A three-dimensional path planning method for pipelines in sloping farmland based on soil moisture risk

Abstract

The present invention provides a three-dimensional path planning method for sloping farmland pipelines based on soil moisture risk, and relates to the technical field of pipeline path planning. The present invention first obtains digital elevation model data and soil moisture sample data of the target study area. By measuring the soil moisture samples, soil moisture and hydraulic parameter data are obtained, including volumetric water content, field water holding capacity and wilting coefficient. The soil moisture risk is predicted using the sequential Gaussian simulation method, and an evaluation grid map is generated. The elevation model data and the moisture risk assessment map are preprocessed and unified into a three-dimensional grid and coordinate system. The pipeline route is planned using the improved A* path search model, and the drainage network layout is optimized to reduce the impact of runoff on soil and crops, and improve the efficiency of water resource utilization and the effect of soil and water conservation in farmland.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline path planning, and in particular to a three-dimensional path planning method for sloping farmland pipelines based on soil moisture risk. Background Art

[0002] In agricultural production, water management of sloping cultivated land is the key to improving farmland utilization efficiency and reducing environmental impact. Especially in the black soil region of Northeast China, due to the complexity of the terrain and the heavy soil, the existence of slope runoff during the rainy season not only affects crop growth, but also aggravates soil erosion and soil loss. The soil moisture of sloping cultivated land shows significant spatial heterogeneity, and its moisture distribution is affected by many factors, such as topography, soil type and rainfall intensity. Therefore, accurate moisture risk assessment and effective drainage system planning are essential for the continuous management and maintenance of sloping cultivated land. However, there are multiple problems in the application of existing pipeline planning methods in sloping cultivated land. Traditional pipeline planning is mainly aimed at urban and industrial use, and less consideration is given to the special needs of agricultural land. The conditions of the application scenarios of the two are very different. These methods pay more attention to factors such as pipeline cost, utilization rate and flow, but ignore the spatial variation characteristics of soil moisture in cultivated land planning, resulting in low efficiency of drainage systems in practical applications, and unable to provide a solution that considers both efficiency and agricultural ecological requirements.

[0003] In actual application, the A* algorithm, as an efficient path search method, has demonstrated its practical value in the drainage network planning of sloping farmland, providing new possibilities in this field. This algorithm has been widely used in many fields such as robot path search and drone flight planning, and is recognized for its high efficiency and high accuracy. It predicts the best path from any node to the target point through heuristic evaluation, especially when dealing with complex and dynamically changing terrains. Compared with traditional algorithms such as Best-first search and Dijkstra, the A* algorithm not only has better execution efficiency, but also has lower resource consumption. It is especially suitable for complex environments that require the integration of multiple weight factors (such as soil moisture risk, terrain obstacles, etc.). Compared with intelligent algorithms such as genetic algorithms and ant colony algorithms, a significant advantage of the A* algorithm is that it is predictable and intuitive, and can guarantee the optimal solution under the appropriate heuristic function.

[0004] In summary, the path planning algorithm currently used in sloping farmland is still imperfect and cannot meet the needs of managing the moisture of farmland in actual production. How to obtain soil moisture information of farmland, better lay out pipelines in sloping farmland, and realize the overall pipeline network layout in the region is an urgent problem to be solved. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a three-dimensional path planning method for slope farmland pipelines based on soil moisture risk, which can optimize the layout of slope farmland pipelines on a grid map, achieve effective management of soil moisture migration, and thus reduce the adverse effects of slope runoff on soil and crops. Through this innovative path planning, water resources can be controlled more accurately and the soil and water conservation effect of farmland can be improved.

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

[0007] A three-dimensional path planning method for pipelines on sloping farmland based on soil moisture risk, comprising:

[0008] Obtain digital elevation model data of the target study area;

[0009] Collect soil moisture samples of the target study area, and perform measurements based on the soil moisture samples to obtain soil moisture data and hydraulic parameter data of the target study area as a whole; the soil moisture data and hydraulic parameter data of the target study area as a whole include volumetric water content data of soil points, field water holding capacity and wilting coefficient of the study area as a whole;

[0010] Based on the soil moisture data and the hydraulic parameter data of the entire target study area, a sequential Gaussian simulation method is used to predict the soil moisture risk of the target study area to obtain a soil moisture risk assessment grid map;

[0011] Preprocessing the digital elevation model data and the soil moisture risk assessment grid map to unify them into a defined three-dimensional grid and coordinate system to obtain aligned data;

[0012] Inputting the aligned data as weights into a preset A* model to construct an improved A* path search model suitable for sloping farmland;

[0013] The locations of the water inlet and outlet planned on the cultivated land are used as the starting point and the end point of the model to obtain the point coordinates and unify them into the defined three-dimensional grid and coordinate system;

[0014] Input the point coordinates into the improved A* path search model to obtain the path planning point set between the locations and construct the drainage network planning;

[0015] The drainage network planning is optimized to obtain the final drainage network layout.

[0016] Preferably, obtaining digital elevation model data of the target study area includes:

[0017] Oblique photogrammetry using drones to obtain a dataset of watershed images in a crop-free state;

[0018] Using RTK, multiple control points were set within the catchment area to calibrate the data and ensure data accuracy;

[0019] The three-dimensional real-scene modeling software is used to construct an oblique photography three-dimensional model to obtain the digital elevation model data; the image resolution of the digital elevation model data is 0.05m, and the digital elevation model is finally downscaled to 0.5m.

[0020] Preferably, soil moisture samples of the target study area are collected, and measurements are performed based on the soil moisture samples to obtain soil moisture data and hydraulic parameter data of the target study area as a whole, including:

[0021] Using a grid sampling method, the catchment area is divided into a plurality of grids, and the sampling positions are adjusted according to preset terrain change characteristics to obtain a plurality of sampling points;

[0022] In a preset season of the watershed, collecting soil ring samples of the watershed based on the sampling points;

[0023] The soil mass moisture content of the soil ring knife sample is determined by the drying method, and the soil bulk density is calculated at the same time to obtain the volumetric moisture content data of the soil; the field water holding capacity and the wilting coefficient are determined by the ring knife method and the laboratory method respectively.

[0024] Preferably, based on the soil moisture data and the hydraulic parameter data of the entire target study area, the soil moisture risk of the target study area is predicted using a sequential Gaussian simulation method to obtain a soil moisture risk assessment grid map, including:

[0025] The soil moisture data and the hydraulic parameter data of the target study area as a whole were transformed by Box-Cox to ensure that the data conformed to the normal distribution;

[0026] Simple Kriging was used to fit the soil moisture data after normal transformation and the hydraulic parameter data of the target study area as a whole to obtain the optimal soil moisture semi-variation model parameters.

[0027] Based on the optimal soil moisture semi-variation model parameters, the field water holding capacity of the catchment area is taken as the upper limit of risk, and the sequential Gaussian simulation method is used to obtain the risk probability of soil moisture excess; the formula for the risk probability of soil moisture excess is as follows: Wherein, P(A) is the probability of water excess when the soil moisture at the point exceeds the upper threshold, and its value range is between 0 and 1. The larger the value, the greater the risk of water excess; SM(x0) is the soil moisture value at point x0, c1 is the field capacity, n(SM(x0)>c1) is the number of times exceeding the threshold c1 in all simulations, and L is the number of sequential Gaussian simulations;

[0028] Taking the wilting coefficient as the lower limit of soil moisture, the risk probability of realizing water deficit is obtained by using the method of sequential Gaussian simulation; the formula for the risk probability of water deficit is: Wherein, P(B) is the probability of water deficit when the soil moisture at this point is lower than the lower threshold, and its value range is between 0 and 1. The larger the value, the greater the risk of water deficit; SM(x0) is the soil moisture value at point x0, c2 is the wilting coefficient, and n(SM(x0)<c2) is the number of times less than the threshold c2 in all simulations;

[0029] Calculate the risk assessment value of soil moisture according to the risk probability of soil moisture excess and the risk probability of water deficit; the formula for the risk assessment value of soil moisture is: P(R) = (P(A)+(1 - P(B))) / 2; wherein, P(R) is the risk assessment value of soil moisture at a certain point in space, and the value range of P(R) is between 0 and 1. The closer it is to 1, the greater the risk of soil moisture excess, and the closer it is to 0, the greater the risk of water deficit;

[0030] Construct the soil moisture risk assessment raster map according to the risk assessment value of the soil moisture.

[0031] Preferably, use the aligned data as weights and input them into a preset A* model to construct an improved A* path search model applicable to sloping farmland, including:

[0032] Obtain the heuristic function of the A* model; the formula of the heuristic function is: minf(n) = g(n)+h(n); wherein, f(n) is the cost function from the starting point to the ending point, g(n) is the actual cost from the starting point to n, and h(n) is the estimated cost from node n to the target;

[0033] During the application process of sloping farmland, obtain the position of the coordinate point based on the three-dimensional planning of the terrain surface; the formula for the position of the coordinate point: (x i , y i , z i ) ∈ S DEM ; wherein x i , y i , z i are the three-dimensional coordinates of node n, and S DEM is the data point set on the surface of the digital elevation model data;

[0034] The actual cost g(n) is calculated based on the spatial distribution characteristics of soil moisture; the calculation formula of the actual cost g(n) is: In the formula, x i+1 ,y i+1 , z i+1 is the three-dimensional coordinate of the n+1th node, b is the harmonic number, w i+1 is the risk assessment value of soil moisture at node i+1;

[0035] Calculate the estimated cost h(n); the formula for the estimated cost h(n) is: In the formula, x D ,y D 、z D is the coordinate of the target point, and a is the horizontal distance between the two points;

[0036] By bringing the calculated actual cost g(n) and the estimated cost h(n) into the heuristic function of the A* model, an updated heuristic function is obtained, and the improved A* path search model is determined according to the updated heuristic function.

[0037] Preferably, optimizing the drainage network planning to obtain the final drainage network layout includes:

[0038] Applying Douglas-Peuker algorithm to the drainage network point set data generated by the drainage network planning to perform point thinning to obtain a point thinning result;

[0039] The point set conversion method is applied to the point thinning result, and the line is closed and cut to optimize the network and obtain the final drainage network layout.

[0040] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0041] The present invention provides a three-dimensional path planning method for sloping farmland pipelines based on soil moisture risk, comprising: acquiring digital elevation model data of a target study area; collecting soil moisture samples of the target study area, and measuring according to the soil moisture samples to obtain soil moisture data and hydraulic parameter data of the target study area as a whole; the soil moisture data and the hydraulic parameter data of the target study area as a whole include volumetric water content data of soil at the point, and the average field water holding capacity and wilting coefficient of the region as a whole; based on the soil moisture data and the hydraulic parameter data of the target study area as a whole, using a sequential Gaussian simulation method to predict the soil moisture risk of the target study area, and obtaining soil moisture. Risk assessment grid map; preprocessing the digital elevation model data and the soil moisture risk assessment grid map to unify them into the defined three-dimensional grid and coordinate system to obtain aligned data; inputting the aligned data into the preset A* model as weights to construct an improved A* path search model suitable for sloping farmland; using the planned water inlet and outlet locations on the farmland as the model start and end points to obtain point coordinates, which are unified into the defined three-dimensional grid and coordinate system; inputting the point coordinates into the improved A* path search model to obtain the path planning point set between positioning points, and construct drainage network planning; optimizing according to the drainage network planning to obtain the final drainage network layout. The present invention can optimize the layout of sloping farmland pipelines on the grid map, realize the effective management of soil moisture migration, and thus reduce the adverse effects of slope runoff on soil and crops. Through this innovative path planning, water resources can be controlled more accurately and the soil and water conservation effect of farmland can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of a three-dimensional path planning process provided by an embodiment of the present invention;

[0045] Figure 3 A soil moisture sampling point distribution map of a catchment area provided by an embodiment of the present invention;

[0046] Figure 4 The DEM and soil moisture risk assessment results of a watershed area provided by the embodiment of the present invention;

[0047] Figure 5 The layout of water inlet and outlet points in a water catchment area provided by an embodiment of the present invention;

[0048] Figure 6 The conventional three-dimensional A* algorithm and the improved A* algorithm path planning results provided in the embodiments of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a three-dimensional path planning method for sloping farmland pipelines based on soil moisture risk, comprising:

[0052] Step 100: Acquire digital elevation model data of the target study area;

[0053] Step 200: collecting soil moisture samples of the target study area, and measuring the soil moisture samples to obtain soil moisture data and hydraulic parameter data of the target study area as a whole; the soil moisture data and hydraulic parameter data of the target study area as a whole include volumetric water content data of the soil, field water holding capacity and wilting coefficient;

[0054] Step 300: Based on the soil moisture data and the hydraulic parameter data of the entire target study area, the soil moisture risk of the target study area is predicted using the sequential Gaussian simulation method to obtain a soil moisture risk assessment grid map;

[0055] Step 400: preprocessing the digital elevation model data and the soil moisture risk assessment grid map to unify them into a defined three-dimensional grid and coordinate system to obtain aligned data;

[0056] Step 500: inputting the aligned data as weights into a preset A* model to construct an improved A* path search model suitable for sloping farmland;

[0057] Step 600: The locations of the water inlet and outlet planned on the farmland are used as the model start point and end point to obtain point coordinates, which are unified into the defined three-dimensional grid and coordinate system;

[0058] Step 700: input the point coordinates into the improved A* path search model, obtain the path planning point set between the locations, and construct the drainage network planning;

[0059] Step 800: Optimize the drainage network planning to obtain the final drainage network layout.

[0060] like Figure 2 As shown, the present invention provides a three-dimensional layout method of concealed pipes based on soil moisture risk of sloping farmland, and the evaluation and layout method includes the following steps:

[0061] Step 1: Use drone oblique photography technology to obtain regional DEM data in the catchment area. The specific steps are as follows:

[0062] 1.1: Use DJI Phantom 4Pro drone for oblique photogrammetry. In cloudless weather with sufficient light, the flight altitude was 100m, and the flight number was 5 times to obtain the watershed image dataset without crop growth. At the same time, 11 control points were set in the watershed using RTK to calibrate the data and ensure the accuracy of the data.

[0063] 1.2: Use ContextCapture (smart3D) software to construct a three-dimensional model of oblique photography to obtain high-precision DEM data of the catchment area with an image resolution of 0.05m. To avoid images with slight changes in terrain, the digital elevation model is downscaled to 0.5m.

[0064] Step 2: Collect soil samples in the catchment area and measure the soil volumetric water content and soil hydraulic parameters. The specific steps are as follows:

[0065] 2.1: Using the grid sampling method, the study area was divided into 60×60m grids, and the sampling locations were appropriately adjusted to meet the terrain change characteristics. Finally, a total of 96 sampling points were arranged, such as Figure 3 shown.

[0066] 2.2: In the season of frequent runoff in the catchment area (September), collect soil ring knife samples with two replicates and a sampling depth of 0-15cm. Make sure there is no rainfall three days before sampling to avoid the influence of gravity water.

[0067] 2.3: The mass water content of soil is measured by drying method, and the bulk density of soil is calculated at the same time to obtain the volumetric water content data of soil. The field water holding capacity and wilting coefficient are measured by ring knife method and laboratory method respectively.

[0068] Step 3: Using the spatial distribution data of soil moisture, set the thresholds of water deficit and surplus, and draw the soil moisture risk assessment (SMRA) map based on the sequential Gaussian simulation method. The specific steps are as follows:

[0069] 3.1: Preprocess the soil moisture data and the hydraulic parameter data of the target study area as a whole, using the Box-Cox normal transformation to ensure that the data conforms to the normal distribution. The inverse transformation is required for the results of the sequential Gaussian.

[0070] 3.2: Simple Kriging is used to fit the soil moisture data and the hydraulic parameter data of the target study area as a whole. Referring to previous literature, the spherical model is the best model for the soil moisture data and the hydraulic parameter data of the target study area as a whole, and the optimal soil moisture semi-variation model parameters can be obtained.

[0071] 3.3: Field water holding capacity refers to the maximum amount of water that the soil can retain after losing free water through gravity under natural conditions. It is the maximum water content of the soil that can be used by plants. Based on the model parameters obtained above, the field water holding capacity of the catchment area is taken as the upper limit of risk. The sequential Gaussian simulation method can be used to obtain the risk probability of excess soil water. The specific formula is as follows:

[0072]

[0073] Where P(A) is the probability of excess water when the soil moisture at this point exceeds the upper threshold, and the value range is between 0 and 1. The larger the value, the greater the risk of excess water. SM(x0) is the soil moisture value at point x0, c1 is the field water holding capacity, n(SM(x0)>c1) is the number of times the threshold c1 is exceeded in all simulations, and L is the number of SGS simulations. In this catchment area, c1 is 34.14% and L is 500 times.

[0074] 3.4: The wilting coefficient refers to the water content of the soil when the crop permanently wilts under drought conditions. Using the same method as 3.3, the wilting coefficient is used as the lower limit of soil moisture, and it is considered that there is a risk of water deficit when it is lower than this value. The risk prediction of water deficit can be achieved using sequential Gaussian simulation. The specific formula is as follows:

[0075]

[0076] In the formula, P(B) is the probability of water deficit occurring when the soil moisture at a certain point is lower than the lower threshold value. Its value range is between 0 and 1. The larger the value, the greater the risk of water deficit. SM(x0) is the soil moisture value at point x0, c2 is the wilting coefficient, n(SM(x0)<c2) is the number of times less than the threshold c2 in all simulations, and L is the number of SGS simulations. In this catchment area, c2 is 26.15% and L is 500 times.

[0077] 3.5: Based on the results of 3.3 and 3.4, a method for comprehensively evaluating soil moisture risk is proposed. The specific formula is as follows:

[0078] P(R) = (P(A)+(1 - P(B))) / 2

[0079] In the formula, P(R) is the risk assessment value of soil moisture at a certain point in space. The value range of P(R) is between 0 and 1. The closer it is to 1, the greater the risk of soil moisture excess, and the closer it is to 0, the greater the risk of soil moisture deficit. The image resolution is 0.5m, and the results are as Figure 4 shown.

[0080] Step 4: Preprocess the DEM data obtained in Step 1 and the water risk assessment data obtained in Step 3, unify them to the same geographic coordinate system, and at the same time divide both into grids with the same 0.5m resolution, so that the pixels are aligned for subsequent calculations.

[0081] Step 5: Based on the above results, construct an improved A* path search algorithm based on DEM and SMRA. The specific steps are as follows:

[0082] 5.1: The core of the A* algorithm lies in the design of the heuristic function f(n). This function consists of the actual cost g(n) and the estimated cost h(n), aiming to minimize f(n), which directly affects the search efficiency and accuracy. The heuristic function is as follows:

[0083] minf(n) = g(n)+h(n)

[0084] In the formula, f(n) is the cost function from the starting point to the ending point, g(n) is the actual cost from the starting point to n, and h(n) is the estimated cost from node n to the target.

[0085] 5.2: Different from the three-dimensional planning of drones that can achieve movement in six directions in space. In the application process of sloping farmland, the three-dimensional planning based on the terrain surface is restricted by DEM. In addition to the eight directions, the three-dimensional A* algorithm needs to conform to the characteristics of terrain changes in terms of elevation changes. Therefore, the coordinate point positions are as follows:

[0086] (x i ,y i ,zi )∈S DEM

[0087] Where x i ,y i , z i is the three-dimensional coordinate of node n, S DEM is the data point set of the DEM surface.

[0088] 5.3: In order to follow the spatial distribution characteristics of soil moisture, the new actual cost g(n) needs to superimpose the risk assessment value on the three-dimensional distance between two points in space as a weight. The calculation formula is as follows:

[0089]

[0090] Where x i ,y i , z i is the three-dimensional coordinate of node n, x i+1 ,y i+1 , z i+1 is the three-dimensional coordinate of the n+1th node, b is the harmonic number, w i+1 is the soil moisture risk assessment value at node n+1. Based on the characteristics of soil moisture risk, b is set to 0.5.

[0091] 5.4: At the same time, in order to reduce the breaking points, the weight of the elevation change is added. The specific formula of the new estimated cost function h(n) is as follows:

[0092]

[0093] Where x i+1 ,y i+1 , z i+1 is the three-dimensional coordinate of the n+1 node, x D ,y D , z D are the coordinates of the target point, and a is the horizontal distance between the two points.

[0094] Step 6: In order to achieve overall control of soil moisture on sloping farmland, a grid network system needs to be laid out on it. Plan appropriate water inlet (slope top) and outlet (drainage ditch) locations on the farmland as the starting and end points of the path planning model, obtain the point coordinates, and unify them into the same coordinate system as step 4, such as Figure 5 shown.

[0095] Step 7: Input the point coordinates into the improved A* model to obtain the path planning point set from the inlet to the outlet, and construct the drainage network planning of the area.

[0096] Step 8: Optimize the obtained drainage network to obtain the drainage network layout of the final area.

[0097] 8.1: Apply the Douglas-Peuker algorithm to the generated drainage network point set data to perform point thinning. The algorithm first connects the ends of the polyline to form a straight line, and then determines the selection of points by calculating the distance from the polyline point to the straight line. In this route optimization, the distance is set to 2.

[0098] 8.2: Convert the result point set of 8.1 into lines, then close and trim the lines, optimize the network, and obtain the final drainage network plan of the catchment area.

[0099] At the same time, the three-dimensional A* path planning algorithm based only on DEM and the improved A* algorithm were compared. The results are as follows Figure 6 As shown in the figure. The improved A* algorithm plans a path of 6393m, while the conventional A* algorithm plans a path of 7315m, which is shorter, less expensive, and more in line with the actual situation. At the same time, the average soil moisture content on the path is calculated. The improved A* algorithm (0.38) is much higher than the moisture content before the improvement (0.36), which can achieve better control of soil moisture and runoff.

[0100] The beneficial effects of the present invention are as follows:

[0101] The present invention uses a new risk assessment method to optimize the layout of sloping farmland pipelines on a grid map, achieving effective management of soil moisture migration, thereby reducing the adverse effects of slope runoff on soil and crops. Through this innovative path planning, water resources can be controlled more accurately and the soil and water conservation effect of farmland can be improved.

[0102] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0103] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A three-dimensional path planning method for sloping farmland pipelines based on soil moisture risk, characterized in that: include: Obtain digital elevation model data of the target study area; Collect soil moisture samples of the target study area, and perform measurements based on the soil moisture samples to obtain soil moisture data and hydraulic parameter data of the target study area as a whole; the soil moisture data and hydraulic parameter data of the target study area as a whole include volumetric water content data at soil points, field water holding capacity, and wilting coefficient; Based on the soil moisture data and the hydraulic parameter data of the entire target study area, a sequential Gaussian simulation method is used to predict the soil moisture risk of the target study area to obtain a soil moisture risk assessment grid map; Preprocessing the digital elevation model data and the soil moisture risk assessment grid map to unify them into a defined three-dimensional grid and coordinate system to obtain aligned data; Inputting the aligned data as weights into a preset A* model to construct an improved A* path search model suitable for sloping farmland; The locations of the water inlet and outlet planned on the cultivated land are used as the starting point and the end point of the model to obtain the point coordinates and unify them into the defined three-dimensional grid and coordinate system; Input the point coordinates into the improved A* path search model to obtain the path planning point set between the locations and construct the drainage network planning; The drainage network planning is optimized to obtain the final drainage network layout.

2. The three-dimensional path planning method for sloping farmland pipeline based on soil moisture risk according to claim 1 is characterized in that: Obtain digital elevation model data for the target study area, including: Oblique photogrammetry using drones to obtain a dataset of watershed images without crops; Using RTK, multiple control points were set within the catchment area to calibrate the data and ensure data accuracy; The three-dimensional real-scene modeling software is used to construct the oblique photography three-dimensional model to obtain the digital elevation model data; the image resolution of the digital elevation model data is 0.05m. In order to avoid images of slight changes in terrain, the digital elevation model is downscaled to 0.5m.

3. The three-dimensional path planning method for sloping farmland pipeline based on soil moisture risk according to claim 2 is characterized in that: Collect soil moisture samples of the target study area, and measure based on the soil moisture samples to obtain soil moisture data and hydraulic parameter data of the target study area as a whole, including: Using a grid sampling method, the catchment area is divided into a plurality of grids, and the sampling positions are adjusted according to preset terrain change characteristics to obtain a plurality of sampling points; In a preset season of the watershed, collecting soil ring samples of the watershed based on the sampling points; The soil mass moisture content of the soil ring knife sample is determined by the drying method, and the soil bulk density is calculated at the same time to obtain the volumetric moisture content data of the soil; the field water holding capacity and the wilting coefficient are determined by the ring knife method and the laboratory method respectively.

4. The three-dimensional path planning method for sloping farmland pipeline based on soil moisture risk according to claim 2 is characterized in that: Based on the soil moisture data and the hydraulic parameter data of the target study area as a whole, the soil moisture risk of the target study area is predicted using the sequential Gaussian simulation method to obtain a soil moisture risk assessment grid map, including: The soil moisture data and the hydraulic parameter data of the target study area as a whole were transformed by Box-Cox to ensure that the data conformed to the normal distribution; Simple Kriging was used to fit the soil moisture data after normal transformation and the hydraulic parameter data of the target study area as a whole to obtain the optimal soil moisture semi-variation model parameters. Based on the optimal soil moisture semi-variation model parameters, the field water holding capacity of the catchment area is taken as the upper limit of risk, and the sequential Gaussian simulation method is used to obtain the risk probability of soil moisture excess; the formula for the risk probability of soil moisture excess is as follows: Where P(A) is the probability of excess water when the soil moisture at the point exceeds the upper threshold, and the value range is between 0 and 1. The larger the value, the greater the risk of excess water. SM(x0) is the soil moisture value at point x0, c1 is the field water holding capacity, n(SM(x0)>c1) is the number of times the threshold c1 is exceeded in all simulations, and L is the number of sequential Gaussian simulations. Taking the wilting coefficient as the lower limit of soil moisture, the sequential Gaussian simulation method is used to obtain the risk probability of realizing water deficit; the formula for the risk probability of water deficit is as follows: In the formula, P(B) is the probability of water deficit when the soil moisture at this point is lower than the lower limit threshold, and its value range is between 0 and 1. The larger the value, the greater the risk of water deficit; SM(x0) is the soil moisture value at point x0, c2 is the wilting coefficient, and n(SM(x0)<c2) is the number of times less than the threshold c2 in all simulations; The risk assessment value of soil moisture is calculated according to the risk probability of excess soil moisture and the risk probability of moisture deficit; the formula of the risk assessment value of soil moisture is: P(R)=(P(A)+(1-P(B))) / 2; wherein P(R) is the risk assessment value of soil moisture at a certain point in space, and the value range of P(R) is between 0 and 1, and the closer to 1, the greater the risk of excess soil moisture, and the closer to 0, the greater the risk of soil moisture deficit; The soil moisture risk assessment grid map is constructed according to the risk assessment value of the soil moisture, and the resolution of the grid image is the same as that of the digital elevation model, which is 0.5 m.

5. The three-dimensional path planning method for sloping farmland pipeline based on soil moisture risk according to claim 4 is characterized in that: The aligned data is input as weights into a preset A* model to construct an improved A* path search model suitable for sloping farmland, including: Obtain a heuristic function of the A* model; the formula of the heuristic function is: minf(n)=g(n)+h(n); where f(n) is the cost function from the starting point to the end point, g(n) is the actual cost from the starting point to n, and h(n) is the estimated cost from node n to the target; In the process of using sloping farmland, the position of the coordinate point is obtained based on the three-dimensional planning of the terrain surface; the formula for the position of the coordinate point is: (x i ,y i ,z i )∈S DEM ; where x i ,y i 、z i is the three-dimensional coordinate of node n, S DEM A data point set for the surface of the digital elevation model data; The actual cost g(n) is calculated based on the spatial distribution characteristics of soil moisture; the calculation formula of the actual cost g(n) is: In the formula, x i+1 ,y i+1 , z i+1 is the three-dimensional coordinate of the n+1th node, b is the harmonic number, w i+1 is the risk assessment value of soil moisture at node i+1; Calculate the estimated cost h(n); the formula for the estimated cost h(n) is: In the formula, x D ,y D 、z D is the coordinate of the target point, and a is the horizontal distance between the two points; By bringing the calculated actual cost g(n) and the estimated cost h(n) into the heuristic function of the A* model, an updated heuristic function is obtained, and the improved A* path search model is determined according to the updated heuristic function.

6. The method for three-dimensional path planning of sloping farmland pipeline based on soil moisture risk according to claim 4 is characterized in that: Optimize the drainage network planning to obtain the final drainage network layout, including: Applying Douglas-Peuker algorithm to the drainage network point set data generated by the drainage network planning to perform point thinning to obtain a point thinning result; The point set conversion method is applied to the point thinning result, and the line is closed and cut to optimize the network and obtain the final drainage network layout.

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