro-water use units that conform to the boundaries of the confirmed land parcels. Second, the existing models lack real-time perception of dynamic rainfall and water requirements at different crop growth stages, and the calculation method is coarse, failing to accurately assess the dynamic balance of supply and demand. Due to the lack of accurate underlying mapping and dynamic calculation capabilities, the system struggles to perform global optimization calculations under physical constraints such as total water intake control limits and canal flow capacity, resulting in the final water rights allocation instructions deviating significantly from the actual water supply capacity and the actual needs of the land parcels.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a gridded manner, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for dividing irrigation district agricultural water use rights into units and dynamically allocating initial water rights in a grid-based manner, comprising the following steps: Step 1: Acquire remote sensing images, field data, total water intake control limits, and channel water carrying capacity of the irrigation area; extract spatial features from the remote sensing images and field data to obtain spatial vector data of the irrigation area; and construct the directed water network topology of the irrigation area based on the spatial vector data. Step 2: Obtain agricultural land rights confirmation data and canal system water supply coverage area. Using the agricultural land rights confirmation data as a hard constraint and the canal system water supply coverage area as a soft constraint, determine the water use grid units of the irrigation area through a spatial topology algorithm; establish the water supply association relationship between the directed water network topology and the water use grid units. Step 3: Obtain crop growth data and rainfall data for the water use grid units. Calculate the irrigation water demand for each water use grid unit based on the crop growth data and rainfall data. Obtain the effective catchment area of ​​each water supply node. Calculate the available irrigation water volume for each water source based on the effective catchment area. Using the irrigation water demand of each water use grid unit as the initial water demand state, select water supply nodes that supply water to each water use grid unit based on the water supply relationship. Allocate water supply and demand for the water use grid units according to a preset tiered supply and demand sequence to obtain the preliminary allocated net irrigation water volume for the water use grid units. Step 4: Calculate the water demand satisfaction rate of the water use grid unit based on the irrigation water demand and the preliminary allocated net irrigation water volume. With the goal of maximizing the water demand satisfaction rate and maximizing the economic benefits of the irrigation area, construct a multi-objective optimization function. With the total water intake control limit and the channel water carrying capacity as constraints, optimize the multi-objective optimization function to obtain the initial water rights of each water use grid unit.

[0008] Further, spatial features are extracted from the remote sensing images and the field data to obtain spatial vector data of the irrigation area, and a directed water network topology of the irrigation area is constructed based on the spatial vector data. The specific steps are as follows: spatial extraction of the watershed rivers and farmland water conservancy projects in the irrigation area is performed using a geographic information system to obtain spatial vector data of the irrigation area. The spatial vector data includes at least the spatial vector elements corresponding to farmland irrigation projects, watershed rivers and water conveyance channels; The farmland irrigation project is used as a network node, the watershed river and the water conveyance channel are used as network edges, and the direction of water flow in the irrigation area is determined as the network direction to construct a directed water network topology for the irrigation area.

[0009] Furthermore, agricultural land rights confirmation data and canal water supply coverage area are obtained. Using the agricultural land rights confirmation data as a hard constraint and the canal water supply coverage area as a soft constraint, the water use grid unit of the irrigation area is determined by spatial topology algorithm. The specific steps are: extracting the boundaries of the land rights confirmation plots in the agricultural land rights confirmation data and using the boundaries of the land rights confirmation plots as boundary constraints. Extract the water supply coverage area of ​​each water conveyance channel and use the water supply coverage area of ​​the channel system as a constraint on the water supply coverage range; Spatial topological intersection processing is performed on the boundary of the confirmed land parcel and the water supply coverage area of ​​the canal system. Based on the spatial intersection relationship, the water supply coverage area of ​​the canal system corresponding to each confirmed land parcel is determined. The irrigation area is spatially divided using the boundary of the confirmed land parcel as the dividing boundary, resulting in multiple independent and non-overlapping water grid units within the irrigation area.

[0010] Furthermore, the water supply association between the directed water network topology and the water-using grid unit is established. The specific steps are as follows: spatial intersection judgment is performed between the water-using grid unit and the water conveyance channel in the directed water network topology, and the water conveyance channel that has a spatial intersection relationship with the water-using grid unit is determined as the associated water conveyance channel of the water-using grid unit. Using the associated water conveyance channel as the starting point of topology tracing, the upstream network nodes and water conveyance channels are traced in the opposite direction of the water flow in the directed water network topology to determine the water supply nodes and corresponding directed water supply paths that supply water to the water-using grid unit, thereby establishing the water supply association relationship between the water-using grid unit and the water supply nodes, water conveyance channels and directed water supply paths.

[0011] Further, crop growth data and rainfall data of the water use grid unit are obtained, irrigation water demand of the water use grid unit is calculated based on the crop growth data and rainfall data, effective catchment area of ​​each water supply node is obtained, and available irrigation water of each water source is calculated based on the effective catchment area. The specific steps are as follows: determine the growth stage of the crop in the water use grid unit according to the crop growth data, determine the stage water demand according to the growth stage, and calculate the crop water demand based on the stage water demand and a preset water demand allocation coefficient. The effective rainfall amount of the water grid unit is determined based on the rainfall data; The irrigation water requirement of the water-using grid unit is determined based on the difference between the crop water requirement and the effective rainfall. Calculate the water catchment volume of each water supply node based on the effective catchment area corresponding to each water supply node and the rainfall during the preset time period. Obtain the storage coefficient corresponding to each water supply node, correct the water collection volume according to the storage coefficient, obtain the available water volume of the water source corresponding to each water supply node, and determine the corresponding available irrigation water volume according to the water source type corresponding to each water supply node.

[0012] Furthermore, taking the irrigation water demand of each water-using grid unit as the initial water demand state, and based on the water supply correlation, water supply nodes that supply water to each water-using grid unit are selected, and the water-using grid unit is allocated according to a preset tiered supply and demand sequence to obtain the preliminary allocated net irrigation water volume of the water-using grid unit. The specific steps are as follows: taking the irrigation water demand of each water-using grid unit as the initial water demand state, determining the water supply nodes that have a water supply relationship with each water-using grid unit according to the water supply correlation, and matching the supply nodes with demand in sequence according to a preset tiered water supply sequence; For the water supply node of the current tier, when the available irrigation water of the water supply node is less than the irrigation water demand of the water grid unit, the available irrigation water of the water supply node is allocated to the water grid unit, and the difference between the irrigation water demand and the available irrigation water is used as the current water demand gap of the next tier, and the water supply node of the next tier is switched to continue the supply and demand matching. The supply and demand are matched based on the available irrigation water volume of the water supply node and the irrigation water demand of the corresponding multiple water grid units; after allocating water volume to any water grid unit, the allocated water volume is deducted from the available irrigation water volume of the water supply node, and the remaining available irrigation water volume after deduction is used to participate in the subsequent water supply matching of other water grid units. When the available irrigation water at the water supply node is greater than or equal to the irrigation water demand, the water volume required to meet the irrigation water demand is used as the water volume allocated to the current step, and the subsequent step water supply of the water grid unit is terminated. The actual allocated water volume corresponding to each step is summed to obtain the preliminary allocated net irrigation water volume of the water grid unit.

[0013] Furthermore, based on the irrigation water demand and the initially allocated net irrigation water, the water demand satisfaction rate of the water use grid unit is calculated. With the maximization of the water demand satisfaction rate and the maximization of the economic benefits of the irrigation area as the synergistic objectives, a multi-objective optimization function is constructed. The specific steps are as follows: the ratio of the initially allocated net irrigation water to the irrigation water demand is used as the water demand satisfaction rate of the water use grid unit, and an upper limit constraint is imposed on the water demand satisfaction rate. The economic benefits of the irrigation district are determined based on the agricultural output income corresponding to the irrigation water volume of each water grid unit and the corresponding water supply cost. A multi-objective optimization function is constructed with maximizing the water demand satisfaction rate as the first optimization objective, maximizing the economic benefits of the irrigation district as the second optimization objective, and the initial allocation of net irrigation water volume for each water-using grid unit as the initial optimization state.

[0014] Furthermore, the multi-objective optimization function is optimized under the constraints of the total water intake control line and the channel water carrying capacity to obtain the initial water rights of each water-using grid unit. The specific steps are as follows: the total water intake control line constrains the total water allocation of each water-using grid unit, and the channel water carrying capacity constrains the water transmission and distribution volume of the corresponding channel. Under the conditions of satisfying the total water intake control limit and the channel water carrying capacity, the multi-objective optimization function is solved iteratively using a non-dominated sorting genetic algorithm. In each iteration, water allocation schemes that do not meet the constraints are eliminated according to the total water intake control limit and the channel water carrying capacity, and the water allocation schemes that meet the constraints are non-dominated sorted to obtain the Pareto solution set. Select target water allocation schemes that satisfy the collaborative objectives from the Pareto solution set, normalize the water demand satisfaction rate and economic benefits of each target water allocation scheme, calculate the comprehensive evaluation value based on the normalized water demand satisfaction rate and economic benefits, select the target water allocation scheme with the highest comprehensive evaluation value, and determine the water allocation amount corresponding to each water use grid unit in the target water allocation scheme as the initial water rights allocation amount of each water use grid unit.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention, based on acquired remote sensing images and field data, extracts spatial vector data through spatial feature extraction and constructs a directed water network topology. It also acquires agricultural land rights confirmation data and canal water supply coverage areas, using agricultural land rights confirmation data as a hard constraint and canal water supply coverage areas as a soft constraint. A spatial topology algorithm is used to determine water use grid units, and a water supply relationship is established between the directed water network topology and the water use grid units. Compared to existing technologies that rely on static administrative divisions and registered area for segmentation, this invention breaks through the limitations of administrative boundaries, deeply integrating the physical water flow direction with the boundaries of property rights plots. This achieves a precise mapping between spatial vector data and agricultural land rights confirmation data, dividing water use grid units that closely match actual plot boundaries. It overcomes the defect of the macro-administrative boundaries being disconnected from the real physical canal system, providing micro-physical boundary data support for the allocation of water rights indicators. This invention also calculates irrigation water demand by acquiring crop growth and rainfall data of water-using grid units, and calculates available irrigation water based on the effective catchment area of ​​water supply nodes. Using irrigation water demand as the initial water demand state, water supply nodes are selected based on water supply relationships, and water is allocated according to a preset tiered supply and demand order to obtain a preliminary net irrigation water allocation. Furthermore, based on the irrigation water demand and the preliminary net irrigation water allocation, the water demand satisfaction rate is calculated. A multi-objective optimization function is constructed with the maximization of the water demand satisfaction rate and the maximization of the economic benefits of the irrigation area as collaborative objectives. The multi-objective optimization function is optimized using the total water intake control limit and the channel water carrying capacity as constraints to obtain the initial water rights for each water-using grid unit. Compared to existing technologies that rely on fixed quotas for unidirectional and extensive allocation, this invention achieves dynamic perception and hierarchical matching of water demand status. Moreover, by introducing the total water intake control limit and channel water carrying capacity as physical constraints in the optimization process, it avoids the allocation scheme from deviating from the actual water supply capacity, ensuring that the initial water rights meet the transmission and distribution limits and actual needs, thus improving the scientific nature and accuracy of resource allocation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a comparison chart of irrigation water requirements for this application and conventional methods. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Example: Please see Figures 1-2 The present invention provides a technical solution: A method for dividing irrigation district agricultural water use rights into units and dynamically allocating initial water rights in a grid-based manner, comprising the following steps: Step 1: Acquire remote sensing images, field data, total water intake control limits, and channel water carrying capacity of the irrigation area. Extract spatial features from the remote sensing images and field data to obtain spatial vector data of the irrigation area, and construct the directed water network topology of the irrigation area based on the spatial vector data.

[0020] In this embodiment, spatial features are extracted from the remote sensing images and the field data to obtain spatial vector data of the irrigation area, and a directed water network topology of the irrigation area is constructed based on the spatial vector data. The specific steps are as follows: spatial extraction of the watershed rivers and farmland water conservancy projects in the irrigation area is performed using a geographic information system to obtain spatial vector data of the irrigation area.

[0021] Remote sensing images of the irrigation area were acquired via high-resolution satellites, combined with on-site survey data and water withdrawal control limits approved by the water resources management department. This fundamental spatial data collection and extraction was undertaken because traditional agricultural water resource management typically relies on static administrative village boundaries for extensive quota allocation, leading to a significant misalignment between digital management boundaries and the actual boundaries of water flow in physical channels. To overcome this administrative barrier, a geographic information system (GIS) was used to extract entity features from real river basins and agricultural water conservancy projects, such as reservoirs, sluice gates, and pumping stations, generating high-precision spatial vector data.

[0022] The spatial vector data includes at least the spatial vector elements corresponding to farmland irrigation projects, watershed rivers and water conveyance channels; The farmland irrigation project is used as a network node, the watershed river and the water conveyance channel are used as network edges, and the direction of water flow in the irrigation area is determined as the network direction to construct a directed water network topology for the irrigation area.

[0023] To accurately represent the physical water supply patterns of the irrigation district, the extracted farmland water conservancy projects are abstracted as network nodes in a graph database, and the watershed rivers and water conveyance channels are abstracted as network edges connecting these nodes. Since water resource allocation is constrained by topographic gravity and pump head, water flow exhibits a strictly unidirectional or controlled direction. Therefore, based on the actual topographic elevation difference and engineering control rules, the water flow direction is forcibly determined as the directed boundary direction of the network topology, thereby constructing a directed water network topology that conforms to the real physical connectivity patterns.

[0024] In real-world farmland irrigation and distribution scenarios, water resources can only flow unidirectionally from high to low or through pumping stations. Therefore, irrigation facilities and channels are abstracted as nodes and directed edges in graph theory. Constructing a directed water network topology essentially defines strict physical transport paths for subsequent water allocation. By establishing the direction of water flow as the network direction, this topology clearly defines the upstream and downstream water supply dependencies between each irrigation node. When water allocation is performed subsequently, water can only be transported from upstream nodes to downstream grids along directed edges. Any water allocation request that flows in the opposite direction or crosses channels without physical connectivity will be deemed invalid at this underlying topology verification stage. This conversion from pure textual logic to graph data structure not only eliminates complex mathematical verification formulas but also fundamentally ensures that all subsequently generated water rights allocation instructions conform to the objective physical laws of unidirectional water flow in the real world, avoiding invalid solutions that cannot be implemented by the algorithm.

[0025] Step 2: Obtain agricultural land rights confirmation data and canal system water supply coverage area. Using the agricultural land rights confirmation data as a hard constraint and the canal system water supply coverage area as a soft constraint, determine the water use grid units of the irrigation area through a spatial topology algorithm; establish the water supply association relationship between the directed water network topology and the water use grid units.

[0026] In this embodiment, agricultural land rights confirmation data and canal water supply coverage area are obtained. The agricultural land rights confirmation data is used as a hard constraint, and the canal water supply coverage area is used as a soft constraint. The water grid unit of the irrigation area is determined by spatial topology algorithm. The specific steps are: extract the boundaries of the land rights confirmation plots in the agricultural land rights confirmation data, and use the boundaries of the land rights confirmation plots as boundary constraints.

[0027] Extract the boundaries of the land parcels with confirmed land rights from the agricultural land rights confirmation data issued by the agricultural management department, and use these boundaries as boundary constraints. The reason for setting this as a hard constraint is that the agricultural land rights confirmation data represents the absolute legal and physical boundaries of farmers' land contract management rights. The ultimate beneficiaries of water rights allocation are specific farmers. Therefore, the division of the underlying grid must not cross or sever the red line of farmers' property rights.

[0028] Extract the water supply coverage area of ​​each water conveyance channel and use the water supply coverage area of ​​the channel system as a constraint on the water supply coverage range.

[0029] Extract the water supply coverage area corresponding to each water conveyance channel and use it as a constraint on the water supply coverage range. The coverage area of ​​the canal system here is usually determined by objective physical conditions such as the actual elevation of the channel and the gravity radiation radius of the water flow. It is set as a soft constraint because the theoretical water supply range of a single channel often spans multiple different farmers' plots, and the same farmer's plot may also be covered by the dual radiation range of the main channel and branch channels.

[0030] Spatial topological intersection processing is performed on the boundary of the confirmed land parcel and the water supply coverage area of ​​the canal system. Based on the spatial intersection relationship, the water supply coverage area of ​​the canal system corresponding to each confirmed land parcel is determined. The irrigation area is spatially divided using the boundary of the confirmed land parcel as the dividing boundary, resulting in multiple independent and non-overlapping water grid units within the irrigation area.

[0031] Spatial topological intersection processing is performed on the boundaries of the land parcels with the irrigation canal water supply coverage area, and the boundaries of the land parcels with confirmed land rights are used as the dividing boundaries to spatially divide the irrigation area. The calculation formula for this spatial topological intersection and grid generation is as follows: In the formula, For the first Each water grid cell is independent and non-overlapping. For any two-dimensional coordinate point within the irrigation area, For the first The boundaries of each land parcel with confirmed ownership. For the first The water supply coverage area of ​​the water conveyance canal system For the first The effective irrigation area of ​​each water grid unit This represents the effective land use coefficient at this coordinate point. For the index of land parcels with confirmed ownership. For indexing water conveyance channels, This is for indexing water grid cells.

[0032] The water grid cell reflects the absolute spatial boundary of a single water grid cell in the two-dimensional physical world. The effective irrigation area of ​​each water use grid unit reflects the actual geographical area within that grid that is suitable for irrigation. A precise geometric cut and separation was performed between the macro-level water management scope and the micro-level farmland ownership plots. This completely eliminated the overlapping and ambiguous areas between water supply maps and agricultural plot maps in traditional water allocation, ensuring that each generated data grid has both a clear water supply source and a clear ownership, thus providing a micro-level data carrier for subsequent water rights allocation to households.

[0033] Specifically, the range of values ​​for the land effective utilization coefficient is set as follows: When the coordinate point is located in a purely effective crop planting area, its value is 1; when the coordinate point is located in ancillary facilities such as field ridges, farm roads, irrigation ditches or pumping stations where actual agricultural planting and irrigation cannot be carried out, its value is 0; if the point is located in a transitional area with mixed land features, a reduced value between 0 and 1 is taken.

[0034] The hard constraints of property rights and the soft constraints of water supply together determine the grid. Spatial form and location. A point is considered valid and included in the current grid set only when it belongs to a farmer's legally owned plot of land and is within the gravity or water-lifting radiation range of a certain canal. Independent variable A reduction factor for the micro-topography within the plot is introduced because the confirmed land area often includes ancillary facilities that cannot be used for crop cultivation, such as field ridges, farm roads, and drainage ditches. This factor affects the final effective irrigated area. It provides direct physical correction.

[0035] Mesh Space Geometry Set The size of the range is positively correlated with the spatial intersection area of ​​hard constraints and soft constraints; the effective irrigation area is positively correlated with the aforementioned spatial intersection area and with the land effective utilization coefficient. That is, the higher the effective utilization coefficient, the less non-planted area is removed in the algorithm, and the larger the final calculated effective irrigation area.

[0036] In routine agricultural management, township administrative boundaries are often directly misappropriated or crude remote sensing identification tiles are used. This embodiment, by introducing topological intersection logic with hard and soft constraints, completes a reconstruction of the underlying data based on geospatial mapping and atlas theory. Using property boundaries as a hard constraint ensures the indivisibility of property rights; using water supply coverage as a soft constraint establishes the accessibility of physical water flow. The practical significance of this logical operation is that it forcibly anchors the originally two independently operating heterogeneous business data in the underlying code space, eliminating invalid calculation nodes where there are plots of land but no canal coverage, or canal coverage but no confirmed ownership, thus improving the purity of the underlying computational micro-elements and the accuracy of subsequent hydrological calculations.

[0037] In this embodiment, the water supply association between the directed water network topology and the water-using grid unit is established. The specific steps are as follows: spatial intersection judgment is performed between the water-using grid unit and the water conveyance channel in the directed water network topology, and the water conveyance channel that has a spatial intersection relationship with the water-using grid unit is determined as the associated water conveyance channel of the water-using grid unit.

[0038] Identify water conveyance channels whose geometric boundaries overlap, intersect, or are tangentially connected to the current water grid cell, and strictly determine them as associated water conveyance channels of that grid cell.

[0039] Using the associated water conveyance channel as the starting point of topology tracing, the upstream network nodes and water conveyance channels are traced in the opposite direction of the water flow in the directed water network topology to determine the water supply nodes and corresponding directed water supply paths that supply water to the water-using grid unit, thereby establishing the water supply association relationship between the water-using grid unit and the water supply nodes, water conveyance channels and directed water supply paths.

[0040] Once the terminal outlet is located, knowing only that segment of the channel is insufficient for comprehensive water resource management and water rights deduction across the entire basin. Therefore, it is necessary to use this associated water conveyance channel as the starting point for topological tracing, triggering a reverse pathfinding mechanism within the underlying directed water network topology. Because real-world farmland water networks are driven by topographic gravity and pumping station thrust, water flow is strictly unidirectional. If downstream tracing is used, the path will diverge in a fan shape, making it impossible to determine which water source is being consumed by a particular plot. However, tracing upstream along the reverse direction of the directed water network topology allows for precise convergence and locking onto the upstream water supply node specific to that grid, such as a main canal, reservoir, or pond, thereby extracting the complete directed water supply path. Through this purely geometric topological tracing process, the micro-level water-using grid units are forcibly bound to the macro-level water supply nodes and conveyance channels, establishing a clear water supply relationship.

[0041] In traditional water resource allocation and management, water rights accounting logic typically adopts a top-down, downstream allocation mechanism. This assumes that after upstream reservoirs or main canals release water, the amount is directly and roughly allocated to downstream villages and towns according to the area under their jurisdiction. The fatal flaw of this forward extrapolation is that it fails to perceive the actual physical connectivity between the fields at the bottom, making it extremely easy to incorrectly allocate water rights to farmland that has not been equipped with terminal branch canals, is too high in elevation to allow water to reach, or has been disconnected due to years of disrepair.

[0042] The system relies on the reverse graph traversal mechanism in graph theory to achieve physical water supply traceability. Starting from the micro-grid of farmland at the end of the chain of ownership and tracing upstream, the water supply relationship is established only when the water network is physically connected and fully meets the requirements of gravity flow. This avoids ineffective allocation due to insufficient connectivity and removes underlying data obstacles for subsequent truly accurate tiered supply and demand matching.

[0043] Step 3: Obtain crop growth data and rainfall data for the water use grid units. Calculate the irrigation water demand for each water use grid unit based on the crop growth data and rainfall data. Obtain the effective catchment area of ​​each water supply node. Calculate the available irrigation water volume for each water source based on the effective catchment area. Using the irrigation water demand of each water use grid unit as the initial water demand state, select water supply nodes that supply water to each water use grid unit based on the water supply relationship. Allocate water supply and demand for the water use grid units according to a preset tiered supply and demand sequence to obtain the preliminary allocated net irrigation water volume for the water use grid units.

[0044] In this embodiment, crop growth data and rainfall data of the water use grid unit are obtained, the irrigation water demand of the water use grid unit is calculated based on the crop growth data and the rainfall data, the effective catchment area of ​​each water supply node is obtained, and the available irrigation water of each water source is calculated based on the effective catchment area. The specific steps are as follows: determine the growth stage of the crop in the water use grid unit according to the crop growth data, determine the stage water demand according to the growth stage, and calculate the crop water demand based on the stage water demand and the preset water demand allocation coefficient. The effective rainfall amount of the water grid unit is determined based on the rainfall data; The irrigation water requirement of the water grid unit is determined based on the difference between the crop water requirement and the effective rainfall.

[0045] First, the growth stage of crops within the water-use grid unit is determined based on agricultural conditions. Since crops at different stages, such as the tillering and jointing stages of rice, have varying water sensitivities, a basic stage-specific water requirement is determined. This is then weighted using a pre-set water requirement allocation coefficient to calculate the crop water requirement for a specific period. This pre-set coefficient is not arbitrarily chosen but rather derived from historical crop lysimeter measurements conducted by local agricultural research departments over the years, as well as industry-specific "Farmland Irrigation Experiment Specifications." It accurately reflects the objective laws governing the physiological ratio of evaporation and transpiration for specific soil types and crop varieties at different developmental stages. Simultaneously, the effective rainfall received by the water-use grid unit is extracted based on local meteorological and rainfall data. Then, the crop water requirement is directly subtracted from the naturally replenished effective rainfall; the difference is the absolute irrigation water requirement for that grid unit. This approach abandons the traditional static model based on fixed quotas and restores the true interplay between agricultural growth and meteorological conditions.

[0046] Calculate the water catchment volume of each water supply node based on the effective catchment area corresponding to each water supply node and the rainfall during the preset time period. Obtain the storage coefficient corresponding to each water supply node, correct the water collection volume according to the storage coefficient, obtain the available water volume of the water source corresponding to each water supply node, and determine the corresponding available irrigation water volume according to the water source type corresponding to each water supply node.

[0047] After clarifying the demand-side indicators, the supply-side indicators are simultaneously dynamically quantified. For different water sources within the irrigation district, their water production capacity cannot be represented by static reservoir capacity, but is driven by both catchment topography and engineering characteristics. The formula for calculating the available irrigation water volume is: In the formula, For the amount of water available for irrigation, The effective catchment area of ​​each water supply node, The rainfall amount for the preset time period. The storage coefficient corresponding to each water supply node. The available water extraction coefficient determined for the type of water source.

[0048] The preset time period is derived from the legally mandated rotational irrigation cycle or the specific critical growth period of crops in the irrigation district's water management operations, such as by ten-day period, by month, or by a specific jointing period. The rainfall value is directly extracted from the meteorological department's actual measurement records or the cumulative amount of short-term numerical forecasts during this period. The storage coefficient is derived from the historical statistical fitting of the water management department's records of the routine operation and water level fluctuations of various micro-storage facilities over many years. The available water extraction coefficient is derived from the rigid calibration of the ecological base flow warning line and dead storage capacity ratio limit that must be retained for different levels of water sources in the water planning standards.

[0049] The range of values ​​for the available water extraction coefficient, determined by the water source type, is typically set as follows: Specifically, differentiated classification and calibration are carried out based on the regulation performance, dead storage capacity limit, and ecological base flow constraint of various water conservancy projects: The basic water source value is 0.8~1.0. These facilities are specifically built for field irrigation and do not have strict macro-level river ecological base flow assessment pressure. In the event of drought, they can be drained to the maximum extent or even completely to ensure the survival of the end-field farmland. For small-scale water storage projects, the value is taken as 0.6~0.8. This level of water source needs to retain a minimum dead storage capacity to maintain the safety of the dam body, and also take into account the basic water use of surrounding villages and towns. Therefore, the actual amount of agricultural water available for dispatch needs to be reduced to a certain extent by physical means. The value for the main water source is 0.4 to 0.6. As the main water source for irrigation areas or even cities, the reserved reservoir capacity above the flood control limit level, the ecological base flow red line of the downstream river section, and the higher priority urban and rural domestic and industrial water quotas must be deducted unconditionally. The extractable proportion for pure agricultural irrigation is subject to the strictest restrictions.

[0050] Available irrigation water reflects the net amount of water that can actually be allocated for agricultural irrigation at a specific water supply node within a preset rainfall cycle. Its core meaning is to break through the limitations of recognizing water supply potential based solely on static water level gauge readings, quantifying and integrating natural rainfall replenishment with the circulating water storage and release capacity of engineering facilities. It assesses the true drought resistance and allocation potential of water sources at all levels, preventing blind cross-regional water diversion due to underestimating the dynamic replenishment capacity of small-scale water sources, and providing extremely accurate supply-side data upper limits for subsequent tiered water supply matching.

[0051] The effective catchment area of ​​each water supply node is physically multiplied by the rainfall during the preset time period to form the natural absolute catchment volume of that water supply node. In farmland water conservancy, ponds and weirs have the characteristics of storing water, receiving rain, and releasing water simultaneously during the irrigation season. The multiple storage coefficient is a mathematical reflection of this characteristic of filling and emptying multiple times a year. It corrects the static catchment volume by a factor to restore the true dynamic water production potential. This takes into account the differences in scheduling constraints for different types of water sources. For example, backbone reservoirs must retain sufficient ecological base flow, while some end ponds can be completely emptied. The amount of available irrigation water is legally extracted and reduced according to the type of water source.

[0052] The available irrigation water volume is strictly positively correlated with the effective catchment area of ​​the water supply node, the rainfall during the preset time period, and the storage coefficient. That is, the larger the catchment area, the more abundant the rainfall during the period, and the stronger the storage and circulation capacity of the facility, the greater the amount of water available for dispatch in the final calculation.

[0053] Traditional water quantity assessment mechanisms often assume that the current reservoir level corresponds to a certain storage capacity, thus determining the maximum water supply. This ignores the hydrological cycle and the strong dynamic resilience of small and medium-sized water conservancy facilities during the rainy season. By introducing a coupled calculation mechanism of the storage coefficient and effective catchment area, a dynamic replenishment system is established for the water supply end at the hydrological calculation level. This allows the underlying algorithm to accurately capture the water supply potential of micro-water sources that can quickly recover after a localized heavy rainfall. This mechanism, based on hydrological dynamic quantification, activates the efficiency of local allocation of basic water sources within the irrigation area. Before inter-regional water transfer through the main canal is used, local water resources can be consumed first. This not only avoids excessive consumption of the reserves of key reservoirs but also significantly reduces evaporation and seepage losses along the way during long-distance water transport from a macro-level scheduling perspective.

[0054] In this embodiment, the irrigation water demand of each water-using grid unit is taken as the initial water demand state. Based on the water supply correlation, water supply nodes that supply water to each water-using grid unit are selected. The water supply and demand of the water-using grid unit are allocated according to a preset tiered supply and demand sequence to obtain the preliminary allocated net irrigation water volume of the water-using grid unit. The specific steps are as follows: taking the irrigation water demand of each water-using grid unit as the initial water demand state, determining the water supply nodes that have a water supply relationship with each water-using grid unit according to the water supply correlation, and matching the supply nodes with demand in sequence according to the preset tiered supply order. For multiple water supply nodes and associated water-using grid cells within the same tier, a preset tiered water supply sequence is constructed. Specifically, based on the established water supply relationships, the topological length of the directed water supply path between the water supply node and each water-using grid cell is calculated first. The grid cell with the shortest path and the lowest physical loss along the way receives the highest matching priority, thereby achieving truly low-loss transmission and distribution on the physical entity surface.

[0055] To accurately reflect the refined scheduling patterns of irrigation districts during periods of water scarcity or alternating periods of abundance and scarcity, a five-tiered supply-demand balance model was constructed. This model strictly follows the tiered allocation logic of "basic water sources first, then main water sources, from near to far, and from micro to macro," dividing the supply side into five consecutive water supply matching levels from bottom to top: First tier: Matching water supply to mountain ponds Based on the established water supply relationships, micro-pond nodes directly related to each water-using grid unit are prioritized. These ponds serve as the most dispersed and peripheral water storage bodies in the fields. Using the irrigation water demand of each water-using grid unit as the initial demand state, the available irrigation water from the ponds is used to prioritize meeting this demand. When the available irrigation water from the pond is less than the irrigation water demand of the water-using grid unit, all remaining available water from the pond is allocated to that grid, and the difference is calculated and locked as the current water demand gap. The system then automatically switches to the second tier to continue supply and demand matching. Second tier: Matching water supply to the river dam After inheriting the current water demand gap generated by the previous step, the system moves to the second step, matching water supply nodes such as weirs and diversion dams. These dams possess dynamic water diversion and instantaneous water flow capabilities, supplementing areas with insufficient mountain pond water systems. After allocating water to any water-using grid unit, the allocated water is deducted in real-time from the available irrigation water of the dam, and the remaining available water is used to match subsequent water supply to other related water-using grid units. If the dam's water volume is less than the current water demand gap, the remaining gap is recalculated and passed down; if the water volume is sufficient, the water volume required to meet the water demand gap is used as the allocated water volume for the current step, and subsequent step water supply for that grid is terminated. Third tier: Water supply matching of small (II) type reservoirs If, after matching in the first two tiers, there are still unmet water demand gaps in the water grid units, then the third tier is initiated, allocating water supply to small (II) type reservoir nodes within the region. Small (II) type reservoirs, as important water storage and regulation projects at the village / group level, have larger storage capacity and more stable seasonal regulation capabilities than mountain ponds and river dams. In this tier, the aforementioned dynamic deduction and gap transmission mechanism is repeated to prioritize water demand for medium-sized plots during key agricultural seasons. Fourth tier: Water supply matching of small (Class I) reservoirs When the water storage potential of regional micro and small water sources is exhausted and still cannot meet the water demand gap, the system automatically switches to the fourth tier, connecting to the township or district-level small (Class I) reservoir water supply nodes. Small (Class I) reservoirs, as secondary backbone water sources within the irrigation area, bear the heavy responsibility of water transmission and distribution over a large area. Based on the directed water network topology, the available irrigation water volume of small (Class I) reservoirs is deducted and precisely distributed at each level, achieving coordinated water allocation across villages and districts. Fifth tier: Matching the main water source reservoir As the highest level of the five-tiered irrigation system, the fifth tier introduces the main water source reservoirs of the irrigation district, such as large and medium-sized backbone reservoirs or inter-basin water transfer projects. Although the water conveyance distance is long and the losses along the way are relatively large, its drought-resistant storage capacity is enormous and its water supply guarantee rate is high, ensuring food security in the irrigation district. In this tier, the remaining water demand gap of each grid is finally matched and dynamically deducted until the water demand gap of each grid is reduced to zero, or the potential of all water supply nodes in all tiers is exhausted.

[0056] For the water supply node of the current tier, when the available irrigation water of the water supply node is less than the irrigation water demand of the water grid unit, the available irrigation water of the water supply node is allocated to the water grid unit, and the difference between the irrigation water demand and the available irrigation water is used as the current water demand gap of the next tier. The water supply node of the next tier is then switched to continue the supply and demand matching.

[0057] In the current tiered matching loop, when the remaining available irrigation water of a water supply node is found to be less than the current irrigation water demand of a related water-using grid unit, the full-amount execution logic is triggered. Specifically, all the remaining available irrigation water of the water supply node is assigned and allocated to the water-using grid unit at once. The difference between the original irrigation water demand of the water-using grid unit and the newly allocated water is automatically calculated, marked, and locked in real time as the current water demand gap. After completing the allocation and gap update for this grid level, the system automatically switches to the next higher-level tiered water supply node, such as switching from a pond to a river dam, and uses this water demand gap as an input parameter to continue supply and demand matching in the next tier.

[0058] The supply and demand are matched based on the available irrigation water volume of the water supply node and the irrigation water demand of the corresponding multiple water grid units; after allocating water volume to any water grid unit, the allocated water volume is deducted from the available irrigation water volume of the water supply node, and the remaining available irrigation water volume after deduction is used to participate in the subsequent water supply matching of other water grid units.

[0059] When faced with a complex many-to-one scenario where a single water supply node simultaneously supplies water to multiple water-using grid units, the process iterates through all associated water-using grid units. After successfully allocating water to any of the currently visited water-using grid units, the allocated water is deducted in real-time from the node's total available irrigation water. The remaining dynamic available irrigation water after deduction is then used to match water supply to the next water-using grid unit. This deduction process continues until the water supply node's water supply is completely exhausted.

[0060] When the available irrigation water volume of the water supply node is greater than or equal to the irrigation water demand, the water volume required to meet the irrigation water demand is used as the water volume allocated to the current step, and the subsequent step water supply of the water grid unit is terminated.

[0061] When the available irrigation water quantity at the current water supply node is found to be sufficient, and its value is greater than or equal to the irrigation water demand of the current water-using grid unit, full supply is provided. The precise amount of water required to meet this irrigation demand is directly allocated to the grid unit as the current tier's allocated water quantity. Since the grid unit's gap has been completely closed in the current tier, a termination command is triggered, completely disconnecting the water supply retrieval path for this water-using grid unit in subsequent higher tiers, thus avoiding duplicate calculations and excessive allocation of water resources.

[0062] The actual allocated water volume corresponding to each step is summed to obtain the preliminary allocated net irrigation water volume of the water grid unit.

[0063] After completing the iterative optimization and cyclic deduction of the above five-level ladder, a set of data combinations is established for the actual water volume obtained by each water-using grid unit in each ladder level. The actual allocated water volume obtained by the water-using grid unit in the first, second, third, fourth, and fifth ladder levels is then summed and aggregated.

[0064] Table 1: Water Allocation Data for Different Typical Water Grid Units As shown in Table 1, the method of this application exhibits the following significant beneficial effects in the dynamic allocation of water rights: It avoids ineffective allocation and water waste. For grids D-06 (without canal coverage) and C-04 (abandoned and uncovered), this application uses bottom-level directed water network topology verification to determine that there are no physically connected paths, precisely truncating their total allocation to 0. This completely eliminates the water rights waste caused by the traditional extensive allocation based on administrative boundaries, ensuring the objective implementation of allocation instructions. It activates local micro-water sources and implements tiered, efficient coordination, strictly adhering to the five-level tiered configuration logic of "basic water sources first, then main water sources, and from near to far." For grid D-05, which only covers micro-water sources, its 32,000... Water demand is 100% absorbed by nearby reservoirs; for grid D-04, which has extremely high water demand, water is dynamically deducted from reservoirs, river dams, small Class II and small Class I reservoirs in sequence, ultimately only 20,000 cubic meters per second are drawn from the main water source. Further, this method maximizes the dynamic water storage potential of small and medium-sized water sources, significantly reducing losses along the route for long-distance water transfer across regions. It achieves high-precision water demand satisfaction: for effective grids with well-connected water network topologies, under the joint scheduling of multi-level water sources, the final total allocation matches the irrigation water demand of each grid. This demonstrates that the algorithm can achieve 100% accurate balance between local and even global water rights supply and demand under complex crop demands and physical network constraints. Traditional water rights allocation software often uses a single linear solution or static amortization for the entire irrigation area, violating the real-world scheduling procedures of hierarchical management, step-by-step allocation, and priority allocation based on proximity in water conservancy projects. By introducing a five-level tiered supply and demand balance model and a dynamic deduction algorithm, farmers can enjoy low-cost local water when water resources are abundant, and supplementary water can be drawn from backbone reservoirs through automatic tiered switching. This hierarchical iteration and dynamic deduction calculation mechanism not only eliminates the disorderly waste of long-distance backbone water sources, but also ensures that the initial allocation results of each grid unit fully meet the scheduling limits of real water conservancy projects.

[0065] Step 4: Calculate the water demand satisfaction rate of the water use grid unit based on the irrigation water demand and the preliminary allocated net irrigation water volume. With the goal of maximizing the water demand satisfaction rate and maximizing the economic benefits of the irrigation area, construct a multi-objective optimization function. With the total water intake control limit and the channel water carrying capacity as constraints, optimize the multi-objective optimization function to obtain the initial water rights of each water use grid unit.

[0066] In this embodiment, the water demand satisfaction rate of the water use grid unit is calculated based on the irrigation water demand and the initial allocated net irrigation water. With the maximization of the water demand satisfaction rate and the maximization of the economic benefits of the irrigation area as the synergistic objectives, a multi-objective optimization function is constructed. The specific steps are as follows: the ratio of the initial allocated net irrigation water to the irrigation water demand is used as the water demand satisfaction rate of the water use grid unit, and an upper limit constraint is imposed on the water demand satisfaction rate.

[0067] The initial net irrigation water allocation is compared with the irrigation water demand of the grid to obtain the water demand satisfaction rate, and an upper limit constraint is imposed on the water demand satisfaction rate, that is, its maximum value is set to 1. When the allocated water volume reaches or exceeds the actual water demand of the crop, the satisfaction rate reaches 100%, and the excess allocated water will not lead to a further increase in the satisfaction rate, thereby preventing the physical waste of water resources.

[0068] The economic benefits of the irrigation district are determined based on the agricultural output income corresponding to the irrigation water volume of each water grid unit and the corresponding water supply cost. A multi-objective optimization function is constructed with maximizing the water demand satisfaction rate as the first optimization objective, maximizing the economic benefits of the irrigation district as the second optimization objective, and the initial allocation of net irrigation water volume for each water-using grid unit as the initial optimization state.

[0069] The expression for the multi-objective optimization function is: In the formula, To ensure the water demand satisfaction rate of the irrigation area, For the economic benefits of the irrigation district, For the first The dynamic allocation of water volume among individual water-using grid cells during the algorithm's iterative optimization process. This represents the total number of water use grid units within the irrigation district. For the first Irrigation water demand of each water grid unit The standard maximum output yield coefficient for crops per unit area. For the price of crops, This refers to the comprehensive water supply cost per unit volume of water generated by the different spatial locations and water transport paths of the grid unit.

[0070] In traditional water resource management, solely pursuing economic benefits leads to high-value-added cash crops consuming all water resources, causing widespread droughts in food crops; while solely pursuing water satisfaction rates results in inefficient water resource allocation and low economic returns. By using a multi-objective collaborative function, the two objectives are mathematically coupled. The agricultural output term in the economic benefit function is directly linked to the water satisfaction rate. This means that if the water volume in a certain grid is excessively reduced, leading to a decrease in the satisfaction rate, its agricultural output will also decrease non-linearly.

[0071] Water supply costs are directly related to the length of the directed water supply path and the hierarchy of water supply nodes. Grids far from the main water source have high water supply costs, and the algorithm automatically weighs this during collaborative optimization: allocating more water to these high-cost grids, while improving their satisfaction rate, will significantly increase the total water supply cost. This spatial cost feedback mechanism forces the algorithm to search for a Pareto optimal solution globally, thus achieving an organic synergy between fairness and efficiency.

[0072] Water demand satisfaction rate and economic benefits respectively reflect the macro-level social equity and economic profitability of the entire irrigation district under a specific water rights allocation scheme. Its core meaning is to transform complex macro-level water conservancy policies into computer-executable mathematical vectors. Its technical effect lies in the ability to intuitively assess the overall benefit boundary of the irrigation district under different water resource policies by adjusting the weights of the objective function.

[0073] The dynamic allocation of water to each water-using grid unit serves as the decision variable for the optimization algorithm, directly determining the size of the numerator. Increased water volume leads to a higher local satisfaction rate, which in turn increases the overall average satisfaction rate. Simultaneously, moderate water input can increase the revenue component in agricultural output, but when water volume exceeds water demand, marginal benefits diminish and water supply costs increase linearly, resulting in a slowdown or even a decline in the growth rate of economic benefits.

[0074] The water demand satisfaction rate is strictly positively correlated with the dynamic water allocation of each grid before saturation; the total economic benefit has a non-linear parabolic correlation with the allocated water volume, that is, the benefit increases with the increase of water volume in the early stage, but the benefit decreases in the opposite direction with the increase of water volume after the water demand threshold of crops is exceeded or the water supply cost is too high.

[0075] By introducing a multi-objective collaborative optimization function, not only can specific water allocation schemes be output, but the optimal solution for economic benefits and resource allocation fairness can also be achieved under multiple constraints. This ensures that the final output of initial water rights safeguards the bottom line of food security while achieving a reasonable allocation of limited water resources to high-efficiency plots, fully empowering the refined water rights allocation of modern irrigation districts.

[0076] In this embodiment, the multi-objective optimization function is optimized using the total water intake control line and the channel water carrying capacity as constraints to obtain the initial water rights of each water-using grid unit. The specific steps are as follows: the total water intake control line constrains the total water allocation of each water-using grid unit, and the channel water carrying capacity constrains the water transmission and distribution volume of the corresponding channel.

[0077] First, the annual water allocation limit for the entire irrigation area or each level of water distribution section is strictly limited by the total water intake control red line; at the same time, the water carrying capacity of each water conveyance channel is used as the maximum flow boundary of each level of network to prevent local allocation schemes from exceeding the safe water conveyance limit of the channel.

[0078] Under the conditions of satisfying the total water intake control limit and the channel water carrying capacity, the multi-objective optimization function is solved iteratively using a non-dominated sorting genetic algorithm. In each iteration, water allocation schemes that do not meet the constraints are eliminated according to the total water intake control limit and the channel water carrying capacity, and the water allocation schemes that meet the constraints are non-dominated sorted to obtain the Pareto solution set.

[0079] Under the condition of satisfying the above boundary constraints, a non-dominated sorting genetic algorithm is used to iteratively solve the multi-objective optimization function. In each iteration of the algorithm, all water allocation schemes that exceed the water intake control limit or exceed the channel's water capacity are eliminated; for valid schemes that pass physical verification, non-dominated sorting is performed, and finally, the algorithm converges in the multi-dimensional solution space and outputs a Pareto solution set that takes into account both fairness and efficiency.

[0080] When using a non-dominated sorting genetic algorithm to iteratively solve a multi-objective optimization function, the parameter settings and physical control logic in the water management scenario are as follows: The population size is set to a range of 100-500. Each individual represents a complete gridded water rights allocation scheme for the entire irrigation district. Setting this size ensures that there are sufficiently diverse water allocation combinations in the initial solution space, avoiding the solution from getting trapped in local optima due to insufficient samples, and strictly controlling the memory overhead of matrix operations. The maximum number of iterations is set to a range of 200-1000. The maximum number of iterations determines the depth of the supply and demand game theory. The setting of the number of iterations is directly linked to the real-time requirements of water management: for regular water rights allocation on a ten-day or monthly basis, a higher value can be used to pursue the ultimate Pareto front; for emergency water allocation under sudden drought conditions, a lower value can be used to shorten the computational cost and quickly output a physically safe feasible solution.

[0081] The crossover probability is set to a range of 0.7 to 0.9. A higher crossover probability means that the algorithm can frequently swap and recombine the best segments from two excellent allocation schemes during optimization. Logically, this is equivalent to integrating the local experience of multiple excellent schedulers, thereby accelerating the convergence of the overall scheme towards the ideal state with both high satisfaction rate and economic benefits.

[0082] The mutation probability is set to a range of 0.01 to 0.1. Keeping the mutation probability at a low level is to prevent the system from degenerating into a disordered random search in the later stages of optimization, thereby destroying the established reasonable water allocation architecture. At the same time, the extremely low probability of mutation can give the system the ability to correct itself by escaping the trap of local optima and explore a more cost-effective global water rights scheduling strategy.

[0083] Select target water allocation schemes that satisfy the collaborative objectives from the Pareto solution set, normalize the water demand satisfaction rate and economic benefits of each target water allocation scheme, calculate the comprehensive evaluation value based on the normalized water demand satisfaction rate and economic benefits, select the target water allocation scheme with the highest comprehensive evaluation value, and determine the water allocation amount corresponding to each water use grid unit in the target water allocation scheme as the initial water rights allocation amount of each water use grid unit.

[0084] The formula for calculating the comprehensive evaluation value is: In the formula, To ensure the water demand satisfaction rate of the irrigation district, For the economic benefits of the irrigation district, and These are the solutions in the Pareto set. The minimum and maximum values ​​of an optimization objective in the set of all solutions. The normalized dimensionless evaluation value of the target. This is a comprehensive evaluation value; and These are preset weighting coefficients for water demand satisfaction rate and economic benefits, respectively. These preset weighting coefficients are derived from strategic manual calibrations conducted by the irrigation district management department based on the macroeconomic policy guidance for the current season, and satisfy... .

[0085] In actual water management projects, the specific range of pre-set weighting coefficients for water demand satisfaction rate and economic benefits is as follows: Drought-stricken food conservation scenarios: Weight values ​​are typically set as follows , When encountering regional droughts or during the critical growth period of food crops, management departments significantly increase the weight of water demand satisfaction rate, forcing the algorithm to sacrifice some economic benefits when seeking optimization, prioritizing the basic survival water needs of large areas of farmland in the entire irrigation area, and preventing large-scale crop failure.

[0086] Water-enhancing efficiency scenarios: Weight values ​​are typically set as follows , In years with abundant water resources or under the policy guidance of encouraging the development of high-value-added cash crops, management departments increase the weight of economic benefits and guide the system to accurately allocate surplus water rights quotas to high-yield plots, thereby maximizing the economic benefits of water resources.

[0087] Balanced scheduling scenario: Weight values ​​are set to , The system does not impose any obvious biased human intervention, but completely relies on the multi-objective optimization algorithm to spontaneously find the absolute compromise between satisfaction rate and output return within the solution space.

[0088] The comprehensive evaluation value specifically reflects the overall merits of a particular water rights allocation scheme in the Pareto solution set when considering both water demand satisfaction rate and economic efficiency. Its core meaning is to compress and map multi-dimensional conflict objectives into a single, rankable optimal score through normalization and weighted summation.

[0089] and These represent the standardized satisfaction rate and economic benefit scores after range normalization. Normalization is necessary because the original satisfaction rate and economic benefit belong to completely different dimensions and orders of magnitude, and cannot be directly added; range transformation maps them uniformly to a normalized value. Within the interval, the distortion effect of dimensional differences on weighting decisions was eliminated. Weighting coefficients. and As a preference moderating factor, it directly determines the degree to which the overall score leans towards grain preservation or efficiency improvement.

[0090] In traditional water rights allocation practices, when faced with multi-objective conflicts, qualitative decisions are often made based on the subjective experience of managers, lacking rigorous mathematical optimization and quantitative selection support. This embodiment introduces a non-dominated sorting genetic algorithm combined with normalized comprehensive evaluation. This reduces the engineering risks of exceeding water quotas and causing canal damage. Furthermore, through the game evolution of Pareto solution sets, the final output of the initial water rights allocation for each water-using grid unit not only conforms to objective engineering physical limits but also aligns with the macro-control of the irrigation district in the current season. This comprehensively realizes a technological leap from extensive, static quota allocation to multi-dimensional constraint dynamic optimization of water rights.

[0091] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0092] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0094] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for dividing agricultural water use units in irrigation districts and dynamically allocating initial water rights in a grid-based manner, characterized in that, include: Step 1: Acquire remote sensing images, field data, total water intake control limits, and channel water carrying capacity of the irrigation area; extract spatial features from the remote sensing images and field data to obtain spatial vector data of the irrigation area; and construct the directed water network topology of the irrigation area based on the spatial vector data. Step 2: Obtain agricultural land rights confirmation data and canal system water supply coverage area. Using the agricultural land rights confirmation data as a hard constraint and the canal system water supply coverage area as a soft constraint, determine the water use grid units of the irrigation area through a spatial topology algorithm; establish the water supply association relationship between the directed water network topology and the water use grid units. Step 3: Obtain crop growth data and rainfall data for the water use grid units. Calculate the irrigation water demand for each water use grid unit based on the crop growth data and rainfall data. Obtain the effective catchment area of ​​each water supply node. Calculate the available irrigation water volume for each water source based on the effective catchment area. Using the irrigation water demand of each water use grid unit as the initial water demand state, select water supply nodes that supply water to each water use grid unit based on the water supply relationship. Allocate water supply and demand for the water use grid units according to a preset tiered supply and demand sequence to obtain the preliminary allocated net irrigation water volume for the water use grid units. Step 4: Calculate the water demand satisfaction rate of the water use grid unit based on the irrigation water demand and the preliminary allocated net irrigation water volume. With the goal of maximizing the water demand satisfaction rate and maximizing the economic benefits of the irrigation area, construct a multi-objective optimization function. With the total water intake control limit and the channel water carrying capacity as constraints, optimize the multi-objective optimization function to obtain the initial water rights of each water use grid unit.

2. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 1, characterized in that: Spatial features are extracted from the remote sensing images and the field data to obtain spatial vector data of the irrigation area, and a directed water network topology of the irrigation area is constructed based on the spatial vector data. The specific steps are as follows: spatial extraction of the watershed rivers and farmland water conservancy projects in the irrigation area is performed using a geographic information system to obtain spatial vector data of the irrigation area. The spatial vector data includes at least the spatial vector elements corresponding to farmland irrigation projects, watershed rivers and water conveyance channels; The farmland irrigation project is used as a network node, the watershed river and the water conveyance channel are used as network edges, and the direction of water flow in the irrigation area is determined as the network direction to construct a directed water network topology for the irrigation area.

3. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 2, characterized in that: To obtain agricultural land rights confirmation data and canal system water supply coverage area, the agricultural land rights confirmation data is used as a hard constraint and the canal system water supply coverage area is used as a soft constraint. The water use grid unit of the irrigation area is determined by spatial topology algorithm. The specific steps are: extract the boundaries of the land rights confirmation plots in the agricultural land rights confirmation data and use the boundaries of the land rights confirmation plots as boundary constraints. Extract the water supply coverage area of ​​each water conveyance channel and use the water supply coverage area of ​​the channel system as a constraint on the water supply coverage range; Spatial topological intersection processing is performed on the boundary of the confirmed land parcel and the water supply coverage area of ​​the canal system. Based on the spatial intersection relationship, the water supply coverage area of ​​the canal system corresponding to each confirmed land parcel is determined. The irrigation area is spatially divided using the boundary of the confirmed land parcel as the dividing boundary, resulting in multiple independent and non-overlapping water grid units within the irrigation area.

4. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 3, characterized in that: The water supply association between the directed water network topology and the water-using grid unit is established by the following steps: spatial intersection determination of the water-using grid unit and the water conveyance channel in the directed water network topology, and water conveyance channel that has a spatial intersection relationship with the water-using grid unit is determined as the associated water conveyance channel of the water-using grid unit. Using the associated water conveyance channel as the starting point of topology tracing, the upstream network nodes and water conveyance channels are traced in the opposite direction of the water flow in the directed water network topology to determine the water supply nodes and corresponding directed water supply paths that supply water to the water-using grid unit, thereby establishing the water supply association relationship between the water-using grid unit and the water supply nodes, water conveyance channels and directed water supply paths.

5. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 1, characterized in that: The process involves acquiring crop growth data and rainfall data for the water-use grid unit, calculating the irrigation water demand of the water-use grid unit based on the crop growth data and the rainfall data, acquiring the effective catchment area of ​​each water supply node, and calculating the available irrigation water volume of each water source based on the effective catchment area. The specific steps are as follows: determining the growth stage of the crop within the water-use grid unit based on the crop growth data, determining the stage water demand based on the growth stage, and calculating the crop water demand based on the stage water demand and a preset water demand allocation coefficient. The effective rainfall amount of the water grid unit is determined based on the rainfall data; The irrigation water requirement of the water-using grid unit is determined based on the difference between the crop water requirement and the effective rainfall. Calculate the water catchment volume of each water supply node based on the effective catchment area corresponding to each water supply node and the rainfall during the preset time period. Obtain the storage coefficient corresponding to each water supply node, correct the water collection volume according to the storage coefficient, obtain the available water volume of the water source corresponding to each water supply node, and determine the corresponding available irrigation water volume according to the water source type corresponding to each water supply node.

6. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 1, characterized in that: Using the irrigation water demand of each water-using grid unit as the initial water demand state, and based on the water supply correlation, water supply nodes that supply water to each water-using grid unit are selected, and the water supply and demand of the water-using grid unit are allocated according to a preset tiered supply and demand sequence to obtain the preliminary allocated net irrigation water volume of the water-using grid unit. The specific steps are as follows: using the irrigation water demand of each water-using grid unit as the initial water demand state, determining the water supply nodes that have a water supply relationship with each water-using grid unit according to the water supply correlation, and matching the supply nodes with demand in sequence according to a preset tiered supply sequence. For the water supply node of the current tier, when the available irrigation water of the water supply node is less than the irrigation water demand of the water grid unit, the available irrigation water of the water supply node is allocated to the water grid unit, and the difference between the irrigation water demand and the available irrigation water is used as the current water demand gap of the next tier, and the water supply node of the next tier is switched to continue the supply and demand matching. The supply and demand are matched based on the available irrigation water volume of the water supply node and the irrigation water demand of the corresponding multiple water grid units; after allocating water volume to any water grid unit, the allocated water volume is deducted from the available irrigation water volume of the water supply node, and the remaining available irrigation water volume after deduction is used to participate in the subsequent water supply matching of other water grid units. When the available irrigation water at the water supply node is greater than or equal to the irrigation water demand, the water volume required to meet the irrigation water demand is used as the water volume allocated to the current step, and the subsequent step water supply of the water grid unit is terminated. The actual allocated water volume corresponding to each step is summed to obtain the preliminary allocated net irrigation water volume of the water grid unit.

7. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 1, characterized in that: Based on the irrigation water demand and the initial allocated net irrigation water, the water demand satisfaction rate of the water use grid unit is calculated. With the maximization of the water demand satisfaction rate and the maximization of the economic benefits of the irrigation area as the synergistic objectives, a multi-objective optimization function is constructed. The specific steps are as follows: the ratio of the initial allocated net irrigation water to the irrigation water demand is used as the water demand satisfaction rate of the water use grid unit, and an upper limit constraint is imposed on the water demand satisfaction rate. The economic benefits of the irrigation district are determined based on the agricultural output income corresponding to the irrigation water volume of each water grid unit and the corresponding water supply cost. A multi-objective optimization function is constructed with maximizing the water demand satisfaction rate as the first optimization objective, maximizing the economic benefits of the irrigation district as the second optimization objective, and the initial allocation of net irrigation water volume for each water-using grid unit as the initial optimization state.

8. The method for dividing irrigation district agricultural water use units and dynamically allocating initial water rights in a grid-based manner according to claim 7, characterized in that: Using the total water intake control line and the channel water carrying capacity as constraints, the multi-objective optimization function is optimized to obtain the initial water rights of each water-using grid unit. The specific steps are as follows: the total water intake control line constrains the total water allocation of each water-using grid unit, and the channel water carrying capacity constrains the water transmission and distribution volume of the corresponding channel. Under the conditions of satisfying the total water intake control limit and the channel water carrying capacity, the multi-objective optimization function is solved iteratively using a non-dominated sorting genetic algorithm. In each iteration, water allocation schemes that do not meet the constraints are eliminated according to the total water intake control limit and the channel water carrying capacity, and the water allocation schemes that meet the constraints are non-dominated sorted to obtain the Pareto solution set. Select target water allocation schemes that satisfy the collaborative objectives from the Pareto solution set, normalize the water demand satisfaction rate and economic benefits of each target water allocation scheme, calculate the comprehensive evaluation value based on the normalized water demand satisfaction rate and economic benefits, select the target water allocation scheme with the highest comprehensive evaluation value, and determine the water allocation amount corresponding to each water use grid unit in the target water allocation scheme as the initial water rights allocation amount of each water use grid unit.

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