Water collection area division method based on big data view angle

Abstract

The invention relates to the technical field of catchment area division, and discloses a catchment area division method based on a big data perspective, and the method comprises the following steps: S101, obtaining and loading a DEM graph of a target research basin; step S102, dividing the DEM graph into t * t grids, and carrying out hydrological analysis operation on the T * t grids; and step S103, segmenting the target research drainage basin according to the result of the hydrological analysis operation in the step S102. According to the method, the traditional step of extracting the river network by using the DEM to generate the grid flow direction and the catchment area is changed, the digital real river-lake network is directly adopted, and the definition of the grid flow direction and the catchment unit division are assisted, so that the DEM depression filling pretreatment problem is solved more efficiently, more comprehensively and reasonably, and in addition, the method has the advantages that the method is simple and convenient to operate. And the size of the grid in the DEM graph is determined according to the specific condition of the target research drainage basin, so that the efficiency and the precision of dividing the catchment area can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of watershed division, and more specifically, to a method for watershed division based on a big data perspective. Background Art

[0002] The current management of river basin water environment faces many challenges. Although the water environment itself has significant holistic characteristics, the information sharing mechanism for water environment protection and pollution prevention is not yet sound at the practical level. This has led to the dispersion of relevant resources and the formation of many information islands, which has seriously hindered the systematic planning and coordinated management of water environment governance in the river basin. Although traditional catchment area division technology has supported river basin analysis to a certain extent, it often fails to fully utilize the advantages of modern information technology, especially in big data processing and analysis. Summary of the invention

[0003] The present invention provides a method for watershed division based on big data perspective, which solves the technical problems in the above-mentioned background technology.

[0004] The present invention provides a method for watershed division based on big data perspective, comprising the following steps:

[0005] Step S101, obtaining and loading the DEM map of the target study basin;

[0006] Step S102, dividing the DEM image into t*t grids and performing hydrological analysis operations on them;

[0007] Hydrological analysis operations include: depression filling, flow extraction, flow extraction, and stream classification;

[0008] Among them, filling depressions is used to fill the depressions in the DEM map;

[0009] Flow direction extraction is based on the D8 algorithm to extract the flow direction in the DEM map;

[0010] Flow extraction is used for flow in the target study basin;

[0011] River classification is used to determine the river class of the target study basin based on its flow;

[0012] Step S103, segmenting the target study watershed according to the result of the hydrological analysis operation in step S102.

[0013] Furthermore, the filling of the depression specifically includes the following steps:

[0014] Step S201, screening non-boundary grids in the DEM map, assigning maximum water surface to them, submerging their original elevation values, and retaining the original elevation values ​​of the boundary grids;

[0015] Step S202, setting a sliding window of size 3*3, sliding in the non-boundary grids in the DEM image;

[0016] Step S203, repeating step S202 until all grids are traversed.

[0017] Furthermore, the maximum water surface is the maximum elevation value of the corresponding grid plus 1.

[0018] Furthermore, when the sliding window traverses the grids in the DEM image, the grid at the center of the sliding window is marked as grid C, and the eight neighboring grids of grid C are traversed;

[0019] Compare the current elevation value of grid C with the elevation value of the neighboring grid. If the sum of the elevation value of the neighboring grid and the minimum variable is less than the current elevation value of grid C, then assign grid C the sum of the elevation value of the neighboring grid and the minimum variable.

[0020] The smallest variables are custom parameters.

[0021] Furthermore, in the process of assigning values ​​to grid C, the size relationship between the original elevation value of grid C and the sum of the elevation value of the neighboring grid and the minimum variable is judged. If the original elevation value of grid C is greater than the sum of the elevation value of the neighboring grid and the minimum variable, the original elevation value of grid C is reassigned to the current elevation value of grid C.

[0022] Furthermore, the method of flow extraction is as follows:

[0023] In the process of sliding the window through the non-boundary grids in the DEM image, the distance weight difference P between the grid C and each neighboring grid is calculated. i , and its calculation formula is as follows:

[0024]

[0025] In the formula, Δz ci Represents the difference in elevation between grid C and neighboring grids 1 to i; d ci is a custom parameter;

[0026] Get the distance weight difference P i The largest neighborhood grid, which is the water flow direction of grid C.

[0027] Furthermore, the method of traffic extraction is as follows:

[0028] Construct the flow network of the target study basin based on the flow information of all grids in the DEM map;

[0029] A non-boundary grid is randomly selected, and all grids flowing to the grid are reversely accumulated according to the flow network to form a catchment area;

[0030] Calculate the flow Q of the grid, and the calculation formula is as follows:

[0031] Q = A × R;

[0032] Where A represents the area of ​​the catchment area; R represents the runoff coefficient per unit area of ​​the catchment area; and R is a custom parameter.

[0033] Further, the river classification method is as follows:

[0034] Rivers are divided into three levels according to flow size and catchment area: high-level rivers, medium-level rivers and low-level rivers;

[0035] The catchment area and flow of the target study basin are matched to the corresponding level.

[0036] Furthermore, the calculation formula of t is as follows:

[0037]

[0038] Where T represents the topographic factor of the target study basin; S represents the soil type factor of the target study basin; V represents the vegetation cover factor of the target study basin.

[0039] Furthermore, the calculation formula of T is as follows:

[0040]

[0041] In the formula, θ represents the slope of the target study basin; Roughness Index represents the surface roughness index of the target study basin;

[0042]

[0043] In the formula, Soil Permeability represents the soil permeability of the target study basin;

[0044]

[0045] Where Vegetation Conver represents the vegetation coverage of the target study watershed; k1, k2, k3, k4 and k5 are all custom parameters.

[0046] The beneficial effects of the present invention are as follows: it changes the traditional steps of using DEM to generate grid flow direction and catchment area to extract river network, directly adopts digitized real river and lake network, and then assists in the definition of grid flow direction and water collection unit division, aiming to solve the DEM depression filling preprocessing problem more efficiently, comprehensively and reasonably. In addition, the grid size in the DEM map is determined according to the specific conditions of the target research basin, which can effectively improve the efficiency and accuracy of water collection area division. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is the overall flow chart of the present invention;

[0048] Figure 2 It is a specific flow chart of the depression filling process of the present invention. DETAILED DESCRIPTION

[0049] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that the discussion of these embodiments is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the contents of this specification. Each example may omit, replace or add various processes or components as needed. In addition, the features described relative to some examples may also be combined in other examples.

[0050] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in one or more embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] like Figure 1-Figure 2 As shown, a method for watershed division based on big data perspective includes the following steps:

[0052] Step S101, obtain and load the DEM map of the target research basin. The DEM map is the abbreviation of Digital Elevation Model (DEM), which is a digital simulation of the ground terrain (i.e., a digital expression of the terrain surface morphology) through limited terrain elevation data. It is a physical ground model that represents the ground elevation in the form of a set of ordered numerical arrays. Preferably, the DEM map is loaded using GIS software. GIS software is the abbreviation of Geographic Information System (Geographic Information System or Geo-Information system, GIS), which is a technical system that collects, stores, manages, calculates, analyzes, displays and describes the relevant geographical distribution data in the entire or part of the earth's surface (including the atmosphere) space under the support of computer hardware and software systems;

[0053] It should be noted that DEM maps and GIS software are both existing technologies and will not be described in detail here.

[0054] Step S102, dividing the DEM image into t*t grids and performing hydrological analysis operations on them;

[0055] Hydrological analysis operations include: depression filling, flow extraction, flow extraction, and stream classification;

[0056] Among them, filling depressions is used to fill the depressions in the DEM map;

[0057] Flow direction extraction is based on the D8 algorithm to extract the flow direction in the DEM map;

[0058] Flow extraction is used for flow in the target study basin;

[0059] River classification is used to determine the river class of the target study basin based on its flow;

[0060] Step S103, segmenting the target study watershed according to the result of the hydrological analysis operation in step S102.

[0061] The specific steps of filling depression include:

[0062] Step S201, screening non-boundary grids in the DEM map, assigning maximum water surface to them, submerging their original elevation values, and retaining the original elevation values ​​of the boundary grids;

[0063] Step S202, setting a sliding window of size 3*3, sliding in the non-boundary grids in the DEM image;

[0064] Step S203, repeating step S202 until all grids are traversed;

[0065] After traversing all the grids in the DEM map, the depressions in the DEM map are filled.

[0066] The maximum water surface is the maximum elevation value of the corresponding grid plus 1.

[0067] When the sliding window traverses the grids in the DEM image, the grid at the center of the sliding window is marked as grid C, and the eight neighboring grids of grid C are traversed;

[0068] Compare the current elevation value of grid C with the elevation value of the neighboring grid. If the sum of the elevation value of the neighboring grid and the minimum variable is less than the current elevation value of grid C, then assign grid C the sum of the elevation value of the neighboring grid and the minimum variable.

[0069] The minimum variable is a custom parameter. Preferably, the minimum variable is 0.1.

[0070] In the process of assigning values ​​to grid C, the relationship between the original elevation value of grid C and the sum of the elevation value of the neighboring grid and the minimum variable is judged. If the original elevation value of grid C is greater than the sum of the elevation value of the neighboring grid and the minimum variable, the original elevation value of grid C is reassigned to the current elevation value of grid C.

[0071] The flow extraction method is as follows:

[0072] In the process of sliding the window through the non-boundary grids in the DEM image, the distance weight difference P between the grid C and each neighboring grid is calculated. i , and its calculation formula is as follows:

[0073]

[0074] In the formula, Δz ci Represents the difference in elevation between grid C and neighboring grids 1 to i; d ci is a custom parameter;

[0075] Get the distance weight difference P i The largest neighborhood grid, which is the water flow direction of grid C;

[0076] As the sliding window traverses the non-boundary grids in the DEM image, the flow direction of all grids can be determined.

[0077] The method of traffic extraction is as follows:

[0078] Construct the flow network of the target study basin based on the flow information of all grids in the DEM map;

[0079] A non-boundary grid is randomly selected, and all grids flowing to the grid are reversely accumulated according to the flow network to form a catchment area;

[0080] Calculate the flow Q of the grid, and the calculation formula is as follows:

[0081] Q = A × R;

[0082] Where A represents the area of ​​the catchment area; R represents the runoff coefficient per unit area of ​​the catchment area; and R is a custom parameter.

[0083] The specific method of river classification is as follows:

[0084] Rivers are divided into three levels according to flow size and catchment area: high-level rivers, medium-level rivers and low-level rivers;

[0085] The catchment area and flow of the target study basin are matched to the corresponding level.

[0086] In addition, since the grid size is related to the efficiency and accuracy of the watershed division method, and the topography, soil type and vegetation coverage of the target study basin will affect the flow and area of ​​the watershed, it is necessary to calculate the size of t. Therefore, the calculation formula of t is as follows:

[0087]

[0088] Where T represents the topographic factor of the target study basin; S represents the soil type factor of the target study basin; V represents the vegetation cover factor of the target study basin.

[0089] The calculation formula of T is as follows:

[0090]

[0091] Wherein, θ represents the slope of the target study basin; Roughness Index represents the surface roughness index of the target study basin; Roughness Index is an indicator used to quantify the roughness or complexity of the surface. It reflects the degree of resistance of the surface structure to the movement of wind or water flow. In hydrology, surface roughness affects the dispersion, velocity and energy consumption of water flow, thereby affecting the flow path and erosion process. The roughness index can be determined based on the surface cover type (such as bare soil, grassland, forest, etc.) or by directly measuring surface characteristics (such as rocks, vegetation density, etc.). A higher roughness index means that the water flow encounters greater resistance, which may lead to a decrease in water flow velocity and local waterlogging.

[0092]

[0093] Where Soli Permeability represents the soil permeability of the target study basin; Soil Permeability refers to the ability of soil to allow water or other fluids to pass through. It is an important parameter for evaluating soil drainage performance and water transfer rate. Soil permeability is affected by factors such as soil particle size, arrangement, compactness, and organic matter content. High permeability soils (such as sand) allow water to infiltrate quickly, while low permeability soils (such as clay) slow down water flow, which may lead to increased surface runoff and potential waterlogging problems. Soil permeability has a direct impact on the generation of surface runoff, groundwater recharge, and the migration of pollutants.

[0094]

[0095] In the formula, Vegetation Cover represents the vegetation coverage rate of the target study watershed; k1, k2, k3, k4 and k5 are all custom parameters. Vegetation Cover refers to the degree of surface coverage by plants, usually expressed as a percentage. Vegetation reduces soil erosion through the fixation of roots. Leaves and stems can intercept precipitation, slow down the kinetic energy of raindrops hitting the surface, increase water transpiration and surface shading, thereby affecting surface energy balance and water cycle. High vegetation coverage helps maintain soil moisture, reduce surface runoff, and increase water infiltration, which is crucial to maintaining ecological balance and preventing soil erosion. In hydrological models, vegetation cover is one of the key factors in evaluating watershed hydrological responses and formulating land management strategies.

[0096] The above describes an embodiment of the present embodiment, but the present embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present embodiment, ordinary technicians in this field can also make many forms, all of which are within the protection of the present embodiment.

Claims

1. A method for watershed division based on big data perspective, characterized in that: The following steps are involved: Step S101, obtaining and loading the DEM map of the target study basin; Step S102, dividing the DEM image into t*t grids and performing hydrological analysis operations on them; Hydrological analysis operations include: depression filling, flow extraction, flow extraction, and stream classification; Among them, filling depressions is used to fill the depressions in the DEM map; Flow direction extraction is based on the D8 algorithm to extract the flow direction in the DEM map; Flow extraction is used for flow in the target study basin; River classification is used to determine the river class of the target study basin based on its flow; Step S103, segmenting the target study watershed according to the result of the hydrological analysis operation in step S102.

2. The method for watershed division based on big data perspective according to claim 1 is characterized in that: The specific steps of filling depression include: Step S201, screening non-boundary grids in the DEM map, assigning maximum water surface to them, submerging their original elevation values, and retaining the original elevation values ​​of the boundary grids; Step S202, setting a sliding window of size 3*3, sliding in the non-boundary grids in the DEM image; Step S203, repeating step S202 until all grids are traversed.

3. The method for watershed division based on big data perspective according to claim 2 is characterized in that: The maximum water surface is the maximum elevation value of the corresponding grid plus 1.

4. The method for watershed division based on big data perspective according to claim 2 is characterized in that: When the sliding window traverses the grids in the DEM image, the grid at the center of the sliding window is marked as grid C, and the eight neighboring grids of grid C are traversed; Compare the current elevation value of grid C with the elevation value of the neighboring grid. If the sum of the elevation value of the neighboring grid and the minimum variable is less than the current elevation value of grid C, then assign grid C the sum of the elevation value of the neighboring grid and the minimum variable. The smallest variables are custom parameters.

5. The method for watershed division based on big data perspective according to claim 4 is characterized in that: In the process of assigning values ​​to grid C, the relationship between the original elevation value of grid C and the sum of the elevation value of the neighboring grid and the minimum variable is judged. If the original elevation value of grid C is greater than the sum of the elevation value of the neighboring grid and the minimum variable, the original elevation value of grid C is reassigned to the current elevation value of grid C.

6. The method for watershed division based on big data perspective according to claim 4 is characterized in that: The flow extraction method is as follows: In the process of sliding the window through the non-boundary grids in the DEM image, the distance weight difference P between the grid C and each neighboring grid is calculated. i , and its calculation formula is as follows: In the formula, Δz ci Represents the difference in elevation between grid C and neighboring grids 1 to i; d ci is a custom parameter; Get the distance weight difference P i The largest neighborhood grid, which is the water flow direction of grid C.

7. The method for watershed division based on big data perspective according to claim 6 is characterized in that: The method of traffic extraction is as follows: Construct the flow network of the target study basin based on the flow information of all grids in the DEM map; A non-boundary grid is randomly selected, and all grids flowing to the grid are reversely accumulated according to the flow network to form a catchment area; Calculate the flow Q of the grid, and the calculation formula is as follows: Q = A × R; Where A represents the area of ​​the catchment area; R represents the runoff coefficient per unit area of ​​the catchment area; and R is a custom parameter.

8. The method for watershed division based on big data perspective according to claim 7, characterized in that: The specific method of river classification is as follows: Rivers are divided into three levels according to flow size and catchment area: high-level rivers, medium-level rivers and low-level rivers; The catchment area and flow of the target study basin are matched to the corresponding level.

9. The method for watershed division based on big data perspective according to claim 2, characterized in that: The calculation formula of t is as follows: Where T represents the topographic factor of the target study basin; S represents the soil type factor of the target study basin; V represents the vegetation cover factor of the target study basin.

10. The method for watershed division based on big data perspective according to claim 9, characterized in that: The calculation formula of T is as follows: In the formula, θ represents the slope of the target study basin; Roughness Index represents the surface roughness index of the target study basin; In the formula, Soil Permeability represents the soil permeability of the target study basin; Where Vegetation Cover represents the vegetation coverage of the target study watershed; k1, k2, k3, k4 and k5 are all custom parameters.

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