An intelligent scheduling method and system for irrigation pipe networks based on hydraulic model

Abstract

This application discloses a method and system for intelligent scheduling of irrigation pipe networks based on a hydraulic model. By constructing a hydraulic irrigation simulation model, it is possible to simulate the hydraulic behavior of the pipe network under different operating scenarios, thereby achieving accurate simulation and prediction of the hydraulic behavior within the irrigation pipe network. On this basis, based on the type of crops in the target area and their water requirements during their growth cycle, and with the goal of optimizing water supply quality, an optimization objective function for intelligent scheduling of the irrigation pipe network is established. Not only is the minimum absolute error between the actual irrigation volume and the theoretical water demand considered, but key conditions such as water supply constraints, head valve working pressure constraints, and pipeline pressure constraints are also incorporated to ensure the scientific nature and rationality of irrigation scheduling. Therefore, it can significantly improve irrigation efficiency, save water resources, promote the healthy growth of crops, and provide effective technical support for solving problems such as water resource shortages and poor irrigation management in modern agriculture.

Description

Technical Field

[0001] The present application relates to the field of agricultural irrigation technology, and in particular to an intelligent scheduling method and system for an irrigation network based on a hydraulic model. Background Art

[0002] In modern agriculture, the effective management and utilization of water resources is a key factor in improving crop yield and quality. With global climate change and population growth, agricultural production faces increasingly severe challenges. Water shortage has become a major bottleneck hindering sustainable agricultural development. Traditional irrigation methods often rely on experience and fixed irrigation schedules, making it difficult to accurately meet the water needs of different crops at different growth stages and to irrigate large areas simultaneously. This leads to water waste and uneven crop growth, making it difficult to uniformly plan irrigation water use. For example, frequent irrigation can lead to soil salinity accumulation, while insufficient irrigation can affect crop growth and yield. Intelligent irrigation scheduling based on hydraulic models can address the challenges of existing technologies and provide a precise and efficient irrigation water allocation solution, significantly reducing water consumption and improving the uniformity and health of crop growth. Summary of the Invention

[0003] The embodiments of the present application provide an intelligent scheduling method and system for irrigation pipe networks based on a hydraulic model, which can achieve precise water distribution and pressure control, thereby reducing the risk of pipe bursts, improving irrigation efficiency, and reducing water waste.

[0004] In order to achieve the above-mentioned purpose, the technical solution of the embodiment of the present invention is:

[0005] In a first aspect, an embodiment of the present invention provides an intelligent scheduling method for an irrigation network based on a hydraulic model, comprising: constructing a hydraulic irrigation model topology map of the target area based on the irrigation water source and irrigation network data of the target area; performing hydraulic irrigation simulation calculations based on the hydraulic irrigation model topology map, completing the call and initialization configuration of the network topology, and providing a data call interface for the intelligent scheduling algorithm to realize the simulation operation of the hydraulic irrigation model; establishing an intelligent scheduling optimization objective function for the irrigation network based on the crop type in the target area with the goal of optimizing the water supply quality; the optimization objective function is used to minimize the absolute error between the actual irrigation volume and the theoretical water demand; establishing intelligent scheduling optimization constraints for the irrigation network by comprehensively considering the irrigation laws of crops, irrigation water quotas, and hydraulic coefficients of the irrigation network; the constraints include water supply constraints, head valve working pressure constraints, and pipeline health constraints; and solving the optimal irrigation network scheduling plan using a preset genetic algorithm based on the optimization objective function and the optimization constraints.

[0006] In some possible implementations, a water conservancy and irrigation model topology map of the target area is constructed based on the irrigation water source and irrigation pipe network data of the target area, including: drawing the water conservancy and irrigation model topology map of the target area based on the graphic information and irrigation data of the target area through preset water conservancy system simulation software; the water conservancy and irrigation model topology map can simulate the actual irrigation conditions of the target area.

[0007] In some possible implementations, the optimization objective function is expressed as:

[0008]

[0009] Where T represents different irrigation stages, i represents the node number, and D iT represents the theoretical water demand of node i in stage T, Q iT is the total watering amount of node i in stage T, T schedual The total irrigation stage is embodied as seven stages of crop planting in the present invention, i node Represents all irrigation nodes in the current irrigation area.

[0010] In some possible implementations, the water supply constraint includes a total water supply constraint for a stage and an irrigation water quota constraint;

[0011] The total water supply constraint in each stage is expressed as:

[0012]

[0013] The irrigation water quota constraint is expressed as;

[0014]

[0015] The working water pressure constraint of the head valve is expressed as:

[0016] H i (B)≥H idem ;

[0017] The pipeline health constraint is expressed as:

[0018]

[0019] Among them, maxD iT and minD iT are the maximum water consumption and minimum water demand of node i in stage T; Q dil It represents the annual water supply quota of the region, which is reflected in the model as the total flow of the first section of the pipeline connected to the reservoir node during the entire irrigation cycle; H i (B) represents the actual working water pressure of each node under the irrigation strategy, H idem is the minimum working pressure of the node; H jb(B) is the pressure of the first node of each pipeline, H jf (B) is the pressure of each pipeline tail node, K is the upper limit coefficient of pipeline pressure, H jul Design pressure for each pipeline.

[0020] In some possible implementations, based on the optimization objective function and optimization constraints, a preset genetic algorithm is used to solve and obtain the optimal irrigation network scheduling solution, including:

[0021] The fitness function is used to evaluate each generation of individuals, and the solution that meets the preset requirements or reaches the upper limit of the number of iterations is regarded as the optimal solution; the fitness function is expressed as:

[0022]

[0023] In a second aspect, an embodiment of the present invention provides an intelligent scheduling system for an irrigation network based on a hydraulic model, comprising: a model construction module for constructing a hydraulic irrigation model topology map for a target area based on the irrigation water source and irrigation network data of the target area; a model simulation operation module for performing hydraulic irrigation simulation calculations based on the hydraulic irrigation model topology map, completing the call and initialization configuration of the network topology, and providing a data call interface for the intelligent scheduling algorithm to realize the simulation operation of the hydraulic irrigation model; an optimization objective function construction module for establishing an optimization objective function for intelligent scheduling of the irrigation network based on the crop type in the target area and with the goal of optimizing water supply quality; the optimization objective function is used to minimize the absolute error between the actual irrigation volume and the theoretical water demand; an optimization constraint condition construction module for establishing optimization constraint conditions for intelligent scheduling of the irrigation network by comprehensively considering the irrigation patterns of the crops, the irrigation water quota, and the hydraulic coefficient of the irrigation network; the constraints include water supply constraints, head valve working pressure constraints, and pipeline health constraints; and a solution module for solving the optimal irrigation network scheduling solution using a preset genetic algorithm based on the optimization objective function and the optimization constraints to obtain the optimal irrigation network scheduling solution.

[0024] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0025] In the embodiment of the present invention, by constructing a hydraulic irrigation simulation model, it is possible to simulate the hydraulic behavior of the pipe network under different operating scenarios, thereby achieving accurate simulation and prediction of the hydraulic behavior in the irrigation pipe network. On this basis, according to the type of crops in the target area and their demand for water during their growth cycle, with the goal of optimizing water supply quality, an intelligent scheduling optimization objective function for the irrigation pipe network is established. Not only is the minimum absolute error between the actual irrigation volume and the theoretical water demand considered, but key conditions such as water supply constraints, head valve working pressure constraints, and pipeline pressure constraints are also incorporated to ensure the scientific nature and rationality of irrigation scheduling. Therefore, it can significantly improve irrigation efficiency, save water resources, promote the healthy growth of crops, and provide effective technical support for solving problems such as water shortages and poor irrigation management in modern agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A schematic flow chart of an embodiment of an intelligent scheduling method for an irrigation network based on a hydraulic model provided for the implementation of the present invention;

[0028] Figure 2 A water conservancy irrigation model topology diagram of an actual irrigation pipe network in an embodiment of the present invention;

[0029] Figure 3 A rotation irrigation area division diagram of the western area of ​​a certain actual irrigation pipe network in an embodiment of the present invention;

[0030] Figure 4 Schematic diagram of a single-objective genetic algorithm optimization process in an embodiment of the present invention;

[0031] Figure 5 This is a comparison chart of the irrigation error rate in seven irrigation stages between the traditional rotation irrigation system and the optimized scheduling of the present invention;

[0032] Figure 6 Schematic diagram of the structure of an intelligent scheduling system for an irrigation network based on a hydraulic model in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0034] In the relevant description of this embodiment, the terms "including, containing, having" and the like are open terms, and are generally understood to include but not be limited to; the term "at least one" is generally understood to mean one or more, where "plurality" refers to two or more; the term "at least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items, for example, "at least one of a, b or c", or "at least one of a, b and c", can all represent: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, c can be single or multiple respectively; the symbol "A / B" is used to describe the selection relationship of associated objects, generally indicating an "or" relationship before and after.

[0035] In the following description of the present embodiment, the terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0036] Those skilled in the art should understand that in the following description of the embodiments of the present application, the order of serial numbers does not mean the order of execution, some or all of the steps can be executed in parallel or sequentially, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0037] It will be understood by those skilled in the art that the numerical ranges in the examples of the present application are to be understood as also specifically disclosing each intermediate value between the upper and lower limits of the ranges. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the range is also included in the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded in the scope.

[0038] Unless otherwise indicated, the technical / scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which this application belongs. Although this application describes only preferred methods and materials, any methods and materials similar or equivalent to those herein may also be used in the implementation or testing of this application. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0039] In order to illustrate the technical solution of the present invention, specific embodiments are provided below.

[0040] In modern agriculture, the effective management and utilization of water resources is a key factor in improving crop yield and quality. With global climate change and population growth, agricultural production faces increasingly severe challenges. Water shortage has become a major bottleneck hindering sustainable agricultural development. Traditional irrigation methods often rely on experience and fixed irrigation schedules, making it difficult to accurately meet the water needs of different crops at different growth stages and to irrigate large areas simultaneously. This leads to water waste and uneven crop growth, making it difficult to uniformly plan irrigation water use. For example, frequent irrigation can lead to soil salinity accumulation, while insufficient irrigation can affect crop growth and yield. Intelligent irrigation scheduling based on hydraulic models can address the challenges of existing technologies and provide precise and efficient irrigation water allocation solutions, significantly reducing water consumption and improving the uniformity and health of crop growth.

[0041] Based on this, the embodiments of the present invention provide an intelligent scheduling method and system for irrigation pipe networks based on a hydraulic model, which can achieve precise water distribution and pressure control, thereby reducing the risk of pipe bursts, improving irrigation efficiency, and reducing water waste.

[0042] Figure 1 A schematic diagram of an embodiment of an intelligent scheduling method for an irrigation network based on a hydraulic model provided by the present invention is shown in FIG. Figure 1 As shown, the above method may include:

[0043] S101, constructing a water conservancy irrigation model topology map of the target area based on the irrigation water source and irrigation pipe network data of the target area;

[0044] Irrigation water sources include, but are not limited to, information on the location, volume, quality, temperature, and flow rate of surface water (such as rivers, lakes, and reservoirs), as well as information on groundwater (such as well depth, yield, and quality). Irrigation pipe network data includes, but is not limited to, the length, radius, valve nodes, elevation, and connections between pipes.

[0045] When constructing the irrigation model topology, the aforementioned parameters are comprehensively considered. Starting with the irrigation water source, the system uses the pipeline information in the irrigation network data to accurately depict the connections from the water source to each irrigation area, including the layout of main pipes, branch pipes, valves, and sprinklers. This information enables the creation of a detailed and accurate topology that not only displays the physical structure of the network but also encompasses the connections between the water source and the network, providing the fundamental data support and visual framework for subsequent irrigation scheduling optimization.

[0046] In some embodiments, the above step S101 can draw a water conservancy and irrigation model topology map of the target area based on the graphic information and irrigation data of the target area through preset water conservancy system simulation software; the water conservancy and irrigation model topology map can simulate the actual irrigation conditions of the target area.

[0047] Specifically, a professional geographic information system (GIS) or hydraulic simulation software (such as EPANET) can be used to construct a water conservancy irrigation model topology map for the target area based on this data. Taking the construction of a water conservancy irrigation model topology map using EPANET as an example: Step 1: Enter the drawing interface. Based on the actual irrigation area, add an irrigation water source (such as a reservoir) and set the location of the reservoir on the drawing interface. Step 2: Add nodes. Use the node button to add pipe connection nodes and valve nodes corresponding to the actual irrigation area on the drawing interface. Step 3: Add pipe segments. Each pipe segment connects two nodes. Curved pipe segments can also be added as needed. Step 4: Input parameters. Click on a node or pipe and enter its related attribute parameters, such as node name, type (such as water source, demand node), head or pipe length, diameter, material, and friction coefficient. Step 5: Generate and check the topology. Save the topology model as a .inp file to ensure that the topology can simulate the actual irrigation area for subsequent use.

[0048] For example, see Figure 2 As shown, Figure 2 This is a water conservancy irrigation model topology diagram of the actual irrigation pipe network in the embodiment of the present invention. After obtaining the data of the irrigation water source, irrigation pipe drawings, and irrigation valve control area of ​​the target area, through the above step S101, a water conservancy irrigation model topology diagram can be drawn. Figure 2 The topology diagram of the water conservancy irrigation model is shown.

[0049] S102, based on the water conservancy irrigation model topology diagram, perform hydraulic irrigation simulation calculations, complete the call and initialization configuration of the pipe network topology, and provide a data call interface for the intelligent scheduling algorithm to realize the simulation operation of the hydraulic irrigation model;

[0050] Specifically, after obtaining the constructed irrigation model topology, hydraulic irrigation simulation calculations are further performed. The core of this step is to use hydraulic simulation software or algorithms to simulate the hydraulic behavior of the irrigation network under different operating conditions based on the actual layout and operating parameters of the network.

[0051] First, import the pipe network topology into the hydraulic simulation software to complete the network call and initial configuration. For example, set parameters such as pipe material, diameter, flow rate, and pressure, and define the operating logic of key nodes such as valves and pump stations. These configurations ensure that the simulation results truly reflect the actual operation of the pipe network.

[0052] Next, the simulation software and the intelligent scheduling algorithm are seamlessly integrated using the data call interface required by the algorithm. This interface allows the algorithm to access the network data in the simulation software in real time, including key information such as flow, pressure, and water quality. This data provides the necessary input for the intelligent scheduling algorithm, enabling it to automatically calculate the optimal irrigation scheduling plan based on the current network status and operational objectives.

[0053] After completing the data call interface connection, the hydraulic irrigation model simulation is initiated. During this process, the simulation software simulates and calculates the hydraulic behavior of the pipe network in real time based on preset operating conditions and parameters. Simultaneously, the intelligent scheduling algorithm continuously receives and processes the data output by the simulation software, automatically adjusting the irrigation scheduling plan based on the algorithm logic to optimize pipe network operation.

[0054] For example, the Python-based WNTR tool library for hydraulic modeling can be used to implement simulations. WNTR offers the following capabilities: generating hydraulic and water quality models of pipe networks, modifying or manipulating pipe network structures, simulating pipe leaks, and visualizing results. The modeling process includes initialization, calling the pipe network topology, and running the simulation.

[0055] Initialization settings include time options and hydraulic simulation presets. Time options can be modified through wntr.options.time, including simulation duration, simulation step size, water demand mode step size, simulation start time, and the total duration and step size of the simulation result report. Hydraulic simulation presets can be modified through wntr.options.hydra ukic, including flow units, adjustment calculation formulas, default water demand mode, and water demand operators.

[0056] The details of creating and calling a pipe network topology in Python are as follows: 1. All calls and calculations for the topology network are based on the WNTR library. Ensure that the library is installed and imported. 2. Call the pipe network model based on the topology network storage address and copy it to the new variable wn. 3. Review the node and link information to ensure that everything corresponds to the actual data. If you need to modify the data, you can modify it by viewing the node properties, including: node water demand (base_demand), pressure (pressure), head (head), link length (length), friction (roughness), diameter (diameter), etc. Once the data is confirmed, simulation can begin.

[0057] The simulation is run by calling run_sim . Simulation results are returned in dictionary format, with each node and pipeline's simulation results stored in run_sim.node and run_sim.link . Node hydraulic simulation results include actual water demand, head, and pressure. Pipe hydraulic simulation results include flowrate, headloss, velocity, status, and friction factor.

[0058] S103, establishing an irrigation network intelligent scheduling optimization objective function based on the crop type in the target area and aiming at optimizing water supply quality; the optimization objective function is used to minimize the absolute error between the actual irrigation amount and the theoretical water demand;

[0059] For example, consider crop irrigation in a certain area. Assume the area is flat, with low soil moisture and little rainfall. The primary irrigation water source comes from a high mountain reservoir. Because water is supplied by gravity flow, the primary control is the opening and closing of valves. This control also needs to be tailored to the water requirements of the crops during their different growth cycles.

[0060] Assuming further that the primary crop in the region is apples, the annual water requirement for apples is 434-479 mm (millimeters). In areas with annual precipitation less than 450 mm, timely and appropriate irrigation according to water demand logic is necessary to meet the apple's water needs. Apple's water requirement increases annually as the tree ages and the amount of branches, leaves, and fruit increases. From budding to fruit ripening and harvesting, the average water consumption intensity per square meter of planting area is approximately 2.01-2.21 mm / d (millimeters / day). Water consumption varies greatly among different phenological periods. Specific irrigation requirements are as follows:

[0061] 1) Bud break period (early to mid-April): water consumption intensity is 1.21-1.47 mm / d.

[0062] 2) Flowering period (late April to early May): water consumption intensity is 1.44-1.81 mm / d.

[0063] 3) New shoots are vigorous and long-lasting (early May to early June): The water consumption intensity is the largest in the entire phenological period, which is 2.58-2.80 mm / d.

[0064] 4) New shoots stop growing for a long time (mid-June to early July): water consumption intensity is significantly reduced to 1.50-1.62 mm / d.

[0065] 5) Secondary growth period of new shoots (mid-July to early September): This period is also the period of rapid fruit expansion, with water consumption intensity of 2.42-2.64 mm / d. During this period, precipitation is unevenly distributed in time and space and transpiration is strong, and the total water consumption can reach 152.0-165.7 mm / d.

[0066] 6) Fruit ripening period (mid-September to mid-October): water consumption intensity is 2.07-2.31 mm / d.

[0067] 7) Leaf-falling period (late October to early November): Water consumption intensity is significantly reduced to 2.01-2.21 mm / d.

[0068] Based on the different scheduling durations of each stage and the basic water requirements per unit planting area, the apple tree is divided into the above seven stages from budding to fruit ripening and picking. The simulation step is set to 1 day, that is, the opening and closing of the head water supply valve (node ​​in the topology diagram) is scheduled on a daily basis. Therefore, the decision variable B = [b1, b2, b3, b4, b5, b6, b7] is a three-dimensional matrix, and b1 to b7 are all n T ×i node A two-dimensional matrix of type bool (elements are 0 or 1), representing the irrigation schemes from the 1st to the 7th stage. T is the number of scheduling days in irrigation stage T, i node is the number of head valves in the irrigation area. Based on the above analysis, the objective function of the optimization problem is set to maximize the satisfaction of irrigation water demand, that is, the absolute error between the actual irrigation amount and the theoretical water demand is minimized. The objective function expression can be expressed as:

[0069]

[0070] Where T represents different irrigation stages, i represents the node number, and D iT represents the theoretical water demand of node i in the Tth stage, T schedual The total irrigation stage is embodied as seven stages of crop planting in the present invention, i node Represents all irrigation nodes in the current irrigation area.

[0071] Q iTis the total water volume of node i in stage T, expressed as:

[0072]

[0073] Among them, q iT is the water supply of node i in one scheduling step in stage T, b iTn Indicates the on / off status of node i in the nth scheduling period of phase T, and can be 0 or 1.

[0074] S104: By comprehensively considering the irrigation patterns of crops, irrigation water quotas, and the hydraulic coefficients of the irrigation pipe network, constraints for intelligent scheduling optimization of the irrigation pipe network are established. The constraints include water supply constraints, head valve working pressure constraints, and pipe health constraints.

[0075] Specifically, taking the above-mentioned apple planting in a certain place as an example, after completing the construction of the irrigation scheduling optimization objective function to ensure irrigation quality, the concept of irrigation water quota is introduced for the apple tree planting area: the irrigation water quota refers to the amount of irrigation water for a certain area of ​​land during the apple preparation period and the entire crop growth period. The calculation of the irrigation water quota usually involves factors such as crop evaporation, soil evaporation and rainfall, and can be calculated by subtracting rainfall from the sum of crop evaporation and soil evaporation. In the prior art, the general value of the irrigation water quota is determined by the net water quota and the irrigation water utilization coefficient of the existing large and medium-sized irrigation district hoppers, small irrigation district canal heads, and wellheads. The calculation method is expressed as follows:

[0076] m 通用 =m 净 / η 斗 ;

[0077] Among them, m 通用 It is the general value of irrigation water quota, in m 3 / mu; m 净 Net water quota, unit is m 3 / mu; η 斗 The irrigation water utilization coefficient is determined based on the actual water use scenario. When specifying the advanced value of the irrigation water quota, the irrigation water utilization coefficient of the actual irrigation technology can be substituted into the above formula for calculation.

[0078] Among them, the irrigation water utilization coefficients of common water-saving irrigation technologies are: η 渠道防渗 =0.7,η 管道 =0.8,η 喷灌 =0.8,η 微灌 =0.85.

[0079] When determining specific irrigation water quotas, the concept of a hydrological year is important. This concept corresponds to hydrological conditions and is used to describe a year under specific hydrological conditions. The guarantee rate represents the degree of drought within a hydrological year. A hydrological year with a guarantee rate of 50% is generally considered a normal year, while a year with a guarantee rate of 75% is considered a drought year.

[0080] From the above, it can be determined that the irrigation water quota constraint of the optimization objective function can be expressed as:

[0081]

[0082] Among them, Q dil Indicates the annual water supply quota for the area. Figure 2 It is reflected as the total flow of the first section of pipeline outside the reservoir node during the entire irrigation cycle.

[0083] In some embodiments, Figure 2 The irrigation area shown is divided into the southern area, the northern area and the western area. Taking the western area as an example, the traditional rotation irrigation strategy design is as follows: Figure 3 As shown in the figure, irrigation valves are opened in a planned manner according to manual experience. The main irrigation method in the irrigation area is drip irrigation. The irrigation area is divided into four rotation irrigation areas according to the main and branch lines. The total area is about 3895.6 mu. The average soil bulk density before rainfall in the 1m soil layer is 1.3g / m 3 The main crop in this area is apple, and the designed maximum water consumption is 3mm / d. Therefore, the hourly water consumption during drip irrigation is 2.62mm / m 2 In addition, the designed water requirements of fruit trees in the seven stages from budding to leaf drop are 1.5mm / d, 2mm / d, 3mm / d, 2mm / d, 3mm / d, 2.5mm / d, 2.5mm / d, and the irrigation water quota is 634.78m 3 / mu, which basically meets the guaranteed irrigation water supply rate in a normal water year. Data related to the rotation irrigation system in September for this area are shown in Table 1, and the number of scheduling steps and water demand in each stage are shown in Table 2.

[0084] Table 1:

[0085]

[0086] Table 2:

[0087]

[0088] Based on this, the total water supply constraint of the objective function can be determined as follows:

[0089]

[0090] Among them, maxD iT and minD iTare the maximum water consumption and minimum water demand of node i in stage T respectively.

[0091] Understandably, during the actual irrigation rotation process, some farmers have illegally connected production water pipes, leading to excessive water use. This unauthorized connection not only results in water shortages and lower reservoir levels, but can also cause pipes to be emptied, creating negative pressure and endangering water supply safety. Therefore, the irrigation district's intelligent scheduling system finely allocates irrigation volume to each irrigation head, formulating water supply scheduling plans for each irrigation head based on the fruit tree irrigation cycle and water demand.

[0092] In some embodiments, the head valve working water pressure constraint is expressed as:

[0093] H i (B)≥H idem ;

[0094] Among them, the constraints H i (B) represents the actual working water pressure of each node under the irrigation strategy, H idem is the minimum working pressure of the node.

[0095] In some embodiments, the health of pipelines should also be considered when formulating water supply scheduling plans for irrigation areas. The pressure H of each pipeline during the scheduling process j (B) must be less than the design pressure H jul K times, where H j (B) can be expressed as its first node pressure H jf (B) and tail node pressure H jb The mean of (B), the pipeline health constraint expression can be expressed as:

[0096]

[0097] S105, according to the optimization objective function and optimization constraints, a preset genetic algorithm is used to solve and obtain the optimal irrigation network scheduling plan.

[0098] Specifically, a genetic algorithm is used to solve the irrigation scheduling optimization problem established in steps S103 and S104. The basic concept of a genetic algorithm is to encode the solution to the problem into a set of genes or chromosomes, and then use genetic operations (including selection, crossover, and mutation) to evolve these individual solutions. Each individual solution is assigned a fitness value, which is used to evaluate its performance in the solution space. During the evolutionary process, solutions with higher fitness are more likely to be used to generate the next generation of solutions.

[0099] Figure 4 This is a schematic diagram of the single-objective genetic algorithm optimization process in an embodiment of the present invention, see Figure 4As shown, the primary task of the genetic algorithm to solve the single-objective optimization problem is to define the fitness function and determine the genetic operator. In the process of algorithm iteration, the fitness function is used to evaluate each generation of individuals. The fitness value reflects the excellence of the individuals in the population. Individuals with higher fitness are better solutions. As the genetic algorithm proceeds, the individual fitness value will continue to increase, and the quality of the solution will improve accordingly, until a solution that meets the requirements is obtained or the number of iterations reaches the upper limit, and the algorithm iteration is terminated. The optimization goal in the example adopted by the present invention is to minimize the absolute error between the actual water supply and the theoretical water storage capacity. Therefore, the absolute value of the objective function can be taken as the fitness function. The minimum fitness value means the minimum absolute error rate. Therefore, the expression of the fitness function can be expressed as:

[0100]

[0101] The definitions and values ​​of the variables in the above formula are as described above. For example, the irrigation area consists of 153 head valves, and the scheduling cycle is divided into 7 stages, where the number of scheduling steps in each stage is n. T They are 20 days, 30 days, 30 days, 30 days, 60 days, 40 days, and 20 days respectively, and the scheduling step is 1 day. Therefore, the decision variable B (given by b iTn The array composed of 7×n T ×153 three-dimensional array.

[0102] Since the decision variable is a three-dimensional matrix and the number of rows and columns of each element is different, it is necessary to crossover different elements separately when performing the crossover operation. The operation steps are as follows: Step 1. Calculate the number of individuals n participating in the crossover according to the crossover operator, and select n individuals with the best fitness values ​​as parent individuals; Step 2. Traverse these n parent individuals, and for each parent individual, randomly select an individual from all individuals as the parent individual to cross with it; Step 3. Traverse each first-level index of the parent individual; Step 4. Randomly take a section in each corresponding second-level index of the parent individual and assign it to the corresponding position of the mother individual.

[0103] Similar to the crossover operation, the compilation operation also requires separate considerations for different orders. Individuals are randomly selected for mutation. For each individual participating in the mutation, a random segment is selected from each of its secondary indexes for mutation. Since the decision variables are encoded as binary code, the compilation operation involves randomly flipping the mutated code segments.

[0104] The present invention provides an intelligent scheduling method for irrigation pipe networks based on a hydraulic model. The method is programmed in Python, and the algorithm parameters can be set as follows: crossover operator (crossover probability is 80%), mutation operator (mutation probability is 10%), initial population size is 30, and the maximum number of iterations is 500 generations.

[0105] Specific steps are as follows Figure 4 As shown in the figure, (1) determine the encoding method, fitness function, operator and algorithm parameters of the individuals in the population; (2) generate a four-dimensional array with the element format of h×7×nT×153 within the feasible domain, where h represents the population size and 7×nT×153 represents the opening and closing combination of 153 head valves in 7 scheduling stages, as the initial population. (3) update the pipe network parameters according to the stage, substitute the individuals into the hydraulic model to run the simulation, and obtain the flow and pressure data of each individual. (4) calculate the fitness value of each individual in the population according to formula (11). (5) judge whether the convergence condition is met. If there is an individual whose fitness value reaches the expectation or the number of iterations reaches the maximum, jump to (7), otherwise execute the next step; (6) perform selection, crossover and mutation operations to generate a new population. Go to (3); (7) the algorithm outputs the current optimal solution, that is, the optimal irrigation head scheduling plan, which is encapsulated as a .txt format document.

[0106] In some embodiments, after obtaining the optimal irrigation network scheduling solution, the results can be further analyzed and evaluated. For example, based on real data from a local irrigation network, programming experiments were conducted using the Python platform. The optimization algorithm parameters can be set as shown in Table 3.

[0107] Table 3:

[0108]

[0109] As shown in Table 3, the pipeline pressure limit coefficient K refers to the ratio of the maximum internal pressure allowed in the pipeline during scheduling to the pipeline's maximum design pressure. The actual water demand of a node is related to the theoretical water demand, DiT, of that node. During scheduling, a node may be open or closed, and its water supply is zero when closed. Therefore, to ensure that the required irrigation volume is met throughout the entire cycle, the water supply of the node should be higher than its theoretical water demand when it is open. After multiple simulation experiments, the node water demand was set to 1.85 times the theoretical water demand.

[0110] Table 4 shows the scheduling process and irrigation error rate of the five head valves in the west branch of the irrigation area 1 within 20 scheduling steps in irrigation phase 1. As can be seen from Table 4, the average error rate between the actual irrigation amount and the theoretical water demand is 7.05%.

[0111] Table 4:

[0112]

[0113] Figure 5 This is a comparison chart of the irrigation error rate between the traditional rotation irrigation system and the optimized scheduling of the present invention in 7 irrigation stages. Figure 5As shown, the average irrigation error rate for traditional rotational irrigation scheduling is 19.6%. This error is primarily caused by irrigation exceeding the theoretical water requirement. The higher error rates in stages 4 and 5 are due to the rotational irrigation system being designed to maintain soil moisture levels by oversupplying water in the face of high summer evaporation. The average irrigation error rate for the scheduling strategy using the proposed method is 6.49%. This refined scheduling allows for timely soil moisture replenishment, ensuring high-quality crop irrigation.

[0114] In the embodiment of the present invention, by constructing a hydraulic irrigation simulation model, it is possible to simulate the hydraulic behavior of the pipe network under different operating scenarios, thereby achieving accurate simulation and prediction of the hydraulic behavior in the irrigation pipe network. On this basis, according to the type of crops in the target area and their demand for water during their growth cycle, with the goal of optimizing water supply quality, an intelligent scheduling optimization objective function for the irrigation pipe network is established. Not only is the minimum absolute error between the actual irrigation volume and the theoretical water demand considered, but key conditions such as water supply constraints, head valve working pressure constraints, and pipeline pressure constraints are also incorporated to ensure the scientific nature and rationality of irrigation scheduling. Therefore, it can significantly improve irrigation efficiency, save water resources, promote the healthy growth of crops, and provide effective technical support for solving problems such as water shortages and poor irrigation management in modern agriculture.

[0115] Based on the same inventive concept, the embodiment of the present application also provides an intelligent scheduling system for irrigation pipe networks based on a hydraulic model. Figure 6 This is a structural diagram of the intelligent scheduling system for irrigation pipe networks based on a hydraulic model in an embodiment of the present invention, see Figure 6 As shown, the irrigation network intelligent scheduling system 600 based on the hydraulic model may include:

[0116] The model building module 601 is used to build a water conservancy irrigation model topology map of the target area based on the irrigation water source and irrigation pipe network data of the target area;

[0117] The model simulation operation module 602 is used to perform hydraulic irrigation simulation calculations based on the hydraulic irrigation model topology diagram, complete the call and initialization configuration of the pipe network topology, and provide a data call interface for the intelligent scheduling algorithm to realize the simulation operation of the hydraulic irrigation model;

[0118] The optimization objective function construction module 603 is used to establish an optimization objective function for intelligent scheduling of the irrigation network based on the crop type in the target area and with the goal of optimizing water supply quality; the optimization objective function is used to minimize the absolute error between the actual irrigation amount and the theoretical water demand;

[0119] Optimization constraint building module 604 is used to establish optimization constraints for intelligent scheduling of the irrigation network by comprehensively considering the irrigation patterns of crops, irrigation water quotas, and the hydraulic coefficient of the irrigation network. The constraints include water supply constraints, head valve working pressure constraints, and pipeline health constraints.

[0120] The solution module 605 is used to solve the problem using a preset genetic algorithm according to the optimization objective function and optimization constraints to obtain the optimal irrigation network scheduling solution.

[0121] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments.

[0122] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. An intelligent scheduling method for irrigation pipe network based on hydraulic model, characterized in that: include: Constructing a water conservancy irrigation model topology map of the target area based on the irrigation water source and irrigation pipe network data of the target area; According to the water conservancy irrigation model topology diagram, hydraulic irrigation simulation calculation is performed to complete the call and initialization configuration of the pipe network topology, and a data call interface is provided for the intelligent scheduling algorithm to realize the simulation operation of the hydraulic irrigation model; According to the crop type in the target area, an optimization objective function for intelligent scheduling of the irrigation network is established with the goal of optimizing water supply quality. The optimization objective function is used to minimize the absolute error between the actual irrigation amount and the theoretical water demand. The objective function is expressed as: ； in, Indicates different irrigation stages, represents the node number, Indicates the Stage Node The theoretical water requirement, The total irrigation stage is embodied as seven stages of crop planting in the present invention. Represents all irrigation nodes in the current irrigation area, For nodes In the stage The total irrigation volume is expressed as: ； in, For the stage node The water supply within a scheduling step is For nodes In the stage No. The open and close status of each scheduling period, the value is 0 or 1; By comprehensively considering the irrigation patterns of crops, irrigation water quotas, and the hydraulic coefficients of the irrigation pipe network, the intelligent scheduling optimization constraints of the irrigation pipe network are established. The constraints include water supply constraints, head valve working pressure constraints, and pipe health constraints. The water supply constraints include the total water supply constraints of the stage and the irrigation water quota constraints; The total water supply constraint in this stage is expressed as: ； in, and Node In the stage The maximum water consumption and minimum water demand within For nodes In the stage No. The open and closed status of each scheduling period, For the stage node Water supply within a scheduling step; The irrigation water quota constraint is expressed as: ； in, Indicates the annual water supply quota for the area; The working pressure constraint of the head valve is expressed as: ； in, represents the actual working water pressure of each node under the irrigation strategy, is the minimum working pressure of the node; The pipeline health constraint is expressed as: ； in, is the pressure of the first node of each pipeline, is the pressure at the tail node of each pipeline, is the upper limit coefficient of pipeline pressure, Design pressure for each pipeline; According to the optimization objective function and the optimization constraints, a preset genetic algorithm is used to solve the problem and obtain the optimal irrigation network scheduling solution.

2. The method according to claim 1, characterized in that The step of constructing a water conservancy irrigation model topology map of the target area based on the irrigation water source and irrigation pipe network data of the target area includes: According to the graphic information and irrigation data of the target area, a water conservancy and irrigation model topology diagram of the target area is drawn through preset water conservancy system simulation software; the water conservancy and irrigation model topology diagram can simulate the actual irrigation situation of the target area.

3. The method according to claim 2, characterized in that According to the optimization objective function and the optimization constraints, a preset genetic algorithm is used to solve the problem and obtain the optimal irrigation network scheduling solution, including: The fitness function is used to evaluate each generation of individuals, and the solution that meets the preset requirements or reaches the upper limit of the number of iterations is regarded as the optimal solution; wherein the fitness function is expressed as: 。 4. An intelligent scheduling system for irrigation pipe networks based on hydraulic models, characterized in that: include: A model building module is used to build a water conservancy irrigation model topology map of the target area based on the irrigation water source and irrigation pipe network data of the target area; The model simulation operation module is used to perform hydraulic irrigation simulation calculations based on the water conservancy irrigation model topology diagram, complete the call and initialization configuration of the pipe network topology, and provide a data call interface for the intelligent scheduling algorithm to realize the simulation operation of the hydraulic irrigation model; The optimization objective function construction module is used to establish an optimization objective function for intelligent scheduling of the irrigation network based on the crop type in the target area and with the goal of optimizing water supply quality; the optimization objective function is used to minimize the absolute error between the actual irrigation amount and the theoretical water demand; the objective function is expressed as: ； in, Indicates different irrigation stages, represents the node number, Indicates the Stage Node The theoretical water requirement, The total irrigation stage is embodied as seven stages of crop planting in the present invention. Represents all irrigation nodes in the current irrigation area, For nodes In the stage The total irrigation volume is expressed as: ； in, For the stage node The water supply within a scheduling step is For nodes In the stage No. The open and close status of each scheduling period, the value is 0 or 1; An optimization constraint construction module is used to establish optimization constraints for intelligent scheduling of irrigation pipe networks by comprehensively considering the irrigation patterns of crops, irrigation water quotas, and the hydraulic coefficients of the irrigation pipe networks. These constraints include water supply constraints, head valve operating pressure constraints, and pipe health constraints. These water supply constraints include total water supply constraints for each stage and irrigation water quota constraints. The total water supply constraint in this stage is expressed as: ； in, and Node In the stage The maximum water consumption and minimum water demand within For nodes In the stage No. The open and closed status of each scheduling period, For the stage node Water supply within a scheduling step; The irrigation water quota constraint is expressed as: ； in, Indicates the annual water supply quota for the area; The working pressure constraint of the head valve is expressed as: ； in, represents the actual working water pressure of each node under the irrigation strategy, is the minimum working pressure of the node; The pipeline health constraint is expressed as: ； in, is the pressure of the first node of each pipeline, is the pressure at the tail node of each pipeline, is the upper limit coefficient of pipeline pressure, Design pressure for each pipeline; The solution module is used to solve the problem using a preset genetic algorithm according to the optimization objective function and the optimization constraints to obtain the optimal irrigation network scheduling solution.

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