Grid Microtopography Recognition Method and System Based on Spatial Feature Clustering Analysis

Abstract

The present invention provides a power grid micro-topography recognition method and system based on spatial feature clustering analysis. The method includes: obtaining grid-discretized terrain height data of at least one area with a known micro-topography type; obtaining a height data matrix of the area with the known micro-topography type according to the grid-discretized terrain height data; obtaining typical spatial distribution characteristics of the area with the known micro-topography according to the height data matrix of the area with the known micro-topography type; obtaining terrain height data of the area to be recognized, and obtaining a height data matrix of the area to be recognized according to the terrain height data of the area to be recognized; preliminarily screening the micro-topography area according to the height data matrix of the area to be recognized; comparing the height data matrix of the preliminarily recognized micro-topography area with the typical spatial distribution characteristics of the area with the known micro-topography type, and re-recognizing the preliminarily recognized micro-topography area. By performing preliminary recognition and secondary recognition on the area to be recognized, the present invention improves the accuracy of micro-topography recognition.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid protection, and particularly relates to a power grid micro-topography recognition method and system based on spatial feature clustering analysis. Background Art

[0002] Micro-topography and micro-meteorology refer to the change of meteorological factors such as wind speed, temperature, and humidity in a small range due to the different altitudes of local terrains in specific mountainous or hilly areas. Generally, the characteristics of micro-meteorology will be reflected in meteorological phenomena such as strong winds, freezing rain, fog and other bad weather. Although micro-topography and micro-meteorology will not cause large-scale changes in weather and climate characteristics, due to the linear and continuous distribution characteristics of transmission lines, the local bad micro-meteorological conditions in the range of one or two towers causing line tripping or damage will affect the normal operation of the entire line ranging from dozens of kilometers to hundreds of kilometers.

[0003] Due to the rapid development of China's power grid construction, there are more and more high-voltage and long-distance transmission lines, and more and more lines cross mountains, canyons, and hilly areas. The influence of power grid micro-topography and micro-meteorology areas is becoming more and more significant. In addition, due to China's vast territory and complex terrain, transmission lines pass through a large number of sparsely populated areas. Therefore, carrying out automatic identification of power grid micro-topography is a prerequisite for realizing accurate prediction of micro-meteorology in micro-topography areas and supporting the stable operation of the power grid. Summary of the Invention

[0004] The main purpose of the present invention is to propose a power grid micro-topography recognition method and system based on spatial feature clustering analysis, aiming to solve the technical problems of difficult and low-precision recognition of power grid micro-topography areas of transmission lines.

[0005] To achieve the above object, the present invention provides a power grid micro-topography recognition method based on spatial feature clustering analysis. The power grid micro-topography recognition method based on spatial feature clustering analysis includes:

[0006] Obtain the grid discretized terrain height data of at least one known micro-topography type area;

[0007] According to the grid discretized terrain height data of the known micro-topography type area, obtain the height data matrix of the known micro-topography type area;

[0008] According to the height data matrix of the known micro-topography type area, obtain the typical spatial distribution characteristics of the known micro-topography area;

[0009] Obtain the terrain height data of the area to be recognized, and obtain the height data matrix of the area to be recognized according to the terrain height data of the area to be recognized;

[0010] Perform a preliminary screening of the micro-topography area based on the height data matrix of the area to be recognized, and obtain a preliminarily recognized micro-topography area;

[0011] Compare the height data matrix of the preliminarily recognized micro-topography area with the typical spatial distribution characteristics of the known micro-topography type areas, and re-recognize the preliminarily recognized micro-topography area.

[0012] In an embodiment of the present invention, the step of obtaining the typical spatial distribution characteristics of the known micro-topography areas according to the height data matrix of the known micro-topography type areas includes:

[0013] Perform a normalization process on the height data matrix of the known micro-topography type areas, and obtain a normalized height data matrix of the known micro-topography type areas;

[0014] Merge the normalized height data matrices of the known micro-topography type areas of the same type, and obtain the terrain height data matrix of each type of known micro-topography area;

[0015] Calculate the matrix average distribution and the average value matrix of the micro-topography areas of the same type;

[0016] Obtain the typical spatial distribution characteristics of the known micro-topography type areas according to the normalized height matrix and the average value matrix of each micro-topography point in the terrain height data matrix of the micro-topography areas of the same type.

[0017] In an embodiment of the present invention, the step of obtaining the typical spatial distribution characteristics of the known micro-topography type areas according to the normalized height matrix and the average value matrix of each micro-topography point in the terrain height data matrix of the micro-topography areas of the same type includes:

[0018] Calculate the first distance between the normalized height matrix of a micro-topography area in the terrain height data matrix of the micro-topography areas of the same type and the average value matrix;

[0019] Calculate the average distance of all micro-topography points in the micro-topography areas of the same type;

[0020] Compare the first distance and the average distance;

[0021] When the maximum first distance is greater than a preset multiple of the average distance, remove the micro-topography points belonging to the maximum first distance from the terrain height data matrix of the micro-topography areas of the same type;

[0022] When the maximum first distance is less than or equal to a preset multiple of the average distance, retain the micro-topography points belonging to the maximum first distance in the terrain height data matrix of the micro-topography areas of the same type.

[0023] In an embodiment of the present invention, the first distance can be calculated through the following calculation formula:

[0024]

[0025] where Lnx is the first distance between a normalized height matrix and an average value matrix of a micro-topography area in the terrain height data matrix of the same type of micro-topography area; M is the amount of grid point data of the selected micro-topography area; h i,j is the grid point value in the height data matrix of the known micro-topography type area.

[0026] In an embodiment of the present invention, the step of preliminarily screening the micro-topography area according to the height data matrix of the area to be identified and obtaining the preliminarily identified micro-topography area includes:

[0027] Calculate the average height and undulation height of the area to be identified;

[0028] Preliminarily identify the area to be identified according to the average height and undulation height of the area to be identified;

[0029] When the average height is less than 200 meters and the undulation height is less than 50 meters, or the average height is greater than 200 meters and the undulation height is less than 50 meters, it is preliminarily identified that the area to be identified does not belong to the micro-topography area, and the identification ends.

[0030] In an embodiment of the present invention, the step of re-identifying the preliminarily identified micro-topography area by comparing the height data matrix of the preliminarily identified micro-topography area with the typical spatial distribution characteristics of the known micro-topography type area includes:

[0031] Perform normalization processing on the height data matrix of the preliminarily identified micro-topography area to obtain the normalized height data matrix of the preliminarily identified micro-topography area;

[0032] Obtain the second distance between the normalized height data matrix of the preliminarily identified micro-topography area and the average value matrix of the spatial distribution of the known micro-topography type area;

[0033] Re-identify the preliminarily identified micro-topography area according to the relationship between the second distance and the average distance.

[0034] In an embodiment of the present invention, the step of re-identifying the preliminarily identified micro-topography area according to the relationship between the second distance and the average distance includes:

[0035] When the second distance is less than or equal to a preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area belongs to the corresponding micro-topography in the known micro-topography type area;

[0036] When the second distance is greater than a preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area does not belong to the corresponding micro-topography in the known micro-topography type areas.

[0037] In an embodiment of the present invention, after the step of determining that the preliminarily identified micro-topography area does not belong to the corresponding micro-topography in the known micro-topography type areas when the second distance exceeds a preset multiple of the average distance, the following steps are further included:

[0038] Process the standardized height data matrix of the preliminarily identified micro-topography area that does not belong to the known micro-topography type area to confirm whether a new micro-topography type can be extracted.

[0039] In an embodiment of the present invention, the method for identifying power grid micro-topography based on spatial feature clustering analysis further includes grouping the area identification results of the area to be identified, and the identification is completed.

[0040] Moreover, the present invention also provides a power grid micro-topography identification system based on spatial feature clustering analysis. The power grid micro-topography identification system based on spatial feature clustering analysis includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the above-mentioned method for identifying power grid micro-topography based on spatial feature clustering analysis are implemented.

[0041] Through the above technical solutions, the method for identifying power grid micro-topography based on spatial feature clustering analysis provided by the embodiments of the present invention has the following beneficial effects:

[0042] First, obtain the grid discretized terrain height data of at least one known micro-topography type area; according to the grid discretized terrain height data of the known micro-topography type area, obtain the height data matrix of the known micro-topography type area; according to the height data matrix of the known micro-topography type area, obtain the typical spatial distribution characteristics of the known micro-topography area; then, obtain the terrain height data of the area to be identified, and according to the terrain height data of the area to be identified, obtain the height data matrix of the area to be identified; conduct a preliminary screening of the micro-topography area according to the height data matrix of the area to be identified, and obtain the preliminarily identified micro-topography area; finally, compare the height data matrix of the preliminarily identified micro-topography area with the typical spatial distribution characteristics of the known micro-topography type area to conduct a secondary identification of the preliminarily identified micro-topography area. By conducting a preliminary identification and a secondary identification of the area to be identified, the present invention improves the accuracy of micro-topography identification and reduces the interference of other external factors; and uses the method of spatial distribution feature matrix to identify micro-topography, which can cover the largest required area range in the simplest way and improves the ease of micro-topography identification.

[0043] Other features and advantages of the present invention will be described in detail in the following detailed implementation section. Description of the Drawings

[0044] The drawings are used to provide an understanding of the present invention and form a part of the specification. Together with the following detailed implementation, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the drawings:

[0045] Figure 1 is a schematic flowchart of the power grid micro-topography recognition method based on spatial feature clustering analysis according to the present invention. Detailed Implementation

[0046] The following will describe in detail the specific embodiments of the present invention with reference to the drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0047] The following describes the power grid micro-topography recognition method based on spatial feature clustering analysis according to the present invention with reference to the drawings.

[0048] As Figure 1 shown, in the embodiment of the present invention, a power grid micro-topography recognition method based on spatial feature clustering analysis is proposed. The power grid micro-topography recognition method based on spatial feature clustering analysis includes:

[0049] Step S10: Obtain the grid-discretized terrain height data of at least one known micro-topography type area;

[0050] Step S20: Obtain the height data matrix of the known micro-topography type area according to the grid-discretized terrain height data of the known micro-topography type area;

[0051] Step S30: Obtain the typical spatial distribution characteristics of the known micro-topography area according to the height data matrix of the known micro-topography type area;

[0052] Step S40: Obtain the terrain height data of the area to be recognized, and obtain the height data matrix of the area to be recognized according to the terrain height data of the area to be recognized;

[0053] Step S50: Conduct a preliminary screening of the micro-topography area according to the height data matrix of the area to be recognized, and obtain the preliminarily recognized micro-topography area;

[0054] Step S60: Compare the height data matrix of the preliminarily recognized micro-topography area with the typical spatial distribution characteristics of the known micro-topography type area, and conduct a re-identification of the preliminarily recognized micro-topography area.

[0055] Specifically, terrain data with a resolution of 30 meters × 30 meters in known typical micro-topography regions is collected, that is, the size of each small grid in the grid is 30 meters × 30 meters; terrain data with a resolution of 30 meters × 30 meters in the region where micro-topography identification is required is collected. Among them, the resolution can be selected according to actual needs, or it can be a resolution of 50 meters × 50 meters. When the resolution is selected as 30 meters × 30 meters, according to the 30-meter resolution terrain data of each known typical micro-topography type region, a square grid of 3 km × 3 km is demarcated on the known micro-topography type region, and the axial directions of the grid are north-south and east-west, obtaining a terrain height data matrix h(100, 100) of the known micro-topography type region. That is, this matrix includes 10,000 grid data points, where 100 represents the terrain height data in the latitudinal and longitudinal directions.

[0056] Further, the same as obtaining the height data of the known micro-topography type region, for the identification of the terrain features of the region to be identified, a square grid of 3 km × 3 km is demarcated in the region to be identified, and the axial directions of the grid are north-south and east-west, obtaining a terrain height data matrix Z(100, 100) of the region to be identified, and 100 represents the terrain height data in the latitudinal and longitudinal directions.

[0057] In order to reduce the interference of external factors and cause errors in identification and improve the accuracy of identification, first, a preliminary screening of the micro-topography region is performed on the terrain height data matrix Z(100, 100) of the region to be identified to remove obvious grid points that do not belong to the micro-topography region, such as plains or open areas, saving time for subsequent further accurate identification. When the preliminary screening is completed, the preliminary identified micro-topography region is obtained. The preliminary identified micro-topography region after preliminary identification is processed, and then through the comparison of the height data matrix of the preliminary identified micro-topography region and the typical spatial distribution characteristics of the known micro-topography type region, the secondary identification of the preliminary identified micro-topography region is realized, and then the identification of the region to be identified is accurately completed.

[0058] In this embodiment, by performing preliminary identification and secondary identification on the region to be identified, the accuracy of micro-topography identification is improved, and the interference of other external factors is reduced; and the method of using the spatial distribution characteristic matrix to identify micro-topography can cover the largest required regional range in the simplest way, improving the ease of micro-topography identification.

[0059] In the embodiment of the present invention, the steps of obtaining the typical spatial distribution characteristics of the known micro-topography region according to the height data matrix of the known micro-topography type region include:

[0060] Step S31: Standardize the height data matrix of the known micro-topography type region and obtain the standardized height data matrix of the known micro-topography type region;

[0061] Step S32: Combine the standardized height data matrices of known microtopography type regions of the same type, and obtain the terrain height data matrices of various known microtopography regions;

[0062] Step S33: Calculate the matrix average distribution and average value matrix of microtopography regions of the same type;

[0063] Step S34: Obtain the typical spatial distribution characteristics of the known microtopography type region according to the standardized height matrix and average value matrix of each microtopography point in the terrain height data matrix of the microtopography region of the same type.

[0064] First, standardize the height data matrix h(100, 100) of the known microtopography type region, and then combine the standardized height data matrices of the known microtopography of the same type to obtain a set of terrain height data matrices H N (t, 100, 100), where t represents that there are t microtopography regions in this type of microtopography region, and N represents that there are N types of microtopography types; finally, calculate the average distribution of the matrix Hn in the nth type of microtopography type to obtain the average value matrix ave_Hn(100, 100) of the nth type of microtopography region; finally, obtain the typical spatial distribution characteristics of the known microtopography type region according to the standardized height matrix hx(100, 100) and average value matrix ave_Hn(100, 100) of each microtopography region in the terrain height data matrix of the microtopography region of the same type.

[0065] In the embodiment of the present invention, the steps of obtaining the typical spatial distribution characteristics of the known microtopography type region according to the standardized height matrix and average value matrix of each microtopography point in the terrain height data matrix of the microtopography region of the same type include:

[0066] Step S341: Calculate the first distance between the standardized height matrix of a microtopography region in the terrain height data matrix of the microtopography region of the same type and the average value matrix;

[0067] Step S342: Calculate the average distance of all microtopography points in the microtopography region of the same type;

[0068] Step S343: Compare the first distance with the average distance;

[0069] Step S344: When the maximum first distance is greater than a preset multiple of the average distance, remove the microtopography point at the maximum first distance from the terrain height data matrix of the microtopography region of the same type;

[0070] Step S345: When the maximum first distance is less than or equal to a preset multiple of the average distance, the micro-topography point located at the maximum first distance is retained in the topographic height data matrix of the micro-topography area of the same type.

[0071] Calculate the first distance Lnx between the normalized height matrix hx(100, 100) of each micro-topography point in the height data matrix Hn(t, 100, 100) of the nth type of known micro-topography area and the average value matrix ave_Hn(100, 100) of the nth type of known micro-topography area, and calculate the average distance ave_Ln of the micro-topography point at t in the nth type of known micro-topography area;

[0072] For the number of micro-topography points in a type of known micro-topography area, there are as many numerical points of the calculated first distance. That is to say, in the same type of known micro-topography area, the number of numerical points of the first distance is the same as the number of micro-topography points in the known micro-topography area. Among these data, when the maximum first distance Lnx exceeds the preset multiple of the average distance ave_Ln, it is considered that the height distribution feature of this micro-topography point is far from this type, and it is removed from the height data matrix Hn(t, 100, 100), and steps S33 and S34 are repeated until the maximum Lnx does not exceed the preset multiple of the average distance ave_Ln. Preferably, the preset multiple is 2, but it can be adjusted between 1.5 - 3 according to the actual situation.

[0073] Therefore, through the above steps, the typical spatial distribution characteristics of the typical micro-topography area of the known type can be obtained.

[0074] Among them, the first distance can be calculated by the following formula:

[0075]

[0076] Among them, Lnx is the first distance between the normalized height matrix and the average value matrix of a micro-topography area in the topographic height data matrix of the same type of micro-topography area; M is the grid point data volume of the selected micro-topography area; h i,j is the grid point value in the height data matrix of the known micro-topography type area.

[0077] In the embodiment of the present invention, the steps of performing a preliminary screening on the micro-topography area according to the height data matrix of the area to be recognized and obtaining the preliminarily recognized micro-topography area include:

[0078] Step S51: Calculate the average height and the undulating height of the area to be recognized;

[0079] Step S52: Preliminarily recognize the area to be recognized according to the average height and the undulating height of the area to be recognized;

[0080] Step S53: When the average height is less than 200 meters and the undulating height is less than 50 meters, or the average height is greater than 200 meters and the undulating height is less than 50 meters, it is preliminarily identified that the area to be identified does not belong to the micro-topography area, and the identification ends.

[0081] In this embodiment, the average height ave_z of the area to be identified is calculated, and the undulating height qif_z of the area to be identified is calculated. The undulating height qif_z is the difference between the average altitude of the three highest points and the average altitude of the three lowest points. If the average height ave_z is less than 200 meters and the undulating height qif_z is less than 50 meters, then this area is a plain area and does not belong to the micro-topography area, and the judgment stops. If the average height ave_z is greater than 200 meters, but the undulating height qif_z is less than 50 meters, then this area is an open area and also does not belong to the micro-topography area, and the judgment stops, and all points in the area to be identified other than this belong to the micro-topography area. In this embodiment, through the preliminary screening of the micro-topography area, the grid points of the obvious non-micro-topography area can be removed, providing more favorable identification conditions for the next secondary identification.

[0082] In the embodiment of the present invention, the steps of re-identifying the preliminarily identified micro-topography area according to the comparison between the height data matrix of the preliminarily identified micro-topography area and the typical spatial distribution characteristics of the known micro-topography type areas include:

[0083] Step S61: Perform standardization processing on the height data matrix of the preliminarily identified micro-topography area to obtain the standardized height data matrix of the preliminarily identified micro-topography area;

[0084] Step S62: Obtain the second distance between the standardized height data matrix of the preliminarily identified micro-topography area and the average value matrix of the spatial distribution of the known micro-topography type areas;

[0085] Step S63: Re-identify the preliminarily identified micro-topography area according to the relationship between the second distance and the average distance.

[0086] For the area to be judged for micro-topography characteristics after preliminary screening, the matrix Z(100, 100) is standardized. When there are N types of known micro-topography types, calculate the second distance Dn between the matrix Z(100, 100) and the average value matrix ave_Hn(100, 100) of the spatial distribution of the N types of known terrain areas in step 2, so that N second distances can be obtained, and then compare the smallest value among the N second distances with the average distance to realize the secondary identification of the preliminarily identified micro-topography area; this secondary identification method improves the accuracy of micro-topography identification and avoids the occurrence of omissions or misjudgments.

[0087] In an embodiment of the present invention, the step of re-identifying the preliminarily identified micro-topography area according to the relationship between the second distance and the average distance includes:

[0088] Step S631: When the second distance is less than or equal to a preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area belongs to the micro-topography corresponding to the average value matrix of the spatial distribution of the known micro-topography type area;

[0089] Step S632: When the second distance is greater than the preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area does not belong to the corresponding micro-topography in the known micro-topography type area.

[0090] Select the minimum distance value among the N second distances, and compare the minimum second distance with the preset multiple of the average distance. If the minimum distance value among the N second distances is less than or equal to the preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area belongs to the micro-topography corresponding to the average value matrix ave_Hn(100, 100) of the spatial distribution of the known micro-topography type area. The value of the preset multiple is the same as the above selection situation.

[0091] If the minimum distance value among the N second distances is greater than the preset multiple of the average distance, it is determined that the area to be identified for micro-topography features does not belong to any of the currently divided known micro-topography type areas, and these micro-topography points are classified into other categories.

[0092] In an embodiment of the present invention, after the step of determining that the preliminarily identified micro-topography area does not belong to the corresponding micro-topography in the known micro-topography type area when the second distance is greater than the preset multiple of the average distance, the following steps are further included:

[0093] Step S63: Process the standardized height data matrix of the preliminarily identified micro-topography area that does not belong to the known micro-topography type area to confirm whether a new micro-topography type can be extracted.

[0094] Process the standardized height data matrix of the preliminarily identified micro-topography area in the area to be identified that does not belong to the selected known micro-topography type area, and re-perform Step S32 and Step S33 to confirm whether an existing known micro-topography area or a new micro-topography type can be extracted, providing more favorable help for the subsequent research of micro-topography.

[0095] In an embodiment of the present invention, the power grid micro-topography identification method based on spatial feature clustering analysis further includes grouping the area identification results of the area to be identified, and the identification is completed; after grouping, the topographic conditions of the area to be identified are clearer, which is beneficial for researchers to clearly and accurately determine the specific grid block positions of the micro-topography.

[0096] To further illustrate the feasibility of identifying micro-topography using the power grid micro-topography identification method of the present invention, three typical micro-topography regions of known passes, mountaintops, and valleys are collected for identifying the micro-topography regions, and the identification process of specific embodiments is described in detail as follows.

[0097] (1) Collection and collation of basic data

[0098] Collect 30-meter resolution terrain data of three typical micro-topography regions of known passes, mountaintops, and valleys; collect 30-meter resolution terrain data of the region where micro-topography needs to be identified.

[0099] (2) Extraction of characteristic information of typical micro-topography regions

[0100] 2.1 According to the 30-meter resolution terrain data of each known micro-topography type region, a 3 km × 3 km grid is delimited in the micro-topography region, and the axial direction of the grid is the north-south direction and the east-west direction, obtaining a terrain height data matrix h(100, 100) of the known micro-topography type region, where 100 represents the terrain height data in the latitudinal and longitudinal directions.

[0101] 2.2 Standardize the height data matrix h(100, 100).

[0102] 2.3 Combine the standardized height data matrices of the same type of known micro-topography to obtain three terrain height data matrices of three types, H1(t1, 100, 100), H2(t2, 100, 100), H2(t2, 100, 100), where t1, t2, and t3 represent the micro-topography regions at t1, t2, and t3.

[0103] 2.4 Calculate the average distribution of the matrices H1, H2, and H3 in the three micro-topography types respectively, obtaining the average value matrices ave_H1(100, 100), ave_H2(100, 100), and ave_H3(100, 100).

[0104] 2.5 Calculate the first distance L1x between the standardized height matrix hx(100, 100) of each micro-topography region in the matrix H1(t1, 100, 100) and the average value matrix ave_H1(100, 100). where h i,j is the grid point value of the matrix hx(100, 100).

[0105] Calculate the average distance ave_Ln of the micro-topography points at t.

[0106] If the L1x value is the largest and exceeds 2 times of ave_Ln, it is considered that the height distribution feature of this micro-topography point is far from this category, and it is excluded from the height data matrix H1(t1, 100, 100). Repeat steps 2.4 and 2.5 until the largest L1x does not exceed 2 times of ave_Ln.

[0107] Similarly, process the matrices H2(t2, 100, 100) and H3(t3, 100, 100).

[0108] Through step 2, the typical spatial distribution features ave_H1(100, 100), ave_H2(100, 100), and ave_H3(100, 100) of the typical micro-topography regions of three types, namely pass, mountaintop, and canyon, can be obtained.

[0109] (3) Preliminary screening of micro-topography features in the area to be identified

[0110] 3.1 Similarly, for the area where micro-topography features are to be identified, a 3km×3km grid is demarcated in this micro-topography area. The axial directions of the grid are north-south and east-west, and a terrain height data matrix Z(100, 100) of the area to be identified is obtained, where 100 represents the terrain height data in the latitudinal and longitudinal directions.

[0111] 3.2 Calculate the average height ave_z of the area where micro-topography features are to be identified;

[0112] 3.3 Calculate the undulating height qif_z of the area where micro-topography features are to be identified, that is, the difference between the average altitude of the three highest points and the average altitude of the three lowest points.

[0113] 3.4 If the average height ave_z is less than 200 meters and the undulating height qif_z is less than 50 meters, then this area is a plain area and does not belong to the micro-topography area, and the identification is stopped. If the average height ave_z is greater than 200 meters but the undulating height qif_z is less than 50 meters, then this area is an open area and also does not belong to the micro-topography area, and the identification is stopped.

[0114] (4) Micro-topography judgment based on spatial feature clustering analysis

[0115] 4.1 For the area where micro-topography features are to be identified after preliminary screening, standardize the matrix Z(100, 100).

[0116] 4.2 Calculate the second distance Dn between the matrix Z(100, 100) and the average value matrix ave_Hn(100, 100) of the spatial distributions of N types of typical micro-topography regions.

[0117] If Dx is the smallest among N values and Dx does not exceed k times ave_Ln, it is determined that the area to be identified for microtopographic features belongs to the microtopography corresponding to the matrix ave_Hn(100, 100). The value of k is the same as that in step 2.5

[0118] If Dx is the smallest among N values, but the distances from all N types of typical microtopographic regions exceed k times ave_Ln, it is determined that the area to be identified for microtopographic features does not belong to any of the currently divided categories, and these microtopographic points are classified into other categories.

[0119] 4.3 Process all the standardized height data matrices in other categories through steps 2.4 and 2.5 to confirm whether new typical microtopographic types can be extracted.

[0120] 4.4 Divide all the areas to be identified for microtopographic features into corresponding groups, and the identification ends.

[0121] Moreover, the present invention also provides a power grid microtopography recognition system based on spatial feature clustering analysis. The power grid microtopography recognition system based on spatial feature clustering analysis includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, the steps of the power grid microtopography recognition method based on spatial feature clustering analysis as described above are implemented.

[0122] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0123] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected to", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection, an electrical connection, or communicable with each other; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the internal connection of two components or the interaction relationship between two components, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0125] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for identifying micro-topography of power grid based on spatial feature clustering analysis, characterized in that The grid micro-topography recognition method based on spatial feature clustering analysis includes: Obtaining grid discretized terrain height data of at least one known micro-topography type area; Obtaining a height data matrix of the known micro-topography type area according to the grid discretized terrain height data of the known micro-topography type area; Obtaining the typical spatial distribution characteristics of the known micro-topography area according to the height data matrix of the known micro-topography type area; Obtaining the terrain height data of the area to be recognized, and obtaining a height data matrix of the area to be recognized according to the terrain height data of the area to be recognized; Performing preliminary screening of the micro-topography area according to the height data matrix of the area to be recognized, and obtaining a preliminarily recognized micro-topography area; Performing re-identification of the preliminarily recognized micro-topography area by comparing the height data matrix of the preliminarily recognized micro-topography area with the typical spatial distribution characteristics of the known micro-topography type area; The step of obtaining the typical spatial distribution characteristics of the known micro-topography area according to the height data matrix of the known micro-topography type area includes: Performing standardization processing on the height data matrix of the known micro-topography type area, and obtaining a standardized height data matrix of the known micro-topography type area; Merging the standardized height data matrices of the known micro-topography type areas of the same type, and obtaining a terrain height data matrix of each type of known micro-topography area; Calculating the matrix average distribution and average value matrix of the micro-topography areas of the same type; Obtaining the typical spatial distribution characteristics of the known micro-topography type area according to the standardized height matrix and the average value matrix of each micro-topography point in the terrain height data matrix of the micro-topography area of the same type.

2. The method for identifying power grid microtopography based on spatial feature clustering analysis according to claim 1, wherein The step of obtaining the typical spatial distribution characteristics of the known micro-topography type point according to the standardized height matrix and the average value matrix of each micro-topography area in the terrain height data matrix of the micro-topography area of the same type includes: Calculating the first distance between the standardized height matrix of each micro-topography point in the terrain height data matrix of the micro-topography area of the same type and the average value matrix; Calculating the average distance of all micro-topography points in the micro-topography area of the same type; Comparing the first distance and the average distance; When the maximum first distance is greater than a preset multiple of the average distance, the micro-topography point belonging to the maximum first distance is removed from the terrain height data matrix of the micro-topography area of the same type; When the maximum first distance is less than or equal to a preset multiple of the average distance, the micro-topography point belonging to the maximum first distance is retained in the terrain height data matrix of the micro-topography area of the same type.

3. The method for identifying micro-topography of a power grid based on spatial feature clustering analysis according to claim 2, wherein The first distance can be calculated by the following calculation formula: Among them, Lnx is the first distance between the standardized height matrix and the average value matrix of a micro-topography area in the terrain height data matrix of micro-topography areas of the same type; M is the amount of grid point data of the selected micro-topography area; h i,j is the grid point value in the height data matrix of the known micro-topography type area.

4. The method for identifying micro-topography of power grid based on spatial feature clustering analysis according to claim 1, wherein The step of performing preliminary screening of the micro-topography area according to the height data matrix of the area to be recognized, and obtaining a preliminarily recognized micro-topography area includes: Calculating the average height and undulating height of the area to be recognized; Preliminarily recognizing the area to be recognized according to the average height and undulating height of the area to be recognized; When the average height is less than 200 meters and the undulating height is less than 50 meters, or the average height is greater than 200 meters and the undulating height is less than 50 meters, it is preliminarily recognized that the area to be recognized does not belong to the micro-topography area, and the recognition ends.

5. The method for identifying micro-topography of a power grid based on spatial feature clustering analysis according to claim 2, wherein The step of re-identifying the preliminarily identified micro-topography area by comparing the height data matrix of the preliminarily identified micro-topography area with the typical spatial distribution characteristics of the known micro-topography type areas includes: Performing standardization processing on the height data matrix of the preliminarily identified micro-topography area to obtain the standardized height data matrix of the preliminarily identified micro-topography area; Obtaining a second distance between the standardized height data matrix of the preliminarily identified micro-topography area and the average value matrix of the spatial distribution of the known micro-topography type areas; Re-identifying the preliminarily identified micro-topography area according to the relationship between the second distance and the average distance.

6. The method for identifying micro-topography of a power grid based on spatial feature clustering analysis according to claim 5, wherein The step of re-identifying the preliminarily identified micro-topography area according to the relationship between the second distance and the average distance includes: When the second distance is less than or equal to a preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area belongs to the corresponding micro-topography in the known micro-topography type areas; When the second distance is greater than the preset multiple of the average distance, it is determined that the preliminarily identified micro-topography area does not belong to the corresponding micro-topography in the known micro-topography type areas.

7. The method for identifying micro-topography of power grid based on spatial feature clustering analysis according to claim 6, characterized in that After the step of when the second distance exceeds the preset multiple of the average distance and it is determined that the preliminarily identified micro-topography area does not belong to the corresponding micro-topography in the known micro-topography type areas, further includes: Processing the standardized height data matrix of the preliminarily identified micro-topography area that does not belong to the known micro-topography type area to confirm whether a new micro-topography type can be extracted.

8. The method for identifying micro-topography of a power grid based on spatial feature clustering analysis according to any one of claims 1 to 7, characterized in that, The power grid micro-topography identification method based on spatial feature clustering analysis further includes grouping the area identification results of the area to be identified, and the identification ends.

9. A power grid micro-topography recognition system based on spatial feature clustering analysis, characterized in that The power grid micro-topography identification system based on spatial feature clustering analysis includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the steps of the power grid micro-topography identification method according to any one of claims 1 to 8 above.

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