Farmland irrigation water flow path planning method

Abstract

The invention discloses a farmland irrigation water flow path planning method, which comprises the following steps of: firstly, constructing a three-dimensional space model containing water supply points and farmland position coordinates to ensure that planned basic data has high accuracy; then, on the basis of the three-dimensional model, a water supply point is definitely designated as a starting point, each water flow point moves from the starting point to an end point, a plurality of possible water flow paths are formed, and the diversity of planning is greatly enriched. Then the optimal water flow path with the lowest energy consumption is screened out from the multiple paths through the path planning principle, so that the planning accuracy and practicability are improved, and the purpose of reducing the irrigation cost under the current condition is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of farmland irrigation path planning, and in particular to a farmland irrigation water flow path planning method. Background Art

[0002] During the cultivation process, farmland needs to be irrigated regularly and quantitatively to maintain the normal growth of crops. At present, there are two common irrigation methods for farmland. One is to open ditches in the fields and then release water through the ditches for flooding. The other is to lay pipes in the fields and then irrigate water through the pipes.

[0003] Although both methods can achieve farmland irrigation, the first method requires the farmland to be located in a low-lying and flat terrain, which results in a limited number of application scenarios. On the contrary, since the method of irrigation through pipes does not need to consider factors such as the terrain of the farmland, it has a wider range of applications.

[0004] In the process of delivering water through pipes, there are usually two types of pipe layouts. One is that a non-removable pipe has been laid in the farmland in advance. When water needs to be delivered, the corresponding valve can be opened to deliver water. The other is to temporarily lay pipes that can be removed at any time after use. Directly aiming the pipe mouth at the farmland that needs to be irrigated can achieve rapid irrigation, eliminating the cumbersome operation of controlling the valve. However, in actual use, whether the operator controls the valve or temporarily lays the pipe, the purpose is to complete the water delivery. Therefore, during the operation, the efficiency of water delivery and energy conservation will not be considered too much, but the relevant valves will be opened randomly or the pipes will be laid at will, as long as water can be delivered. This will lead to excessive energy consumption corresponding to the water flow path formed during the irrigation process, because higher irrigation costs are required. Summary of the invention

[0005] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a method for planning a water flow path for farmland irrigation. The present invention plans a more reasonable water flow path in the farmland, thereby reducing energy consumption to the greatest extent, thereby achieving the purpose of reducing irrigation costs.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for planning a farmland irrigation water flow path includes the following planning steps:

[0008] S1. Construct a three-dimensional spatial model including the location coordinates of the water supply points in the area and the location coordinates of all farmlands;

[0009] S2, taking the water supply point in the three-dimensional space model as the starting point, and selecting a number of water flow points at the starting point;

[0010] S3, taking the farmland that needs water supply in the three-dimensional space model as the end point, making each water flow point move from the starting point to the end point, and each forming a water flow path;

[0011] S4. Select a water flow path with the lowest energy consumption from all water flow paths through the path planning principle. This water flow path is the optimal water flow path from the starting point to the end point.

[0012] As a further solution of the present invention: the process of selecting the water flow point in step S2 is as follows:

[0013] S21, defining the locations outside the water supply points and all farmlands in the three-dimensional space model as the value space of the water flow points;

[0014] S22, based on the value space, selecting a set number of spatial points in the three-dimensional space model, and calculating the position coordinates of each spatial point using a position coordinate calculation formula;

[0015] S23, taking the water supply point in the three-dimensional space model as the starting point, and setting a three-dimensional neighborhood with the water supply point as the center point;

[0016] S24. Select the spatial points that fall into the three-dimensional neighborhood of step S23 from all the spatial points in step S22, and record these spatial points as water flow points. At the same time, mark the position coordinates of these spatial points as the initial position coordinates of the corresponding water flow points, and store the water flow points and their corresponding initial position coordinates in the initial population set.

[0017] As a further solution of the present invention: the position coordinate calculation formula is as follows:

[0018]

[0019]

[0020] In the formula, Represents the initial position coordinates of the nth spatial point in the value space, the superscript 0 represents the initial state, n∈[1,N], where N represents the total number of spatial points; Indicates the value of the initial position coordinates of the nth spatial point in the value space in the first dimension; Indicates the value of the initial position coordinates of the nth spatial point in the value space in the jth dimension; Indicates the value of the initial position coordinates of the nth spatial point in the value space in the Jth dimension; Indicates the minimum value of the value space in the jth dimension; It represents the maximum value of the value space in the jth dimension; Tent(i,j) represents the chaotic random number of the nth spatial point in the value space in the jth dimension; Tent(n,j-1) represents the chaotic random number of the nth spatial point in the value space in the j-1th dimension, and when j-1=1, Tent(n,j-1) is a random number in [0,1]; δ represents a random number in [0,1].

[0021] As a further solution of the present invention: the process of forming the water flow path in step S3 is as follows:

[0022] S31, selecting the water flow point closest to the water supply point in the initial population set, and recording the water flow point as the initial elite water flow point;

[0023] S32, determining the farmland that needs water supply in the three-dimensional space model, and taking the farmland as the end point, connecting the starting point and the end point, and providing a direction for updating the position of the water flow point;

[0024] S33, the initial position coordinates of the initial elite water flow point and the water flow points in the initial population set are simultaneously brought into the water flow point position coordinate update formula as initial values, and the end point is reached through gradual iterative updates, and the path traversed by each water flow point constitutes the water flow path of the water flow point.

[0025] As a further solution of the present invention: the water flow point position coordinate update formula is expressed as follows:

[0026]

[0027] In the formula, represents the position coordinates of the i-th water flow point in the initial population set in the m+1th iteration, m∈[0,M], M represents the total number of iterations, i∈[1,N]; represents the position coordinates of the i-th water flow point in the initial population set in the m-th iteration; Levy(d) represents the flight distribution function; x m ,y m and z m Both represent random parameters in the mth iteration; Represents the energy value of the i-th water flow point in the initial population set in the m+1th iteration; represents the energy value of the i-th water flow point in the initial population set in the m-th iteration, and when m=0, the energy value is 0; rand(0,1) represents a random number in [0,1]; β m represents the random walk coefficient; else represents the meaning of mathematical logic or; represents the position coordinates of the elite water flow points of all water flow points in the initial population set in the mth iteration, and when m = 0, That is the position coordinates of the initial elite water flow point; The selection rule is: in the mth iteration, the energy value E in the initial population set m The position coordinate X of the minimum water flow point m That is

[0028] As a further solution of the present invention: the calculation formula of the energy value is as follows:

[0029]

[0030] Where η1 represents the time weighting factor, η2 represents the energy consumption weighting factor, and η1+η2=1; t m,m-1 Indicates that the i-th water flow point in the initial population set is Move to The time spent, represents the position coordinates of the i-th water flow point in the initial population set in the m-1th iteration; when m-1=-1 or 0, t m,m-1 is 0 and equal represents the initial position coordinates of the i-th water flow point in the initial population set; σ represents the magnitude parameter; R i represents the number of lift pumps used to raise the water level on the path that the i-th water flow point in the initial population set has passed; Indicates that the i-th water flow point in the initial population set is When , the torque of the rth lift pump on the path it has passed; Indicates that the i-th water flow point in the initial population set is When , the rotational angular acceleration of the rth lift pump on the path it has passed; express and The absolute value of the product; dt represents the differential with respect to time.

[0031] As a further solution of the present invention: the flight distribution function is specifically calculated as follows:

[0032]

[0033] Where w and k both represent random numbers in [0,1]; λ represents a random number in [0,2]; Γ(·) represents the gamma function.

[0034] As a further solution of the present invention: the random walk coefficient is specifically calculated as follows:

[0035]

[0036] x m =ρm ×cos(θ m );

[0037] y m =ρ m ×sin(θ m );

[0038] z m =ρ m ×θ m ;

[0039] In the formula, e represents a natural constant; ρ m represents the length parameter in the mth iteration; θ m Represents a random number in [0,2π] in the mth iteration.

[0040] As a further solution of the present invention, the process of selecting the optimal water flow path in step S4 is as follows:

[0041] S41. Calculate the total energy value of each water flow path using the path planning principle. The calculation formula is as follows:

[0042]

[0043] In the formula, E i Represents the total energy value of the i-th water flow point in the initial population set during the entire iteration process;

[0044] S42, selecting a water flow path corresponding to the minimum total energy value, which is the optimal water flow path from the starting point to the end point.

[0045] As a further solution of the present invention: the process of establishing the three-dimensional space model in step S1 is as follows:

[0046] S11. Identify the geographical area to be scanned, including water supply points and all farmland areas;

[0047] S12, scanning the geographic area using a laser radar and saving the scan data;

[0048] S13, converting the collected scanning data into point cloud data, and using a registration algorithm to convert the point cloud data into a complete three-dimensional space model.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention first ensures that the basic data of the planning is highly accurate by constructing a three-dimensional spatial model including the coordinates of the water supply point and the farmland location. Then, based on the three-dimensional model, the water supply point is clearly specified as the starting point, and each water flow point is allowed to move from the starting point to the end point, forming multiple possible water flow paths, which greatly enriches the diversity of planning. Then, among the multiple paths, the optimal water flow path with the lowest energy consumption is screened out through the path planning principle, thereby improving the accuracy and practicality of the planning and achieving the purpose of reducing irrigation costs under current conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the present invention.

[0052] Figure 2 This is a diagram of the farmland irrigation system in the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 , the present invention mainly includes the following contents.

[0055] 1. Construct a three-dimensional space model.

[0056] Using radar scanning (especially laser radar, or LiDAR) to build a three-dimensional spatial model that includes the coordinates of the water supply points and all the farmland in the area is a process that combines high-precision measurement and three-dimensional modeling technology. The main steps include:

[0057] 1.1. Preliminary preparation:

[0058] Determine the scanning area: Identify the geographical area that needs to be scanned, including areas where water supply points and farmland are located.

[0059] Plan scanning paths: Plan the best scanning paths and site locations based on terrain and obstacles to ensure data integrity and accuracy.

[0060] Prepare equipment: Select a suitable lidar scanner and other necessary auxiliary equipment, such as Beidou navigation receiver, total station, etc.

[0061] 1.2 Data Collection:

[0062] Perform lidar scans: Use lidar scanners to collect data at planned sites and paths. Lidar generates point cloud data by emitting laser pulses and measuring their return time to calculate distance.

[0063] Synchronize Beidou navigation data: During the scanning process, Beidou navigation data is recorded synchronously to convert point cloud data into three-dimensional coordinates with precise geographic location.

[0064] 1.3 Data processing:

[0065] Point cloud data generation and registration: The collected radar data is converted into point cloud data, and the point cloud data collected at different sites or at different times are merged into a complete 3D model using a registration algorithm.

[0066] Filtering and cleaning: Remove noise points, ground points (if not required), and other non-target objects (such as vehicles, pedestrians, etc.) to improve the quality of the data.

[0067] Feature extraction: Extract key features such as boundaries and center points of water supply points and farmland from point cloud data.

[0068] 1.4. 3D model construction:

[0069] Select modeling software: Choose appropriate 3D modeling software according to your needs, such as ArcGIS, BentleyMicroStation, Autodesk Revit, etc.

[0070] Create 3D objects: Based on the extracted features, create 3D objects in the modeling software, such as point objects for water supply points, polygon objects for farmland, etc. You can directly generate 3D models using point cloud data, or you can model based on contour lines extracted from point cloud data.

[0071] Adding height information: Point cloud data contains height information, which can be used to create 3D models with realistic terrain and vegetation.

[0072] Textures and Materials: Add textures and materials to 3D objects to enhance the realism and visual quality of your models.

[0073] After the above series of operations, a three-dimensional spatial model is obtained that truly restores the three-dimensional landform information of the farmland in the corresponding proportion.

[0074] 2. Get the initial population set.

[0075] Taking the water supply point as the starting point, based on the optimization of the Hippocampus algorithm, the position coordinates of the water flow point in the three-dimensional area of ​​the starting point are obtained. The specific process is as follows:

[0076] 2.1、Define the value space:

[0077] In the process of farmland planning and design, a water supply point is usually installed for several adjacent farmlands, so that a unit water supply system can be formed. The present invention also focuses on the study of a unit water supply system, and the remaining unit water supply systems are processed in the same way.

[0078] In the unit water supply system, all locations except the water supply point and the farmland area can be defined as the value space of the water flow point. However, in the actual design process, the area where the pipeline can be laid is usually on the ridge. Therefore, the spatial position on the ridge in the unit water supply system constitutes the value range of the water flow point. This means that the water flows in the pipe laid on the ridge. The end point usually refers to the point in the farmland that needs water supply with the shortest distance to the power supply point. Setting a water outlet at this point can realize irrigation in the farmland.

[0079] 2.2. Select a spatial point and calculate the position coordinates:

[0080] After determining the value space, a certain number of spatial points need to be selected randomly or according to certain rules in this value space. These points represent the potential locations where the water flow points may pass. Then, the specific position coordinates of each selected spatial point are calculated using the position coordinate calculation formulas shown in formulas (1) to (3).

[0081] The position coordinate calculation formula is as follows:

[0082]

[0083]

[0084] It should be noted that when j-1=1, Tent(n,j-1) is a random number in [0,1].

[0085] 2.3. Set the three-dimensional neighborhood of the water supply point:

[0086] Next, a three-dimensional neighborhood is set with the water supply point as the center. The size of this three-dimensional neighborhood can be adjusted according to the actual situation to simulate the range that the water flow can cover starting from the water supply point. The points in this neighborhood are more likely to become actual water flow points because they are closer to the water supply point and are concentrated near the water supply point, indicating that the water flow starts from the water supply point.

[0087] 2.4. Select the water flow points and record the initial position coordinates:

[0088] From all the spatial points selected in 2.2, select those points that fall within the three-dimensional neighborhood set in 2.3. These points are considered to be actual water flow points because they are within the direct influence of the water supply point. Then, the position coordinates of these water flow points are recorded as their initial position coordinates, and these water flow points and their corresponding initial position coordinates are stored in an initial population set. This initial population set will be used in the subsequent water flow simulation iteration process.

[0089] In summary, by defining the value space, selecting and calculating the position of spatial points, setting the neighborhood of water supply points, screening water flow points and recording their initial position coordinates, basic data and framework are provided for simulating water flow distribution in a three-dimensional spatial model.

[0090] 3. Plan the water flow path.

[0091] Based on the optimization of the Hippocampus algorithm, each water flow point moves from the starting point to the end point and forms a water flow path respectively; the specific process is as follows:

[0092] 3.1. Select the water flow point closest to the water supply point in the initial population set and record it as the initial elite water flow point:

[0093] Purpose: It is necessary to select a starting point from all possible water flow points that is most likely to be close to or quickly reach the water supply point. This point is specially marked as the initial elite water flow point.

[0094] Operation: Calculate the distance from each water flow point to the water supply point, and select the one with the shortest distance as the initial elite water flow point.

[0095] 3.2. Determine the farmland that needs water supply in the three-dimensional space model, and use the farmland as the end point, connect the starting point and the end point, and provide direction for the location update of the water flow point:

[0096] Purpose: To clarify the target location where the water flow needs to reach and provide a basic directional guide for the movement of the water flow point.

[0097] Operation: Determine the specific location of the farmland in the 3D spatial model and take the point closest to the water supply point as the end point. Then, draw a straight line from the start point to the end point or determine a basic direction, which will serve as a reference or preferred direction for the water flow point to move.

[0098] 3.3. Substitute the initial position coordinates of the initial elite water flow point and the water flow points in the initial population set as initial values ​​into the water flow point position coordinate update formula shown in formula (4), and gradually iterate and update to reach the end point. The path traversed by each water flow point constitutes the water flow path of the water flow point:

[0099] Purpose: Iteratively update the position of each water flow point so that it gradually approaches and eventually reaches the farmland (end point), while recording the moving path of each water flow point.

[0100] Action: Define a position update formula based on multiple factors such as current position, velocity, acceleration, preferred direction (the direction from the start point to the end point), and possibly other optimization parameters (such as resistance, change in water velocity, etc.).

[0101] The current positions of all water flow points (including the initial elite water flow point) are input into the position update formula as initial values.

[0102] Through multiple iterations, the position of each water flow point is continuously updated until they approach or reach the end point.

[0103] In each iteration, the position of each water flow point is recorded to construct their flow path from the starting point to the end point.

[0104] This process combines the ideas of optimization algorithms and path planning to find the optimal (or nearly optimal) path from the water supply point to the farmland by simulating the flow of water in three-dimensional space.

[0105] The formula for updating the coordinates of the water flow point position is as follows:

[0106]

[0107] The calculation formula of energy value is shown in formula (5):

[0108]

[0109] The flight distribution function is specifically shown in formula (6) and formula (7):

[0110]

[0111] The random walk coefficient is specifically shown in formula (8) to formula (12):

[0112]

[0113] x m =ρ m ×cos(θ m ) (10)

[0114] y m =ρ m ×sin(θ m ) (11)

[0115] z m =ρ m ×θ m (12)

[0116] It should be noted that when m = 0, the energy value is 0. When m=0, That is the position coordinates of the initial elite water flow point; The selection rule is: in the mth iteration, the energy value E in the initial population set m The position coordinate X of the minimum water flow point m That is When m-1=-1 or 0, t m,m-1 is 0 and equal

[0117] 4. Obtain the optimal water flow path.

[0118] Through the path planning principle, a water flow path with the lowest energy consumption is selected from all water flow paths. This water flow path is the optimal water flow path from the starting point to the end point. The specific contents are as follows:

[0119] 4.1. Use the path planning principle to calculate the total energy value of each water flow path. The calculation formula is as follows:

[0120]

[0121] 4.2. Select the water flow path corresponding to the minimum total energy value. This water flow path is the optimal water flow path from the starting point to the end point.

[0122] The corresponding optimal water flow path can be obtained through the above analysis, so the present invention has the following advantages:

[0123] Accurate spatial positioning: First, by constructing a three-dimensional spatial model containing the coordinates of water supply points and farmland locations, the basic data for planning is ensured to be highly accurate. The three-dimensional model not only reflects the two-dimensional coordinates of the geographical location, but also incorporates elevation information, which is crucial for the accurate calculation of water resource allocation and flow paths, especially in areas with complex terrain and significant elevation differences.

[0124] Clear definition of starting point and end point: Based on the 3D model, the water supply point is clearly designated as the starting point, and the specific water flow point selection is refined, and the farmland that needs water supply is accurately located as the end point. This detailed definition of the starting point and end point provides a clear goal and direction for the subsequent water flow path planning, reducing the ambiguity and uncertainty in the planning process.

[0125] Multiple path generation and comparison: By allowing each water flow point to move from the starting point to the end point, multiple possible water flow paths are formed. This step greatly enriches the diversity of planning. Multi-path generation not only reflects the flexibility of planning, but also provides a wealth of candidate solutions for subsequent selection of the optimal path. This multi-path generated based on the actual geographical environment and constraints helps to discover potential better solutions.

[0126] Scientific path selection principle: Among multiple paths, the optimal water flow path is selected through path planning principles. The "path planning principles" here may include but are not limited to factors such as the shortest path length, the lowest energy consumption, and the smallest impact on the environment. This selection process based on scientific principles ensures that the final water flow path can reach the optimal or near-optimal state under multiple considerations, thereby improving the accuracy and practicality of the planning.

[0127] The coherence and systematicness of the overall process: From building a three-dimensional spatial model to selecting water flow points, and then to generating and optimizing water flow paths, the entire planning process has clear logic and compact steps, forming a coherent and systematic planning system. This systematic planning method helps to reduce omissions and errors in the planning process and improve the accuracy and reliability of planning results.

[0128] like Figure 2 As shown, first determine the farmland topography, irrigation water pipelines, pump configuration, valve node configuration, and farmland crop planting conditions in the region. The irrigation water pipeline consists of multiple pipelines forming a vertical and horizontal, multi-cross pipeline network. The pipeline can flow through each field and is equipped with corresponding intelligent pipeline water valves, which control the water inflow and water inflow of the farmland in blocks.

[0129] like Figure 2 As shown, the farmland irrigation system has a reservoir O1 that delivers water to the water pipeline, and the water valves are A1, A2, A3, ..., A 14 They correspond to farmland 1, farmland 2, ..., farmland 14. The farmland as a whole is composed of high-lying parts and low-lying parts. Water is transported from low-lying to high-lying parts through water pumps B1, B2, B3, ..., B6. Under different water demand, the irrigation system will issue different instructions to control the water valve to adjust the reasonable use of water resources. The actual water flow path is optimized and controlled by the intelligent optimization scheduling irrigation system.

[0130] In summary, the entire planning process of the present invention has shown many significant advantages in terms of planning accuracy, including precise spatial positioning, clear definition of starting and ending points, generation and comparison of multiple paths, scientific path selection principles, and the coherence and systematicness of the overall process. These advantages together ensure the accuracy and practicality of the planning results.

[0131] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for planning a farmland irrigation water flow path, characterized in that: The planning steps include: S1. Construct a three-dimensional spatial model including the location coordinates of the water supply points in the area and the location coordinates of all farmlands; S2, taking the water supply point in the three-dimensional space model as the starting point, and selecting a number of water flow points at the starting point; S3, taking the farmland that needs water supply in the three-dimensional space model as the end point, making each water flow point move from the starting point to the end point, and each forming a water flow path; S4. Select a water flow path with the lowest energy consumption from all water flow paths through the path planning principle. This water flow path is the optimal water flow path from the starting point to the end point.

2. A method for planning a farmland irrigation water flow path according to claim 1, characterized in that: The process of selecting the water flow point in step S2 is as follows: S21, defining the locations outside the water supply points and all farmlands in the three-dimensional space model as the value space of the water flow points; S22, based on the value space, selecting a set number of spatial points in the three-dimensional space model, and calculating the position coordinates of each spatial point using a position coordinate calculation formula; S23, taking the water supply point in the three-dimensional space model as the starting point, and setting a three-dimensional neighborhood with the water supply point as the center point; S24. Select the spatial points that fall into the three-dimensional neighborhood of step S23 from all the spatial points in step S22, and record these spatial points as water flow points. At the same time, mark the position coordinates of these spatial points as the initial position coordinates of the corresponding water flow points, and store the water flow points and their corresponding initial position coordinates in the initial population set.

3. A method for planning a farmland irrigation water flow path according to claim 2, characterized in that: The position coordinate calculation formula is as follows: In the formula, Represents the initial position coordinates of the nth spatial point in the value space, the superscript 0 represents the initial state, n∈[1,N], where N represents the total number of spatial points; Indicates the value of the initial position coordinates of the nth spatial point in the value space in the first dimension; Indicates the value of the initial position coordinates of the nth spatial point in the value space in the jth dimension; Indicates the value of the initial position coordinates of the nth spatial point in the value space in the Jth dimension; Indicates the minimum value of the value space in the jth dimension; It represents the maximum value of the value space in the jth dimension; Tent(i,j) represents the chaotic random number of the nth spatial point in the value space in the jth dimension; Tent(n,j-1) represents the chaotic random number of the nth spatial point in the value space in the j-1th dimension, and when j-1=1, Tent(n,j-1) is a random number in [0,1]; δ represents a random number in [0,1].

4. A method for planning a farmland irrigation water flow path according to claim 3, characterized in that: The process of forming the water flow path in step S3 is as follows: S31, selecting the water flow point closest to the water supply point in the initial population set, and recording the water flow point as the initial elite water flow point; S32, determining the farmland that needs water supply in the three-dimensional space model, and taking the farmland as the end point, connecting the starting point and the end point, and providing a direction for updating the position of the water flow point; S33, the initial position coordinates of the initial elite water flow point and the water flow points in the initial population set are simultaneously brought into the water flow point position coordinate update formula as initial values, and the end point is reached through gradual iterative updates, and the path traversed by each water flow point constitutes the water flow path of the water flow point.

5. A method for planning a farmland irrigation water flow path according to claim 4, characterized in that: The formula for updating the coordinates of the water flow point position is as follows: In the formula, represents the position coordinates of the i-th water flow point in the initial population set in the m+1th iteration, m∈[0,M], M represents the total number of iterations, i∈[1,N]; represents the position coordinates of the i-th water flow point in the initial population set in the m-th iteration; Levy(d) represents the flight distribution function; x m ,y m and z m Both represent random parameters in the mth iteration; Represents the energy value of the i-th water flow point in the initial population set in the m+1th iteration; represents the energy value of the i-th water flow point in the initial population set in the m-th iteration, and when m=0, the energy value is 0; rand(0,1) represents a random number in [0,1]; β m represents the random walk coefficient; else represents the meaning of mathematical logic or; represents the position coordinates of the elite water flow points of all water flow points in the initial population set in the mth iteration, and when m = 0, That is the position coordinates of the initial elite water flow point; The selection rule is: in the mth iteration, the energy value E in the initial population set m The position coordinate X of the minimum water flow point m That is 6. A method for planning a farmland irrigation water flow path according to claim 5, characterized in that: The energy value is calculated as follows: Where η1 represents the time weighting factor, η2 represents the energy consumption weighting factor, and η1+η2=1; t m,m-1 Indicates that the i-th water flow point in the initial population set is Move to The time spent, represents the position coordinates of the i-th water flow point in the initial population set in the m-1th iteration; when m-1=-1 or 0, t m,m-1 is 0 and equal represents the initial position coordinates of the i-th water flow point in the initial population set; σ represents the magnitude parameter; R i represents the number of lift pumps used to raise the water level on the path that the i-th water flow point in the initial population set has passed; Indicates that the i-th water flow point in the initial population set is When , the torque of the rth lift pump on the path it has passed; Indicates that the i-th water flow point in the initial population set is When , the rotational angular acceleration of the rth lift pump on the path it has passed; express and The absolute value of the product; dt represents the differential with respect to time.

7. A method for planning a farmland irrigation water flow path according to claim 6, characterized in that: The flight distribution function is calculated as follows: Where w and k both represent random numbers in [0,1]; λ represents a random number in [0,2]; Γ(·) represents the gamma function.

8. A method for planning a farmland irrigation water flow path according to claim 7, characterized in that: The random walk coefficient is calculated as follows: x m =ρ m ×cos(θ m ); y m =ρ m ×sin(θ m ); With m =ρ m ×θ m ; In the formula, e represents a natural constant; ρ m represents the length parameter in the mth iteration; θ m Represents a random number in [0,2π] in the mth iteration.

9. A method for planning a farmland irrigation water flow path according to claim 8, characterized in that: The process of selecting the optimal water flow path in step S4 is as follows: S41. Calculate the total energy value of each water flow path using the path planning principle. The calculation formula is as follows: In the formula, E i Represents the total energy value of the i-th water flow point in the initial population set during the entire iteration process; S42, selecting a water flow path corresponding to the minimum total energy value, which is the optimal water flow path from the starting point to the end point.

10. A method for planning a farmland irrigation water flow path according to claim 9, characterized in that: The process of establishing the three-dimensional space model in step S1 is as follows: S11. Identify the geographical area to be scanned, including water supply points and all farmland areas; S12, scanning the geographic area using a laser radar and saving the scan data; S13, converting the collected scanning data into point cloud data, and using a registration algorithm to convert the point cloud data into a complete three-dimensional space model.

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