A micro-terrain intelligent zoning management method based on K-means algorithm

Abstract

The present invention discloses a micro-topography intelligent zoning management method based on K-means algorithm, comprising the following steps: S1, obtaining micro-topography boundary data, and classifying the irrigated crops within the micro-topography boundary; S2, based on the micro-topography coordinate data combined with typical irrigation points, using the Canopy algorithm to determine the number of sub-irrigation areas; S3, using the K-means algorithm to further divide the sub-irrigation areas and determine the location coordinates of the sub-irrigation areas; S4, calculating the irrigation weight and irrigation volume in each sub-irrigation area, and obtaining the total irrigation flow of each sub-irrigation area. The present invention can optimize the irrigation zoning according to the detailed terrain data of different areas, is more suitable for the irrigation conditions of micro-topography crops themselves, improves the irrigation efficiency, and has the advantages of fewer constraints, fast calculation, and accurate results compared to the existing irrigation management methods.

Description

Technical Field

[0001] The present invention relates to the field of micro-terrain irrigation, and in particular to a micro-terrain intelligent zoning management method based on a K-means algorithm. Background Art

[0002] Microtopography refers to the undulating terrain designed by artificially simulating the earth's morphology and its undulating rhythm during the landscape construction process. The ground is undulating but the amplitude of the undulation is small. Microtopography is mainly artificially transformed terrain in garden landscapes and is widely used in park greening, road greening and residential greening. The characteristics of microtopography include micro-elevation in the vertical direction and surface roughness in the horizontal direction, and it also has significant temporal and spatial heterogeneity.

[0003] The irrigation area of ​​micro-topography is large, and artificial flooding is time-consuming and water-consuming; the micro-topography is undulating, and laying micro-irrigation pipes is prone to problems. Traditional irrigation methods are difficult to meet the requirements of micro-topography undulations, diverse plant varieties, and regional intelligent management and control. The current garden intelligent irrigation method is mainly "point-surface", that is, data monitoring of each location point to control the valve switch, ignoring the important step of regional management. Since micro-topography generally plants various types of trees, shrubs, and ground cover plants, the irrigation area of ​​various plants is large and the irrigation water requirements are different, so it is more suitable for the "point-block-surface" regional irrigation management method. Therefore, the present invention proposes a micro-topography intelligent zoning management method based on the K-means algorithm. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a micro-topography intelligent zoning management method based on the K-means algorithm, which has the advantages of being able to intelligently divide irrigation micro-areas according to the actual conditions of the micro-topography, provide the most suitable irrigation system for the micro-topography, improve the utilization efficiency of irrigation water and the maintenance effect of the micro-topography, thereby solving the problems in the background technology.

[0006] (II) Technical solution

[0007] In order to achieve the above advantages of being able to intelligently divide irrigation micro-areas according to the actual conditions of micro-topography, provide the most suitable irrigation system for micro-topography, improve the utilization efficiency of irrigation water and the maintenance effect of micro-topography, the specific technical solutions adopted by the present invention are as follows:

[0008] A micro-terrain intelligent zoning management method based on K-means algorithm, the management method comprises the following steps:

[0009] S1. Obtain micro-topography boundary data and classify the irrigated crops within the micro-topography boundary;

[0010] S2, based on micro-topography coordinate data combined with typical irrigation points, the number of sub-irrigation areas is determined using the Canopy algorithm;

[0011] S3, using K-means algorithm to further divide the sub-irrigation areas and determine the location coordinates of the sub-irrigation areas;

[0012] S4. Calculate the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area.

[0013] Furthermore, the typical irrigation point is determined according to the irrigation crop position of the micro-topography, including the following steps: selecting trees and shrubs as typical irrigation crops, and using the tree position coordinates and shrub centroid coordinates as the typical irrigation point P i , where i represents the i-th typical irrigation point and its value is an integer greater than zero.

[0014] Furthermore, the method of determining the number of sub-irrigation areas by using the Canopy algorithm based on micro-topography coordinate data combined with typical irrigation points includes the following steps:

[0015] S21, taking the position coordinates of a preset number of typical irrigation points as an original data set, and selecting point P1 from the original data set as the first class, called Canopy;

[0016] S22, select point P2 from the original data set, calculate the distance from point P2 to point P1, and if the distance is less than the maximum irrigation distance, add point P2 to the Canopy, otherwise use point P2 as a new Canopy;

[0017] S23, determining whether the distance is less than the average irrigation distance, if not, ignoring it, and if so, deleting point P2 from the original data set;

[0018] S24. Continue to execute S22 and S23 for other typical irrigation points in the original data set until the set is empty, the algorithm ends, and clustering is completed, wherein the total number of Canopies is the value K of the number of sub-irrigation areas.

[0019] Furthermore, the micro-topography coordinate data are boundary coordinates of the micro-topography, and the micro-topography coordinates are provided by GIS or manually input.

[0020] Furthermore, the maximum irrigation distance and the average irrigation distance are two distance thresholds of Canopy, and the maximum irrigation distance is the maximum working distance of the irrigation device under normal working conditions, and the average irrigation distance is the middle number between the maximum working distance and the minimum working distance of the irrigation device under normal working conditions.

[0021] Furthermore, the method of further dividing the sub-irrigation areas by using the K-means algorithm to determine the location coordinates of the sub-irrigation areas includes the following steps:

[0022] S31, establishing an initial partition according to the number K of sub-irrigation areas to obtain K clusters, and randomly selecting K points as initial centroids;

[0023] S32, calculating the distance between each typical irrigation point and each initial centroid one by one, and adding the distance to the nearest cluster to calculate the centroid of each cluster;

[0024] S33, repeatedly executing S32 until the centroid of each cluster does not change within a certain accuracy range or the maximum number of iterations is reached, and then the obtained cluster centroid is used as the center of the sub-irrigation area, that is, the irrigation device placement point.

[0025] Furthermore, the calculation of the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area includes the following steps:

[0026] S41. Establish a three-dimensional rectangular coordinate system on the micro-topography and use (x k ,y k ,h k ) represents a point on the micro-topography surface, where x k represents the horizontal coordinate of the kth sub-irrigation area, y k represents the ordinate of the kth irrigation zone, h k represents the distance between the ground point of the kth irrigation zone and the reference elevation;

[0027] S42. Calculate the irrigation volume of each sub-irrigation area using the following formula:

[0028] V k =S k ·h k ;

[0029] Among them, S k represents the area of ​​the kth irrigation zone;

[0030] S43. Calculate the total irrigation flow of each sub-irrigation area based on the irrigation weight and irrigation volume in each sub-irrigation area. The calculation formula is as follows:

[0031]

[0032] Among them, Q represents the total irrigation flow, w represents the irrigation weight in each sub-irrigation area, and η is the sprinkler water utilization coefficient, which is generally 0.7-0.9.

[0033] Furthermore, the calculation of the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area also includes the following steps:

[0034] The maximum permissible seepage depth is set to H. Seepage exceeding the depth H is regarded as invalid irrigation water due to deep seepage.

[0035] The position at a height H below the lowest point of the micro-topography is used as the reference elevation line, and the infiltration water exceeding this position is regarded as invalid irrigation water.

[0036] Furthermore, each of the sub-irrigation areas is a relatively independent unit, and there is no need to consider the mutual exchange of water between the sub-irrigation areas.

[0037] Furthermore, the irrigation weight w in each sub-irrigation area is set according to the different water requirements of different types of water-demanding crops during their growth cycle.

[0038] (III) Beneficial effects

[0039] Compared with the prior art, the present invention provides a micro-terrain intelligent zoning management method based on the K-means algorithm, which has the following beneficial effects:

[0040] (1) The method of the micro-topography zoning algorithm based on the hybrid clustering of the Canopy algorithm and the K-means algorithm proposed in the present invention can optimize the irrigation zoning according to the detailed terrain data of different regions, is more suitable for the irrigation conditions of micro-topography crops, and improves the irrigation efficiency. Compared with the existing irrigation management method, it has the advantages of fewer constraints, faster calculation and more accurate results.

[0041] (2) The novel micro-topography zoning management method proposed in the present invention can determine the optimal irrigation water volume according to different topography, which can not only meet the irrigation water needs of different crops, but also achieve the purpose of economical water conservation, realize the rational allocation of water resources, improve the efficiency of irrigation management, and provide the best irrigation system for micro-topography.

[0042] (3) The micro-topography data collection method proposed in the present invention has the advantages of high data collection accuracy, high universality, and easy sampling. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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 work.

[0044] Figure 1is a flow chart of a micro-terrain intelligent zoning management method based on a K-means algorithm according to an embodiment of the present invention;

[0045] Figure 2 It is a schematic diagram of the principle of the Canopy algorithm in the micro-terrain intelligent zoning management method based on the K-means algorithm according to an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of the division of sub-irrigation areas in the micro-terrain intelligent zoning management method based on the K-means algorithm according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0048] According to an embodiment of the present invention, a micro-terrain intelligent zoning management method based on a K-means algorithm is provided.

[0049] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1-3 As shown, according to the micro-terrain intelligent zoning management method based on the K-means algorithm according to an embodiment of the present invention, the management method comprises the following steps:

[0050] S1. Obtain micro-topography boundary data and classify the irrigated crops within the micro-topography boundary;

[0051] The micro-topography coordinate data are the boundary coordinates of the micro-topography, in the form of (x, y), and the micro-topography coordinates are provided by GIS or can be manually input.

[0052] S2, based on micro-topography coordinate data combined with typical irrigation points, the number of sub-irrigation areas is determined using the Canopy algorithm;

[0053] The typical irrigation point is determined according to the irrigation crop position of the micro-topography, including the following steps: since the micro-topography can be used to plant trees, shrubs and other plants, trees and shrubs are selected as typical irrigation crops, and the tree position coordinates and shrub centroid coordinates are used as the typical irrigation point P i (x i ,y i ), where i represents the i-th typical irrigation point and its value is an integer greater than zero.

[0054] The Canopy algorithm is used to classify i chaotic irrigation points into k irrigation areas with certain rules. Specifically, the method of determining the number of sub-irrigation areas by using the Canopy algorithm based on micro-topography coordinate data combined with typical irrigation points includes the following steps:

[0055] S21, taking the position coordinates of i typical irrigation points as the original data set, the Canopy algorithm selects a point P1 (x1, y1) from the original data set, and takes P1 as the first class, called Canopy;

[0056] S22, select point P2 (x2, y2) from the original data set, calculate the distance from point P2 to point P1, and if the distance is less than the maximum irrigation distance T1, add point P2 to the Canopy, otherwise use point P2 as a new Canopy;

[0057] S23, determine whether the distance is less than the average irrigation distance T2, if not, ignore it, if so, it means that P2 and the Canopy are close enough, and delete point P2 from the original data set to avoid repeated addition to other Canopies;

[0058] S24. Continue to execute S22 and S23 for other typical irrigation points in the original data set until the set is empty, the algorithm ends, and clustering is completed, wherein the total number of Canopies is the value K of the number of sub-irrigation areas.

[0059] Irrigation distance is an important node for dividing the area and is also an important constraint in the Canopy algorithm. Figure 2 As shown, the maximum irrigation distance T1 and the average irrigation distance T2 are taken as the two distance thresholds of Canopy, and the maximum irrigation distance T1 is the maximum working distance of the irrigation device under normal working conditions, and the average irrigation distance T2 is the middle number between the maximum working distance and the minimum working distance of the irrigation device under normal working conditions.

[0060] S3, using K-means algorithm to further divide the sub-irrigation areas and determine the location coordinates of the sub-irrigation areas;

[0061] To further determine the center of the irrigation area, the number of sub-areas K determined by the Canopy algorithm is used as the initial value of the K-means algorithm to divide the sub-irrigation area. Specifically, the sub-irrigation area is further divided by the K-means algorithm to determine the location coordinates of the sub-irrigation area, including the following steps:

[0062] S31, establishing an initial partition according to the number K of sub-irrigation areas to obtain K clusters, and randomly selecting K points as initial centroids;

[0063] S32, calculating the distance between each typical irrigation point and each initial centroid one by one, and adding the distance to the nearest cluster to calculate the centroid of each cluster;

[0064] S33, repeat S32 until the centroid of each cluster does not change within a certain accuracy range or reaches the maximum number of iterations, and then use the obtained centroid of the cluster as the center of the sub-irrigation area, that is, the irrigation device placement point. For each sub-irrigation area, a sprinkler irrigation device is installed at the center of the area.

[0065] S4. Calculate the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area.

[0066] The calculation of the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area includes the following steps:

[0067] S41. Establish a three-dimensional rectangular coordinate system on the micro-topography and use (x k ,y k ,h k ) represents a point on the micro-topography surface, where x k represents the horizontal coordinate of the kth sub-irrigation area, y k represents the ordinate of the kth irrigation zone, h k represents the distance between the ground point of the kth irrigation zone and the reference elevation;

[0068] S42. Calculate the irrigation volume of each sub-irrigation area using the following formula:

[0069] V k =S k ·h k ;

[0070] Among them, S k represents the area of ​​the kth irrigation zone;

[0071] S43. Calculate the total irrigation flow of each sub-irrigation area based on the irrigation weight and irrigation volume in each sub-irrigation area. The calculation formula is as follows:

[0072]

[0073] Among them, Q represents the total irrigation flow, w represents the irrigation weight in each sub-irrigation area, and η is the sprinkler water utilization coefficient, which is generally 0.7-0.9.

[0074] Specifically, each type of water-demanding crop has different irrigation weights w according to its water demand during its growth period.

[0075] Each sub-irrigation area is a relatively independent unit, and there is no need to consider the mutual exchange of water between the sub-irrigation areas.

[0076] In addition, the calculation of the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area also includes the following steps:

[0077] The maximum permissible seepage depth is set to H. Seepage exceeding the depth H is regarded as invalid irrigation water due to deep seepage.

[0078] The position at height H below the lowest point of the micro-topography (the lowest point of the sub-irrigation area) is used as the reference elevation line, and the infiltration water exceeding this position is regarded as invalid irrigation water.

[0079] In order to facilitate understanding of the above technical solution of the present invention, a specific embodiment of the present invention is described in detail below.

[0080] A school in Handan was selected as the detailed implementation point. The 1954 Beijing rectangular coordinate system standard was adopted. The micro-topography position coordinates were (58014.968, 53106.118) (57890.908, 53315.704). It was surrounded by four school roads with a total area of ​​20,300 square meters. The crops planted in the micro-topography were crabapple trees and green grass. First, the typical irrigation point P was determined. i (x i ,y i ), according to the actual planting situation, 10 typical irrigation points were determined, namely P1 (57933.839, 53163.370), P2 (57935.125, 53170.331), P3 (57919.513, 53195.236), P4 (57930.113, 53202.214), P5 (57950.998, 53243.589), P6 (57954.563, 53250.674), P7 (57960.868, 53291.998), P8 (57965.769, 53283.324), P9 (58009.665, 53307.788), P 10 (58012.435, 53310.046). Each type of water-demanding crop has different irrigation weights w according to its water demand during the growth period. Generally speaking, the water demand is larger during the seedling and growth periods, and smaller during the flowering and fruiting period. The average irrigation distance T2 and the maximum irrigation distance T1 are taken as the two distance thresholds of the Canopy algorithm, where T1>T2. According to the sprinkler equipment, the maximum irrigation distance is 25 meters and the average irrigation distance is 23 meters.

[0081] 1. Use Canopy algorithm to determine the number of sub-irrigation areas K:

[0082] 1) Take 10 irrigation points as a data set, and select a point P1 from it as the first class, called Canopy.

[0083] 2) Continue to select a point P2 from the set, and calculate the distance d from P2 to P1 21 = 10.29 < T1 < T2, which means that P2 and the Canopy are close enough. At this time, delete P2 from the set to avoid adding it to other Canopies repeatedly.

[0084] 3) If the distance d from P2 to other points is greater than T1, then take P2 as a new Canopy.

[0085] 4) Continue to perform the above operations on the remaining 8 irrigation points until the set is empty. The algorithm ends, the clustering is completed, and the total number of Canopies is the number of sub-regions K value. After calculation, K = 3.

[0086] II. Use the K-means algorithm to divide the sub-irrigation areas:

[0087] 1) Establish an initial partition according to the k value to obtain k clusters, that is, randomly select k points as the initial centroids;

[0088] 2) Calculate the distance from each point P i to each initial centroid, and add it to the nearest cluster;

[0089] 3) Recalculate the centroid of each cluster;

[0090] 4) Repeat steps 2) and 3) until the centroids of each cluster do not change within a certain accuracy range or reach the maximum number of iterations.

[0091] The centroid of the cluster obtained through the above steps can be used as the center of the irrigation area, that is, the installation point of the sprinkler device.

[0092] As Figure 3 shown, the initial K value in this area is set to 3, and the maximum number of iterations is 3 times, resulting in three final clusters: the first is P1, P2, P3, P4, and the center of the irrigation area is (57929.647, 53182.788); the second is P5, P6, P7, P8, and the center of the irrigation area is (57958.002, 53267.936); the third is P9, P10, and the center of the irrigation area is (58011.05, 5330.089).

[0093] III. Subsequently, calculate the irrigation volume according to h k and then determine the irrigation flow rate Q.

[0094] 1) Set the maximum allowable seepage depth H. Seepage exceeding the depth H is considered invalid irrigation water due to deep seepage. Considering the ecological habits and root conditions of the crabapple trees, the value of H is 1 meter;

[0095] 2) The position of height H below the lowest point of micro-topography is the reference elevation line;

[0096] 3) Establish a three-dimensional rectangular coordinate system on the micro-topography. The points on the micro-topography are represented by (x k ,y k ,h k ) means, where x k represents the horizontal coordinate of the kth irrigation zone; y k represents the ordinate of the kth irrigation zone; h k represents the distance between the ground point of the kth irrigation zone and the reference elevation;

[0097] 4) Calculate the irrigation volume V required for the sub-irrigation area k =S k ·h k , where S k Represents the area of ​​the Kth irrigation zone, in m2.

[0098] 5) Calculate the total irrigation flow Where η is the sprinkler water utilization coefficient, which is generally 0.7-0.9.

[0099] In summary, with the aid of the above-mentioned technical scheme of the present invention, the method of the micro-topography zoning algorithm based on the hybrid clustering of the Canopy algorithm and the K-means algorithm proposed in the present invention can optimize the irrigation zoning according to the detailed terrain data of different regions, and is more suitable for the irrigation conditions of the micro-topography crops themselves, thereby improving the irrigation efficiency. Compared with the existing irrigation management methods, it has the advantages of fewer constraints, fast calculation, and accurate results. In addition, the new micro-topography zoning management method proposed in the present invention can determine the optimal irrigation water volume according to different terrains, which can not only meet the irrigation water requirements of different crops, but also achieve the purpose of economic water conservation, realize the reasonable allocation of water resources, improve the efficiency of irrigation management, and provide the best irrigation system for micro-topography. In addition, the micro-topography data collection method proposed in the present invention has the advantages of high data collection accuracy, high universality, and easy sampling.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A micro-terrain intelligent zoning management method based on K-means algorithm, characterized in that: The management method includes the following steps: S1. Obtain micro-topography boundary data and classify the irrigated crops within the micro-topography boundary; S2, based on micro-topography coordinate data combined with typical irrigation points, the number of sub-irrigation areas is determined using the Canopy algorithm; The typical irrigation point is determined according to the irrigation crop position of the micro-topography, including the following steps: selecting trees and shrubs as typical irrigation crops, and using the tree position coordinates and shrub centroid coordinates as the typical irrigation point P i , where i represents the i-th typical irrigation point and its value is an integer greater than zero; The method of determining the number of sub-irrigation areas by using the Canopy algorithm based on micro-topography coordinate data combined with typical irrigation points includes the following steps: S21, taking the position coordinates of a preset number of typical irrigation points as an original data set, and selecting point P1 from the original data set as the first class, called Canopy; S22, select point P2 from the original data set, calculate the distance from point P2 to point P1, and if the distance is less than the maximum irrigation distance, add point P2 to the Canopy, otherwise use point P2 as a new Canopy; S23, determining whether the distance is less than the average irrigation distance, if not, ignoring it, and if so, deleting point P2 from the original data set; S24, continue to execute S22 and S23 for other typical irrigation points in the original data set until the set is empty, the algorithm ends, and clustering is completed, wherein the total number of Canopy is the number K value of sub-irrigation areas; S3, using K-means algorithm to further divide the sub-irrigation areas and determine the location coordinates of the sub-irrigation areas; The method of further dividing the sub-irrigation areas by using the K-means algorithm to determine the location coordinates of the sub-irrigation areas includes the following steps: S31, establishing an initial partition according to the number K of sub-irrigation areas to obtain K clusters, and randomly selecting K points as initial centroids; S32, calculating the distance between each typical irrigation point and each initial centroid one by one, and adding the distance to the nearest cluster to calculate the centroid of each cluster; S33, repeatedly executing S32 until the centroid of each cluster does not change within a certain accuracy range or reaches a maximum number of iterations, and then taking the obtained centroid of the cluster as the center of the sub-irrigation area, that is, the irrigation device placement point; S4. Calculate the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area.

2. According to claim 1, a micro-terrain intelligent zoning management method based on K-means algorithm is characterized in that: The micro-topography coordinate data are the boundary coordinates of the micro-topography, and the micro-topography coordinates are provided by GIS or manually input.

3. According to claim 1, a micro-terrain intelligent zoning management method based on K-means algorithm is characterized in that: The maximum irrigation distance and the average irrigation distance are two distance thresholds of Canopy, and the maximum irrigation distance is the maximum working distance of the irrigation device under normal working conditions, and the average irrigation distance is the middle number between the maximum working distance and the minimum working distance of the irrigation device under normal working conditions.

4. According to the micro-terrain intelligent zoning management method based on K-means algorithm as described in claim 1, it is characterized in that: Calculating the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area includes the following steps: S41. Establish a three-dimensional rectangular coordinate system on the micro-topography and use (x k ,y k ,h k ) represents a point on the micro-topography surface, where x k represents the horizontal coordinate of the kth sub-irrigation area, y k represents the ordinate of the kth irrigation zone, h k represents the distance between the ground point of the kth irrigation zone and the reference elevation; S42. Calculate the irrigation volume of each sub-irrigation area using the following formula: ； Among them, S k represents the area of ​​the kth irrigation zone; S43. Calculate the total irrigation flow of each sub-irrigation area based on the irrigation weight and irrigation volume in each sub-irrigation area. The calculation formula is as follows: ； Among them, Q represents the total irrigation flow, w represents the irrigation weight in each sub-irrigation area, and η is the sprinkler water utilization coefficient, which is generally 0.7-0.

9.

5. According to claim 4, a micro-terrain intelligent zoning management method based on K-means algorithm is characterized in that: The calculation of the irrigation weight and irrigation volume in each sub-irrigation area to obtain the total irrigation flow of each sub-irrigation area also includes the following steps: The maximum permissible seepage depth is set to H. Seepage exceeding the depth H is regarded as invalid irrigation water due to deep seepage. The position at a height H below the lowest point of the micro-topography is used as the reference elevation line, and the infiltration water exceeding this position is regarded as invalid irrigation water.

6. The micro-terrain intelligent zoning management method based on K-means algorithm according to claim 4 is characterized in that: Each of the sub-irrigation areas is a relatively independent unit, and there is no need to consider the mutual exchange of water between the sub-irrigation areas.

7. The micro-terrain intelligent zoning management method based on K-means algorithm according to claim 4 is characterized in that: The irrigation weight w in each sub-irrigation area is set according to the different water requirements of different types of water-demanding crops during their growth cycle.

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