Microtopography judgment method and system based on inverse distance weighting method and decision tree

Abstract

The invention discloses a microtopography determination method and system based on an inverse distance weighting method and a decision tree, and relates to the technical field of microtopography determination, and the method comprises the steps: filtering invalid values and abnormal values in original elevation data through employing a filtering function and a matrix recording method; calculating large and small window terrain indexes based on the data matrix, and calculating to obtain corresponding weighted terrain indexes according to the large and small window terrain indexes; according to a preset terrain judgment rule set, generating a judgment decision tree, and based on the neighborhood terrain reference matrix, the neighborhood terrain gradient matrix and the weighted terrain index, utilizing the judgment decision tree to generate micro-terrain classification data of the micro-terrain judgment area; obtaining a terrain classification matrix after classification of the micro-terrain judgment area by using a preset terrain classifier and through the micro-terrain classification data; the problems that an original microtopography classification method is greatly influenced by the calculation window radius, the microtopography classification result is unstable, and the icing thickness prediction precision is not high are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of micro-topography determination, and more specifically, to a micro-topography determination method and system based on an inverse distance weighted method and a decision tree. Background Art

[0002] Microtopography refers to the type of surface undulations within a small area. With the deepening of research, microtopography has gradually been deeply applied in the fields of landscape classification, terrain feature identification, etc., and its scientificity and usability have been verified in different professions. In the practice of power grid icing observation and ice thickness warning, it is found that the development of ice and microtopography show a relatively obvious correlation. Therefore, microtopography can be used as a feasible indicator for icing prediction. However, in the practice of icing prediction, it is found that the original microtopography index cannot predict the icing value well, and the error between the predicted value and the actual value is large. It is necessary to make appropriate adjustments to the original terrain index in order to make a more scientific and reasonable icing prediction. Summary of the invention

[0003] The purpose of the present invention is to provide a micro-topography determination method and system based on the inverse distance weighted method and decision tree. The determination method aims to solve the problems that the original micro-topography classification method is greatly affected by the calculation window radius, the micro-topography classification results are unstable, and the ice thickness prediction accuracy is not high. It has been verified that the method can improve the accuracy of micro-topography classification and ice cover value prediction, and can be promoted and applied in ice cover value prediction.

[0004] The above technical objectives of the present invention are achieved through the following technical solutions: In a first aspect, the present application provides a micro-topography determination method based on an inverse distance weighted method and a decision tree, comprising the following specific steps: The original elevation data of the micro-topography determination area is obtained, and the invalid values ​​and abnormal values ​​in the original elevation data are filtered out by using a filter function and a matrix notation to obtain a data matrix corresponding to the original elevation data; Based on the data matrix, the large and small window terrain indexes of the micro-topography determination area are calculated, and the corresponding weighted terrain indexes are calculated according to the large and small window terrain indexes; Based on the data matrix and the large and small window terrain index, the neighborhood terrain reference matrix and neighborhood terrain slope matrix of the micro-topography determination area are calculated; A decision tree is generated according to a preset terrain determination rule set, and based on a neighborhood terrain reference matrix and a neighborhood terrain slope matrix, as well as a normalized weighted terrain index, micro-topography classification data of a micro-topography determination area is generated using the decision tree; By using the preset terrain classifier and the micro-terrain classification data, a terrain classification matrix after the micro-terrain determination area classification is obtained.

[0005] Based on the above technical solution, the present invention can also be improved as follows.

[0006] Furthermore, the above original elevation data is specifically: ; In the formula, , Respectively represent the length and width of the micro-topography determination area, is the size of the grid. The grid uses an equilateral grid. and Respectively represent the number of grid divisions in the length and width directions of the micro-topography determination area, Representing a grid The height value of .

[0007] Furthermore, the above-mentioned large and small window terrain indexes include a large window terrain index and a small window terrain index. The large window terrain index is specifically: ; In the formula, represents the large window terrain index, Indicates is the annular neighborhood window with the center coordinates, Indicates the elevation of the window center point. represents the total number of grids in the annular neighborhood window, represents the grid elevation within the window, and , express The radius of .

[0008] Furthermore, the above small window terrain index is specifically: ; In the formula, represents the small window terrain index, Indicates The coordinates of the rectangular window centered at Indicates the elevation of the window center point. Indicates the total number of grids in the small window, represents the grid elevation within the window, and , for The radius of .

[0009] Furthermore, the above weighted terrain index is specifically: ,in, , ; In the formula, represents the weighted terrain index, Represents the size window terrain index, represents the weight matrix, Take 2, The distance between the current point and the specified point.

[0010] Furthermore, the above neighborhood terrain reference matrix is ​​specifically: ,in, ; In the formula, represents the neighborhood terrain reference matrix, Indicates the row and column number corresponding to the window. Indicates the cell elevation value within the window.

[0011] Furthermore, the above neighborhood terrain slope matrix is ​​specifically: ,in, ; In the formula, represents the neighborhood terrain slope matrix, Indicates the row and column number corresponding to the window. Represents the slope value of non-core cells relative to core cells in the window.

[0012] In a second aspect, the present application provides a micro-terrain determination system based on an inverse distance weighted method and a decision tree, which is applied to a micro-terrain determination method based on an inverse distance weighted method and a decision tree in any one of the first aspects, comprising: The first module is used to obtain the original elevation data of the micro-topography determination area, and filter the invalid values ​​and abnormal values ​​in the original elevation data by using the filter function and matrix notation to obtain the data matrix corresponding to the original elevation data; The second module is used to calculate the large and small window terrain indexes of the micro-topography determination area based on the data matrix, and calculate the corresponding weighted terrain index according to the large and small window terrain indexes; The third module is used to calculate the neighborhood terrain reference matrix and neighborhood terrain slope matrix of the micro-topography determination area based on the data matrix and the large and small window terrain index; The fourth module is used to generate a determination decision tree according to a preset terrain determination rule set, and generate micro-topography classification data of the micro-topography determination area using the determination decision tree based on the neighborhood terrain reference matrix and the neighborhood terrain slope matrix, and the normalized weighted terrain index; The fifth module is used to obtain a terrain classification matrix after the micro-topography determination area is classified by using a preset terrain classifier and through micro-topography classification data.

[0013] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the methods in the first aspect when executing the computer program.

[0014] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any one of the methods in the first aspect.

[0015] Compared with the prior art, the present invention has at least the following beneficial effects: In the present application, a micro-topography determination method based on the inverse distance weighted method and the decision tree is adopted. Through multiple filtering processes, the influence of invalid values ​​and outliers is eliminated. The weighted terrain index of large and small windows is used to weaken the influence of the calculation window radius size on the classification of the terrain index. The terrain classification effect can be made more reasonable, and the problems such as the original micro-topography classification method being greatly affected by the calculation window radius, the unstable micro-topography classification results, and the low accuracy of ice thickness prediction are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 A method flow chart of a micro-topography determination method in an embodiment of the present invention; Figure 2 is a flow chart of a determination method in an embodiment of the present invention; Figure 3 Schematic diagram of original elevation data in an embodiment of the present invention; Figure 4 It is a schematic diagram of a large window weighted terrain index in the weighted terrain index in an embodiment of the present invention; Figure 5 A schematic diagram of a small window weighted terrain index in a weighted terrain index in an embodiment of the present invention; Figure 6 is the standard deviation of the large window weighted terrain index in the embodiment of the present invention; Figure 7 is the standard deviation of the small window weighted terrain index in the embodiment of the present invention; Figure 8 A schematic diagram of the slope of the micro-topography determination area in an embodiment of the present invention; Fig. 9 A schematic diagram of a decision tree according to an embodiment of the present invention; Fig.10 Schematic diagram of the terrain classification matrix after the micro-topography determination area is classified in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0018] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0020] In the description of the embodiments of the present invention, "plurality" means at least 2.

[0021] Embodiment 1: In order to solve the problems that the original micro-topography classification method is greatly affected by the calculation window radius, the micro-topography classification result is unstable, and the ice thickness prediction accuracy is not high, this embodiment provides a micro-topography determination method based on the inverse distance weighted method and the decision tree, such as Figure 1 and Figure 2 As shown, the following specific steps are included: S1, obtaining the original elevation data of the micro-topography determination area, using the filter function and matrix notation to filter out the invalid values ​​and abnormal values ​​in the original elevation data, and obtaining the data matrix corresponding to the original elevation data.

[0022] Optionally, the above original elevation data is specifically: ; In the formula, , Respectively represent the length and width of the micro-topography determination area, is the size of the grid. The grid uses an equilateral grid. and Respectively represent the number of grid divisions in the length and width directions of the micro-topography determination area, Representing a grid The height value of .

[0023] Among them, the original elevation data of the micro-topography determination area obtained, the grid size can be set to 12.5m, and the mean filter function is selected for filtering, such as Figure 3 As shown in Figure 1, the original elevation data is obtained after filtering.

[0024] S2, based on the data matrix, calculate the large and small window terrain indexes of the micro-topography determination area, and calculate the corresponding weighted terrain index according to the large and small window terrain indexes.

[0025] Optionally, the above-mentioned large and small window terrain indexes include a large window terrain index and a small window terrain index, and the large window terrain index is specifically: ; In the formula, represents the large window terrain index, Indicates is the annular neighborhood window with the center coordinates, Indicates the elevation of the window center point. represents the total number of grids in the annular neighborhood window, represents the grid elevation within the window, and , express The radius of .

[0026] Optionally, the small window terrain index is specifically: ; In the formula, represents the small window terrain index, Indicates The coordinates of the rectangular window centered at Indicates the elevation of the window center point. Indicates the total number of grids in the small window, represents the grid elevation within the window, and , for The radius of .

[0027] Among them, in the large and small window terrain position index, the radius of the large window annular neighborhood can be set to 10, and the radius of the small window rectangular neighborhood can be set to 5, and then the corresponding weighted terrain index is calculated respectively through the determined weighting function; Figure 4 and Figure 5 As shown, they respectively represent the processing results after being processed by the weighted function.

[0028] Optionally, the weighted terrain index is specifically: ,in, , ; In the formula, represents the weighted terrain index, Represents the size window terrain index, represents the weight matrix, Take 2, The distance between the current point and the specified point.

[0029] Specifically, the weighted terrain index can also be normalized, that is, the corresponding standard deviation is calculated. The standard deviation corresponding to the weighted terrain index is as follows: Figure 6 and Figure 7 shown.

[0030] S3, based on the data matrix and the large and small window terrain indices, the neighborhood terrain reference matrix and the neighborhood terrain slope matrix of the micro-topography determination area are calculated.

[0031] Optionally, the above neighborhood terrain reference matrix is ​​specifically: ,in, ; In the formula, represents the neighborhood terrain reference matrix, Indicates the row and column number corresponding to the window. Indicates the cell elevation value within the window.

[0032] Optionally, the above neighborhood terrain slope matrix is ​​as follows Figure 8 As shown, specifically: ,in, ; In the formula, represents the neighborhood terrain slope matrix, Indicates the row and column number corresponding to the window. Represents the slope value of non-core cells relative to core cells in the window.

[0033] S4, generating a determination decision tree according to a preset terrain determination rule set, and generating micro-topography classification data of the micro-topography determination area using the determination decision tree based on a neighborhood terrain reference matrix and a neighborhood terrain slope matrix, as well as a normalized weighted terrain index.

[0034] Among them, the decision tree is generated by designing micro-topography classification judgment rules, such as Fig. 9 As shown, the data obtained above are used to generate micro-topography type distribution data.

[0035] S5, using the preset terrain classifier and the micro-terrain classification data, a terrain classification matrix after the micro-terrain determination area is classified is obtained; specifically, the final micro-terrain classification result is as follows: Fig.10 shown.

[0036] Embodiment 2: In order to solve the problems that the original micro-topography classification method is greatly affected by the calculation window radius, the micro-topography classification result is unstable, and the ice thickness prediction accuracy is not high, this embodiment provides a micro-topography determination method based on inverse distance weighted method and decision tree, such as Figure 2 As shown, the following specific steps are included: S1. The original elevation data of the micro-topography determination area is , then we can remember for: ; In the formula, , Respectively represent the length and width of the micro-topography determination area, is the size of the grid. The grid uses an equilateral grid. and Respectively represent the number of grid divisions in the length and width directions of the micro-topography determination area, Representing a grid The height value of .

[0037] S2, select the filter function, use matrix notation, and The filtering operation can be written as: ; in, It is the data matrix of the gridded DEM after filtering. is the filter function, It is the matrix expression of the aforementioned original elevation dataset.

[0038] S3. Calculate the size window terrain position index and , the calculation formula is as follows: ; In the formula, represents the large window terrain index, Indicates is the annular neighborhood window with the center coordinates, Indicates the elevation of the window center point. represents the total number of grids in the annular neighborhood window, represents the grid elevation within the window, and , express The radius of the small window is: ; In the formula, represents the small window terrain index, Indicates The coordinates of the rectangular window centered at Indicates the elevation of the window center point. Indicates the total number of grids in the small window, represents the grid elevation within the window, and , for The radius of .

[0039] S4. Determine the weighting function and calculate the weighted terrain index , using matrix notation as follows: ; in is the weight matrix, which can be calculated as follows: ; in, , Usually take 2, The distance between the current point and the specified point.

[0040] S5. Calculate the normalized terrain position index: ;in is a normalization function.

[0041] S6. Calculate neighborhood terrain benchmark and the slope of the neighboring terrain .

[0042] The above neighborhood terrain reference matrix is ​​specifically: ,in, ; In the formula, represents the neighborhood terrain reference matrix, Indicates the row and column number corresponding to the window. Indicates the cell elevation value within the window.

[0043] Optionally, the above neighborhood terrain slope matrix is ​​as follows Figure 8 As shown, specifically: ,in, ; In the formula, represents the neighborhood terrain slope matrix, Indicates the row and column number corresponding to the window. Represents the slope value of non-core cells relative to core cells in the window.

[0044] S7. Based on the micro-topography definition, design a set of judgment rules and generate a decision tree ,like Fig. 9 As shown, using the above obtained , , , use decision trees to generate micro-topography classification data. It can be expressed as: ; in, is a preset terrain classifier, is the filter function, is the terrain classification matrix output after classification, such as Fig.10 shown.

[0045] Embodiment 3: The embodiment of the present application provides a micro-terrain determination system based on an inverse distance weighted method and a decision tree, which is applied to a micro-terrain determination method based on an inverse distance weighted method and a decision tree in any one of Embodiment 1, comprising: The first module is used to obtain the original elevation data of the micro-topography determination area, and use the filter function and matrix notation to filter out the invalid values ​​and abnormal values ​​in the original elevation data to obtain the data matrix corresponding to the original elevation data.

[0046] The second module is used to calculate the large and small window terrain indexes of the micro-topography determination area based on the data matrix, and calculate the corresponding weighted terrain index according to the large and small window terrain indexes.

[0047] The third module is used to calculate the neighborhood terrain reference matrix and neighborhood terrain slope matrix of the micro-topography determination area based on the data matrix and the large and small window terrain indexes.

[0048] The fourth module is used to generate a determination decision tree according to a preset terrain determination rule set, and based on the neighborhood terrain reference matrix and the neighborhood terrain slope matrix, as well as the normalized weighted terrain index, use the determination decision tree to generate micro-topography classification data of the micro-topography determination area.

[0049] The fifth module is used to obtain a terrain classification matrix after the micro-topography determination area is classified by using a preset terrain classifier and through micro-topography classification data.

[0050] Embodiment 4: The embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method of any one of Embodiment 1 or Embodiment 2 is implemented.

[0051] Embodiment 5: The embodiment of the present application provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions enable a computer to execute any method in Embodiment 1 or Embodiment 2.

[0052] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A micro-topography determination method based on inverse distance weighted method and decision tree, characterized in that: The specific steps include: Acquire the original elevation data of the micro-topography determination area, filter invalid values ​​and abnormal values ​​in the original elevation data by using a filter function and matrix notation, and obtain a data matrix corresponding to the original elevation data; Based on the data matrix, the large and small window terrain indexes of the micro-topography determination area are calculated, and the corresponding weighted terrain indexes are calculated according to the large and small window terrain indexes; Based on the data matrix and the large and small window terrain indexes, a neighborhood terrain reference matrix and a neighborhood terrain slope matrix of the micro-topography determination area are calculated; Generate a decision tree according to a preset terrain determination rule set, and generate micro-topography classification data of a micro-topography determination area using the decision tree based on the neighborhood terrain reference matrix, the neighborhood terrain slope matrix, and the normalized weighted terrain index; A terrain classification matrix after classification of the micro-topography determination area is obtained by using a preset terrain classifier and the micro-topography classification data.

2. The micro-topography determination method based on inverse distance weighted method and decision tree according to claim 1 is characterized in that: The original elevation data is specifically: ； In the formula, , Respectively represent the length and width of the micro-topography determination area, is the size of the grid. The grid uses an equilateral grid. and Respectively represent the number of grid divisions in the length and width directions of the micro-topography determination area, Representing a grid The height value of .

3. The micro-topography determination method based on inverse distance weighted method and decision tree according to claim 1 is characterized in that: The large and small window terrain indexes include a large window terrain index and a small window terrain index. The large window terrain index is specifically: ； In the formula, represents the large window terrain index, Indicates is the annular neighborhood window with the center coordinates, Indicates the elevation of the window center point. represents the total number of grids in the annular neighborhood window, represents the grid elevation within the window, and , express The radius of .

4. The micro-topography determination method based on inverse distance weighted method and decision tree according to claim 3 is characterized in that: The small window terrain index is specifically: ； In the formula, represents the small window terrain index, Indicates The coordinates of the rectangular window centered at Indicates the elevation of the window center point. Indicates the total number of grids in the small window, represents the grid elevation within the window, and , for The radius of .

5. A micro-topography determination method based on inverse distance weighted method and decision tree according to claim 3 or 4, characterized in that: The weighted terrain index is specifically: ,in, , ; In the formula, represents the weighted terrain index, Represents the size window terrain index, represents the weight matrix, Take 2, The distance between the current point and the specified point.

6. The micro-topography determination method based on inverse distance weighted method and decision tree according to claim 1 is characterized in that: The neighborhood terrain reference matrix is ​​specifically: ,in, ; In the formula, represents the neighborhood terrain reference matrix, Indicates the row and column number corresponding to the window. Indicates the cell elevation value within the window.

7. The micro-topography determination method based on inverse distance weighted method and decision tree according to claim 1 is characterized in that: The neighborhood terrain slope matrix is ​​specifically: ,in, ; In the formula, represents the neighborhood terrain slope matrix, Indicates the row and column number corresponding to the window. Represents the slope value of non-core cells relative to core cells in the window.

8. A micro-terrain determination system based on inverse distance weighted method and decision tree, applied to a micro-terrain determination method based on inverse distance weighted method and decision tree as claimed in any one of claims 1 to 7, characterized in that: include: The first module is used to obtain the original elevation data of the micro-topography determination area, and filter the invalid values ​​and abnormal values ​​in the original elevation data by using a filter function and a matrix notation to obtain a data matrix corresponding to the original elevation data; The second module is used to calculate the large and small window terrain indexes of the micro-topography determination area based on the data matrix, and calculate the corresponding weighted terrain index according to the large and small window terrain indexes; The third module is used to calculate the neighborhood terrain reference matrix and the neighborhood terrain slope matrix of the micro-topography determination area based on the data matrix and the large and small window terrain indexes; The fourth module is used to generate a determination decision tree according to a preset terrain determination rule set, and based on the neighborhood terrain reference matrix and the neighborhood terrain slope matrix, and the normalized weighted terrain index, use the determination decision tree to generate micro-topography classification data of the micro-topography determination area; The fifth module is used to obtain a terrain classification matrix after the micro-terrain determination area is classified by using a preset terrain classifier and the micro-terrain classification data.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the method according to any one of claims 1 to 7 is implemented when the processor executes the computer program.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which enable a computer to execute the method of any one of claims 1-7.

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