Slope unit optimization method, system and equipment and computer readable storage medium

Abstract

The embodiment of the invention provides a slope unit optimization method, system and device and a computer readable storage medium, and the method comprises the steps: dividing an obtained digital terrain, and obtaining initial slope units; calculating an object consistency error and a comprehensive heterogeneity score based on the reference slope unit and the initial slope unit; screening the initial slope units through the object consistency error and the comprehensive heterogeneity score, and determining a plurality of slope units to be selected; determining the local average similarity of each to-be-selected slope unit in the plurality of to-be-selected slope units; based on the local average similarity, classifying the plurality of to-be-selected slope units, and determining to-be-optimized slope units; and carrying out subdivision or combination processing on the to-be-optimized slope unit, and determining a final slope unit. According to the scheme, the unreasonable slope units are optimized, and the division error can be reduced, so that the division accuracy of the slope units is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geographic information, in particular to a slope unit optimization method, system, device and computer readable storage medium. BACKGROUND

[0002] To make reliable landslide risk early warning in high mountain and hilly areas, firstly, appropriate evaluation units must be selected to extract geological environmental data. Common evaluation units include grid units, slope units, landform units and unique condition units.

[0003] The geographical and topographical conditions of high mountain and hilly areas in different regions are obviously different, resulting in different shape characteristics of slope units. Fixed scales can only achieve the subdivision of landforms with certain area and shape characteristics, so that when the automatic delimitation method is used, the slope unit optimization technology still often divides unreasonable slope units and the accuracy of the divided slope units is poor. SUMMARY

[0004] The embodiments of the present application expect to provide a slope unit optimization method, system, device and computer readable storage medium, which can improve the accuracy of slope unit division.

[0005] The technical solution of the present application is as follows: In a first aspect, the embodiments of the present application provide a slope unit optimization method, which comprises: dividing the obtained digital terrain to obtain initial slope units; calculating object consistency error and comprehensive heterogeneity score based on reference slope units and the initial slope units, and screening the initial slope units through the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units; determining the local average similarity of each candidate slope unit in the plurality of candidate slope units; classifying the plurality of candidate slope units based on the local average similarity to determine a slope unit to be optimized; performing subdivision or merging processing on the slope unit to be optimized to determine a final slope unit.

[0006] In the above scheme, the calculation of object consistency error and comprehensive heterogeneity score based on reference slope units and the initial slope units, and the screening of the initial slope units through the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units comprises: determining object consistency error based on the reference slope units and the initial slope units; screening the initial slope unit to determine a candidate slope unit according to the object consistency error; calculating a comprehensive heterogeneity score of the candidate slope unit, and screening the candidate slope unit according to the comprehensive heterogeneity score to determine the plurality of slope units to be selected.

[0007] In the above scheme, the local average similarity of each slope unit to be selected in the plurality of slope units to be selected is determined by: For each slope unit to be selected in the plurality of slope units to be selected, the internal similarity and the adjacent difference degree of the slope unit to be selected are determined; Based on the internal similarity and the adjacent difference degree, the local heterogeneity and the local uniformity of the slope unit to be selected are determined; The local heterogeneity and the local uniformity are normalized to determine the local average similarity of the slope unit to be selected.

[0008] In the above scheme, the local heterogeneity and the local uniformity of each slope unit to be selected are determined based on the internal similarity and the adjacent difference degree, comprising: The area value and the boundary value of the slope unit to be selected are determined; Based on the adjacent difference degree and the area value, the local heterogeneity of the slope unit to be selected is determined; Based on the internal similarity and the boundary value, the local uniformity of the slope unit to be selected is determined.

[0009] In the above scheme, the plurality of slope units to be selected are classified based on the local average similarity to determine the slope unit to be optimized, comprising: For each slope unit to be selected in the plurality of slope units to be selected, if the local average similarity is less than a first similarity threshold and the area value is greater than an area threshold, the slope unit to be selected is determined as a first unreasonable slope unit; wherein the first unreasonable slope unit represents that the refinement degree of the current slope unit is less than a preset refinement threshold; If the local average similarity is greater than a second similarity threshold and the boundary value is less than a boundary threshold, the slope unit to be selected is determined as a second unreasonable slope unit; wherein the second unreasonable slope unit represents that the refinement degree of the current slope unit is greater than a preset refinement threshold; The first unreasonable slope unit and the second unreasonable slope unit are determined as the slope unit to be optimized.

[0010] In the above scheme, the slope unit to be optimized is subdivided or merged to determine the final slope unit, comprising: If the to-be-optimized slope unit is a first unreasonable slope unit, the to-be-optimized slope unit is subdivided by a slope unit with the highest comprehensive heterogeneity score to determine the final slope unit; If the to-be-optimized slope unit is a second unreasonable slope unit, the to-be-optimized slope unit is merged based on local average similarity and local heterogeneity of the to-be-optimized slope unit to obtain a merged slope unit; The internal uniformity of the merged slope unit and the local average similarity of the merged slope unit are determined. In a case where the local average similarity of the merged slope unit is less than a local similarity threshold and the internal uniformity of the merged slope unit is greater than an internal uniformity threshold, the merged slope unit is determined as the final slope unit.

[0011] In the above scheme, the operation based on the reference slope unit and the initial slope unit to determine the object consistency error comprises: The operation based on the reference slope unit and the initial slope unit to determine a first partial error and a second partial error; wherein the first partial error is a partial error of the reference slope unit and the initial slope unit; and the second partial error is a partial error of the initial slope unit and the reference slope unit. The object consistency error is determined based on the first partial error and the second partial error. And / or, The calculation of the comprehensive heterogeneity score of the candidate slope unit comprises: The global Moran's index and the global variance of the to-be-selected slope unit are calculated. The comprehensive heterogeneity score is determined based on the global Moran's index and the global variance.

[0012] In a second aspect, the embodiments of the present application provide a slope unit optimization system, comprising a division unit, a calculation unit and a determination unit, wherein, The division unit is configured to divide an acquired digital terrain to obtain an initial slope unit. The calculation unit is configured to calculate an object consistency error and a comprehensive heterogeneity score based on a reference slope unit and the initial slope unit. The determining unit is configured to: screen the initial slope unit based on the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units; determine a local average similarity of each candidate slope unit in the plurality of candidate slope units; classify the plurality of candidate slope units based on the local average similarity to determine a slope unit to be optimized; and perform subdivision or merging processing on the slope unit to be optimized to determine a final slope unit.

[0013] In a third aspect, an embodiment of the present application provides a slope unit optimization device, which comprises a processor and a memory, wherein The memory is configured to store a computer program. The processor is configured to call and run the computer program from the memory to execute the method according to the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium storing executable instructions for causing a processor to execute the method according to the first aspect.

[0015] The embodiments of the present application provide a slope unit optimization method, system, device and computer readable storage medium. The method comprises: dividing an acquired digital terrain to obtain an initial slope unit; calculating an object consistency error and a comprehensive heterogeneity score based on a reference slope unit and the initial slope unit; screening the initial slope unit based on the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units; determining a local average similarity of each candidate slope unit in the plurality of candidate slope units; classifying the plurality of candidate slope units based on the local average similarity to determine a slope unit to be optimized; and performing subdivision or merging processing on the slope unit to be optimized to determine a final slope unit. In the above solution, on the one hand, the object consistency error and the comprehensive heterogeneity score are calculated based on the reference slope unit and the initial slope unit, and the initial slope unit is screened based on the object consistency error and the comprehensive heterogeneity score, so that unreasonable slope units can be removed, and the rationality of slope unit division can be improved. On the other hand, the local average similarity of each candidate slope unit in the plurality of candidate slope units is determined, and the plurality of candidate slope units are classified based on the local average similarity to determine the slope unit to be optimized, and the subdivision or merging processing is performed on the slope unit to be optimized to determine the final slope unit. In this process, the unreasonable slope units are optimized, the division error can be reduced, and the accuracy of slope unit division can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings shown in the accompanying drawings are incorporated into the description and form part of the description, which show the embodiments consistent with the present application, and together with the description serve to explain the technical solutions of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0017] The flowchart shown in the drawings is only an exemplary description, and is not necessarily to include all contents and operations / steps, and is not necessarily to be executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so that the actual execution order can be changed according to the actual situation.

[0018] Figure 1 An optional flowchart of a slope unit optimization method is provided for the embodiments of the present application Figure 1 ; Figure 2 An optional flowchart of a slope unit optimization method is provided for the embodiments of the present application Figure 2 Figure 3 An optional flowchart of a slope unit optimization method is provided for the embodiments of the present application Figure 3 ; Figure 4 A structural diagram of a slope unit optimization system is provided for the embodiments of the present application. Figure 5 A structural diagram of a slope unit optimization device is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0019] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the specific technical solutions of the present application will be further described in detail below in combination with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application, but are not used to limit the scope of the present application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0021] In the following description, "some embodiments", "the embodiment", "the embodiments of the present application" and the like are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict.

[0022] If the application file contains similar descriptions such as "first / second", the following description is added: in the following description, the terms "first\second\third" are only used to distinguish similar objects, and do not represent a specific order of the objects. Understandably, "first\second\third" can be interchanged in a specific order or sequence as allowed, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein.

[0023] Based on this, the embodiments of the present application provide a slope unit optimization method, Figure 1 An optional flowchart of a slope unit optimization method is provided for the embodiments of the present application Figure 1 The steps shown will be described. Figure 1

[0024] S101, divide the obtained digital terrain to obtain initial slope units.

[0025] In the embodiments of the present application, the initial slope unit is the initially divided slope unit. The shaping process of the slope unit is associated with the natural morphological features, thereby reflecting the physical relationship between the landslide and the geological environment information. The acquisition and application of the slope unit are attracting more and more attention. According to the traditional hydrological principle, the slope unit is defined as the intersection area of the drainage ridge line and the catchment valley line. The ridge line is the boundary of the watershed, and the area between the two ridge lines is the catchment area. Extracting the valley line can divide the watershed into two slope units.

[0026] In some embodiments of the present application, from the perspective of geomorphology, the slope unit not only corresponds to a single slope, but also can correspond to multiple slopes or even the entire watershed. In the basic hydrological analysis process, new definitions and restrictions are added to the slope unit, i.e. the slope unit is considered as an area that is obviously different from the adjacent area in terms of terrain features. In addition to the ridge line and the valley line, the elevation, slope and curvature, etc. are also used as the boundary line.

[0027] In some embodiments of the present application, the slope unit optimization method is suitable for various terrain scenes for extracting geological environment data.

[0028] In some embodiments of the present application, the execution subject of the slope unit optimization method is a slope unit optimization device. The slope unit optimization device can be a server or a terminal device, and the embodiments of the present application do not make specific limitations on this.

[0029] ​In some embodiments of the present application, the digital terrain is divided by a digital elevation model to obtain at least two initial basin units; based on an initial flow accumulation area threshold and a reduction factor, the at least two initial basin units are respectively continuously divided to obtain sub-basin basins of the at least two initial basin units; and in a case where the sub-basin basins meet preset conditions, initial slope units are determined; wherein the preset conditions are that the sub-basin basins meet minimum surface areas and minimum circular variances.

[0030] In some embodiments of the present application, the initial basin units are several larger half-basins obtained by dividing the digital terrain.

[0031] The digital elevation model is a digital model for storing the ground elevation information in the form of a regular grid or a triangular net, and is a core component of the digital terrain model. Its core features and applications are as follows: It should be noted that the digital elevation model mathematically represents the terrain surface by a discrete elevation point array, and only contains elevation information (Z value), while the digital terrain model can contain landform attributes such as slope and slope direction. The data structure of the digital elevation model includes three main forms of regular grid (Grid), irregular triangular net (TIN) and contour line model, among which the regular grid is the most widely used due to its convenience for calculation and analysis.

[0032] For example, the r.slopeunits v1.0 software has a wide range of uses in capturing the slope changes of the landform, and therefore is used to obtain a plurality of sets of data for the preliminary division of the slope units. In a case where a given digital elevation model and some input parameters are given, the algorithm first divides the digital terrain into several larger half-basins, i.e., initial basin units.

[0033] In some embodiments of the present application, the sub-basin basin is a region obtained by further dividing the initial basin unit.

[0034] In some embodiments of the present application, if the at least two initial basin units do not meet the preset conditions, the at least two initial basin units are respectively divided based on the initial flow accumulation area threshold and the reduction factor to obtain initial sub-basin basins of each initial basin unit; the initial flow accumulation area threshold is updated to determine an updated initial flow accumulation area threshold; and the initial sub-basin basins are continuously divided based on the updated initial flow accumulation area threshold and the reduction factor to obtain sub-basin basins of the at least two initial basin units.

[0035] For example, the flow accumulation area F=t*r*k; wherein t is the initial flow accumulation area threshold, r is the reduction factor, and k is a weight coefficient. The updated initial flow accumulation area threshold t’=F*0.7.

[0036] It should be noted that the preset condition is that the sub-basin meets the minimum surface area and the minimum circular variance.

[0037] In some embodiments of the present application, in the case that the sub-basin meets the minimum surface area (a) and the minimum circular variance (c), the sub-basin is not divided, and thus the initial slope unit is determined based on the current sub-basin, i.e., the current sub-basin is determined as the initial slope unit.

[0038] For example, in each iteration, the sub-basin is continuously divided according to the reduction factor (r) of the initial flow accumulation area threshold (t). When a sub-basin (i.e., a sub-basin) of an initial basin unit meets the user-defined minimum surface area (a) and minimum circular variance (c), it is selected as the initial slope unit, and the iteration process ends.

[0039] S102, based on the reference slope unit and the initial slope unit, calculate the object consistency error and the comprehensive heterogeneity score; and screen the initial slope unit through the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units.

[0040] In some embodiments of the present application, based on the reference slope unit and the initial slope unit, the first part error and the second part error are determined by operation; wherein the first part error is the part error of the reference slope unit and the initial slope unit; the second part error is the part error of the initial slope unit and the reference slope unit; based on the first part error and the second part error, the object consistency error is determined;

[0041] In some embodiments of the present application, based on the reference slope unit and the initial slope unit, the first part error and the second part error are determined by operation; wherein the first part error is the part error of the reference slope unit and the initial slope unit; the second part error is the part error of the initial slope unit and the reference slope unit; based on the first part error and the second part error, the object consistency error is determined; In some embodiments of the present application, the global Moran's index and the global variance of the candidate slope unit are calculated; and based on the global Moran's index and the global variance, the comprehensive heterogeneity score is determined.

[0042] In some embodiments of the present application, the slope unit optimization device can determine the first part error and the second part error based on the reference slope unit and the initial slope unit by operation; and determine the object consistency error based on the first part error and the second part error.

[0043] The first step to eliminate unreasonable slope units is to calculate the error between the automatically extracted slope units and the actual reference slope units. The object consistency error is introduced to objectively evaluate the ability of extracting slope units. This error calculation method is based on the comparison of each object between the extracted slope units and the reference slope units. Compared with the existing error measurement methods, it can consider the size, shape and position of each slope unit at the object level. In addition, it is sensitive to both under-subdivision and over-subdivision, which helps to make reasonable inferences through step-by-step error evolution analysis. The OCE calculation formula is as follows wherein, represents the partial error of the automatically extracted slope unit and the reference slope unit, represents the partial error of the reference slope unit and the automatically extracted slope unit. The partial error is calculated as follows: , , is the reference slope unit, is the i-th in the reference slope unit. is the automatically extracted slope unit, is the i-th in it. represents the number of grid units in and respectively represent the intersection and union of and . is the size of the intersection of and relative to the weight of all grid units in that intersect with . is the weight of the importance of relative to . (x) is a trigonometric function. In addition, is calculated by replacing and in the equation. OCE is standardized between , wherein 0 represents complete consistency without error, and 1 represents complete mismatch. It is generally considered that OCE < 0.35 has good consistency match.

[0044]

[0045] ​​​It can be understood that, based on the reference slope unit and the initial slope unit, the object consistency error is determined by performing calculation; by screening the initial slope unit through the object consistency error, unreasonable slope units can be removed, the rationality of slope unit division can be improved, the division error can be reduced, and thus the accuracy of slope unit division can be improved.

[0046] In some embodiments of the present application, the slope unit optimization device can calculate a global Moran index of the candidate slope unit; calculate a global variance of the candidate slope unit; and determine a comprehensive heterogeneity score based on the global Moran index and the global variance.

[0047] For example, in order to pursue global optimization, the terrain also needs to be moderately subdivided. The heterogeneity inside and outside all slope units should be maximized. According to this assumption, the global optimal scale is selected, and the global Moran index (MI) and the global variance (V) are used to evaluate the external heterogeneity and the internal homogeneity of the terrain respectively, and the calculation formula is as follows: Wherein, m is the total number of slope units; is a spatial proximity index, which is equal to 1 when the slope unit h and the slope unit i have a common boundary, otherwise it is equal to 0; is the average width of the slope unit , y is the average width of the entire terrain; and is the surface area and the circular variance of the slope unit i. The angle needs to be converted to radian, and the average value and the difference value should be calculated according to the formula of Alvioli et al.

[0048] The comprehensive heterogeneity score (gs) is calculated as follows: The group with the highest gs value is considered to achieve the maximum balance of internal homogeneity and external heterogeneity, and is determined as the best subdivision. However, as the proportion of the candidate slope unit set gradually increases, the maximum or minimum value of MI and V will also change, and the highest gs value will also change.

[0049] It can be understood that, based on the reference slope unit and the initial slope unit, the object consistency error and the comprehensive heterogeneity score are calculated; by screening the initial slope unit through the object consistency error and the comprehensive heterogeneity score, unreasonable slope units can be removed, and the rationality of slope unit division can be improved.

[0050] S103, determining a local average similarity of each candidate slope unit in the plurality of candidate slope units.

[0051] In some embodiments of the present application, for each of the plurality of candidate slope units, the internal similarity and the adjacent difference degree of each of the plurality of candidate slope units are determined; based on the internal similarity and the adjacent difference degree, the local heterogeneity and the local uniformity of each of the plurality of candidate slope units are determined; and the local heterogeneity and the local uniformity are normalized to determine the local average similarity of each of the plurality of candidate slope units.

[0052] In some embodiments of the present application, the area value and the boundary value of each of the plurality of candidate slope units are determined; based on the adjacent difference degree and the area value, the local heterogeneity of each of the plurality of candidate slope units is determined; and based on the internal similarity and the boundary value, the local uniformity of each of the plurality of candidate slope units is determined.

[0053] S104, based on the local average similarity, the plurality of candidate slope units are classified to determine the slope unit to be optimized.

[0054] In some embodiments of the present application, for each of the plurality of candidate slope units, if the local average similarity is less than a first similarity threshold and the area value is greater than an area threshold, the candidate slope unit is determined as a first unreasonable slope unit; if the local average similarity is greater than a second similarity threshold and the boundary value is less than a boundary threshold, the candidate slope unit is determined as a second unreasonable slope unit; and the first unreasonable slope unit and the second unreasonable slope unit are determined as the slope unit to be optimized.

[0055] In some embodiments of the present application, the first unreasonable slope unit represents that the refinement degree of the current slope unit is less than a preset refinement threshold; and the second unreasonable slope unit represents that the refinement degree of the current slope unit is greater than the preset refinement threshold.

[0056] S105, the slope unit to be optimized is subjected to subdivision or merging processing to determine the final slope unit.

[0057] In some embodiments of the present application, if the slope unit to be optimized is the first unreasonable slope unit, the slope unit to be optimized is subjected to subdivision processing by the slope unit with the highest comprehensive heterogeneity score to determine the final slope unit. In some embodiments of the present application, if the slope unit to be optimized is the second unreasonable slope unit, the slope unit to be optimized is subjected to merging processing based on the local average similarity and the local heterogeneity of the slope unit to be optimized to obtain a merged slope unit; the internal uniformity of the merged slope unit and the local average similarity of the merged slope unit are determined; and in a case where the local average similarity of the merged slope unit is less than a local similarity threshold and the internal uniformity of the merged slope unit is greater than an internal uniformity threshold, the merged slope unit is determined as the final slope unit.

[0058] It can be understood that, on the one hand, the object consistency error and the comprehensive heterogeneity score are calculated based on the reference slope unit and the initial slope unit; the unreasonable slope unit can be removed, and the rationality of the slope unit division can be improved by screening the initial slope unit through the object consistency error and the comprehensive heterogeneity score; on the other hand, the local average similarity of each candidate slope unit in the plurality of candidate slope units is determined; and the plurality of candidate slope units are classified based on the local average similarity to determine the slope unit to be optimized; the final slope unit is determined by performing subdivision or merging processing on the slope unit to be optimized, and in this process, the unreasonable slope unit is optimized, the division error can be reduced, and thus the accuracy of the slope unit division can be improved.

[0059] In some embodiments of the present application, as shown in Figure 2 S103 can be implemented through S1031, S1032 and S1033, as follows: S1031, for each candidate slope unit in the plurality of candidate slope units, determine the internal similarity and the adjacent difference degree of each candidate slope unit.

[0060] In some embodiments of the present application, the slope unit optimization device can calculate the variance and the standard deviation of each candidate slope unit in the plurality of candidate slope units, and determine the internal similarity and the adjacent difference degree of each candidate slope unit.

[0061] For example, in order to more reasonably compare the similarity and the difference degree of each candidate slope unit, the variance and the standard deviation are used to represent, instead of their direct values, and the average value of all adjacent slope units is taken as a reference.

[0062] First, the internal similarity and the adjacent difference degree of each candidate slope unit are determined by the domain variability variance ( ) and the neighborhood variability standard deviation ( ), and the calculation formula is as follows: wherein, and are the length-width difference of the slope unit and the average value difference with the adjacent SU (i.e. slope unit); , are defined as shown in the above formula; and are the standard deviation of and the average value of the adjacent SU.

[0063] S1032, determine the local heterogeneity and local homogeneity of each candidate slope unit based on the internal similarity and the adjacent difference degree.

[0064] In some embodiments of the present application, the area value and the boundary value of each candidate slope unit are determined; the local heterogeneity of each candidate slope unit is determined based on the adjacent difference degree and the area value; and the local homogeneity of each candidate slope unit is determined based on the internal similarity and the boundary value.

[0065] For example, in order to improve the accuracy of identification, the area value (A) and the boundary value (B) are introduced to consider the size of the slope unit and the diversity of the boundary, respectively. ) and (B) ) are the area of SUi and its adjacent SU, respectively. and n are the perimeter of SUi and the length of the common boundary of its adjacent SU, respectively. and

[0066] The area, the boundary, and the length-width difference are integrated into the local heterogeneity (H) and the area and the length-width homogeneity are also integrated into the local homogeneity (U). S1033, normalize the local heterogeneity and the local homogeneity to determine the local average similarity of each candidate slope unit.

[0067] In some embodiments of the present application, the slope unit optimization device can normalize the local heterogeneity and the local homogeneity to determine the local average similarity of each candidate slope unit.

[0068] For example, after normalizing (H) and (U), a general algebraic formula is used to represent the quantitative comparison of the two parts to determine the local average similarity of each SU (S), wherein the range of S is [-1, 1].

[0069] ​​​​​​​It can be understood that, by determining the local average similarity of each of the plurality of candidate slope units, and classifying the plurality of candidate slope units based on the local average similarity, the slope unit to be optimized is determined; and by performing the subdivision or merging processing on the slope unit to be optimized, the final slope unit is determined, in this process, the unreasonable slope unit is optimized, which can reduce the division error, thereby improving the accuracy of the slope unit division.

[0070] In some embodiments of the present application, as shown in Figure 3 S104 can be implemented by S1041, S1042 and S1043, as follows: S1041, for each of the plurality of candidate slope units, if the local average similarity is less than the first similarity threshold and the area value is greater than the area threshold, the candidate slope unit is determined as the first unreasonable slope unit; wherein the first unreasonable slope unit represents that the refinement degree of the current slope unit is less than the preset refinement threshold.

[0071] In some embodiments of the present application, the first unreasonable slope unit represents that the refinement degree of the current slope unit is less than the preset refinement threshold; wherein the preset refinement threshold is used to distinguish whether the division of the slope unit is reasonable.

[0072] In some embodiments of the present application, for each of the plurality of candidate slope units, if the local average similarity is less than the first similarity threshold and the area value is greater than the area threshold, it indicates that the division of the candidate slope unit is not detailed enough, and the candidate slope unit is determined as the first unreasonable slope unit.

[0073] For example, the first unreasonable slope unit can be an under-subdivided slope unit. The first similarity threshold is -0.6, and the area threshold is 3σ.

[0074] S1042, if the local average similarity is greater than the second similarity threshold and the boundary value is less than the boundary threshold, the candidate slope unit is determined as the second unreasonable slope unit; wherein the second unreasonable slope unit represents that the refinement degree of the current slope unit is greater than the preset refinement threshold.

[0075] In some embodiments of the present application, the second unreasonable slope unit represents that the refinement degree of the current slope unit is greater than the preset refinement threshold; wherein the preset refinement threshold is used to distinguish whether the division of the slope unit is reasonable.

[0076] In some embodiments of the present application, for each of the plurality of candidate slope units, if the local average similarity is greater than the second similarity threshold and the boundary value is less than the boundary threshold, it indicates that the division of the candidate slope unit is too detailed, and the candidate slope unit is determined as the second unreasonable slope unit.

[0077] For example, the second unreasonable slope unit can be an oversubdivided slope unit. The second similarity threshold is 0.8, and the boundary threshold is 0.3.

[0078] In some embodiments of the present application, the slope unit optimization device can determine the first unreasonable slope unit and the second unreasonable slope unit as the slope units to be optimized.

[0079] In some embodiments of the present application, the slope unit optimization device can determine the first unreasonable slope unit and the second unreasonable slope unit as the slope units to be optimized.

[0080] For example, the first unreasonable slope unit can be an undersubdivided slope unit, and the second unreasonable slope unit can be an oversubdivided slope unit. Through field investigation and three-dimensional terrain analysis, the characteristics of undersubdivided slope units and oversubdivided slope units are summarized. The area of an undersubdivided slope unit is larger than that of its adjacent area, and the boundary along the terrain is loose. Their adjacent surfaces differ greatly, and the similarity within the unit is low. The area of an oversubdivided slope unit is smaller than that of a normal unit, and it is more fragmented. They are usually mixed with the surrounding slope units, sharing a relatively long common boundary. The internal similarity of an oversubdivided slope unit is high, and the difference with its adjacent unit is small.

[0081] It can be understood that by classifying a plurality of candidate slope units through local average similarity, area value and boundary value, unreasonable slope units, i.e., slope units to be optimized, are determined, which facilitates subsequent optimization of the slope units to be optimized, and thus improves the accuracy of slope unit division.

[0082] In some embodiments of the present application, S105 can be implemented by S1051 or S1052-S1054, as follows: S1051, if the slope unit to be optimized is the first unreasonable slope unit, the slope unit to be optimized is subdivided by the slope unit with the highest comprehensive heterogeneity score, and the final slope unit is determined.

[0083] In some embodiments of the present application, if the slope unit to be optimized is the first unreasonable slope unit, the slope unit to be optimized can be further subdivided, and the subdivided slope unit is replaced by the slope unit with the highest comprehensive heterogeneity score, so as to determine the final slope unit.

[0084] In some other embodiments of the present application, if the to-be-optimized slope unit is the first unreasonable slope unit, the to-be-optimized slope unit can be further subdivided, and the subdivided slope unit is replaced by the slope unit with the highest comprehensive heterogeneity score. Since the subdivided slope unit is replaced by the slope unit with the highest comprehensive heterogeneity score, the subdivided slope unit can be excessively subdivided. Therefore, the subdivided slope unit replaced by the slope unit with the highest comprehensive heterogeneity score is determined as a second unreasonable slope unit. Based on the local average similarity and the local heterogeneity of the to-be-optimized slope unit, a merged slope unit is obtained through merging processing. The internal uniformity of the merged slope unit and the local average similarity of the merged slope unit are determined. If the local average similarity of the merged slope unit is less than the local similarity threshold value, and the internal uniformity of the merged slope unit is greater than the internal uniformity threshold value, the merged slope unit is determined as a final slope unit.

[0085] In S1052, if the to-be-optimized slope unit is the second unreasonable slope unit, based on the local average similarity and the local heterogeneity of the to-be-optimized slope unit, a merged slope unit is obtained through merging processing.

[0086] In some embodiments of the present application, if the to-be-optimized slope unit is the second unreasonable slope unit, based on the local average similarity and the local heterogeneity of the to-be-optimized slope unit, the to-be-optimized slope unit and its adjacent to-be-optimized slope unit are merged to obtain a merged slope unit.

[0087] In S1053, the internal uniformity of the merged slope unit and the local average similarity of the merged slope unit are determined.

[0088] In some embodiments of the present application, after obtaining the merged slope unit, the internal uniformity of the merged slope unit and the local average similarity of the merged slope unit are calculated. The specific calculation method is the same as the calculation method of the internal uniformity and the local average similarity of the to-be-selected slope unit, which will not be described here.

[0089] In S1054, if the local average similarity of the merged slope unit is less than the local similarity threshold value, and the internal uniformity of the merged slope unit is greater than the internal uniformity threshold value, the merged slope unit is determined as a final slope unit.

[0090] In some embodiments of the present application, the local similarity threshold value and the internal uniformity threshold value are used to evaluate whether the slope unit is excessively subdivided, that is, whether the merged slope unit is excessively merged.

[0091] In some embodiments of the present application, if the local average similarity of the merged slope unit is less than the local similarity threshold, and the internal uniformity of the merged slope unit is greater than the internal uniformity threshold, it is considered that the merged slope unit meets the reasonable division, and the merged slope unit is determined as the final slope unit.

[0092] In some embodiments of the present application, if the local average similarity of the merged slope unit is greater than the local similarity threshold, and / or the internal uniformity of the merged slope unit is less than the internal uniformity threshold, it is considered that the merged slope unit is over-merged, the merging is cancelled, and the merging process is re-performed. The internal uniformity of the merged slope unit and the local average similarity of the merged slope unit are continuously determined until the local average similarity of the merged slope unit is less than the local similarity threshold, and the internal uniformity of the merged slope unit is greater than the internal uniformity threshold, at which point the merged slope unit is determined as the final slope unit.

[0093] For example, the region associated with the under-subdivision unit is replaced by the corresponding SUs multiple times, and then the replacement with the highest gs value is considered as the best replacement. However, the best replacement cannot guarantee proper terrain re-subdivision for all coexisting under-subdivision units. Some replacements can generate new subdivisions. Therefore, the re-division of the SU will be marked as an over-subdivision unit for examination in the next optimization stage.

[0094] Then, although the units are effectively corrected by merging, the units are aggregated with adjacent SUs. In order to prevent over-merging, a local average similarity ( ) is defined as a sub-region merging indicator of local similarity change ( ). The principle of the merging criterion is as follows: on the one hand, the determination of the merging order of the local heterogeneity ( ) needs to be in two or more adjacent units, and the unit with lower local heterogeneity is preferred for merging. On the other hand, the internal homogeneity change between the new SU and the original SU needs to be estimated. The merging between completely different SUs can generate a new SU with complex internal structure. This case belongs to over-merging and should be avoided. A given threshold of LSCT is used to quantify those over-merging cases. LSCT can be obtained from a large number of LSCs generated by manual training of the merging process. In the present application, Ubuntu 22.04 system and GRASS GIS 7.8.0 software are used.

[0095] It should be noted that the SU is the slope unit.

[0096] It can be understood that, by optimizing unreasonable slope units, the final slope unit is determined, and the accuracy of slope unit division can be improved.

[0097] Based on the slope unit optimization method of the above embodiment, the embodiment of the present application further provides a slope unit optimization system, as shown in Figure 4 Figure 4 The structure diagram of a slope unit optimization system provided by the embodiment of the present application is shown in the figure, and the slope unit optimization system 4 includes a division unit 401, a calculation unit 402 and a determination unit 403, wherein, The division unit 401 is configured to divide the obtained digital terrain to obtain initial slope units. The calculation unit 402 is configured to calculate the object consistency error and the comprehensive heterogeneity score based on the reference slope unit and the initial slope unit. The determination unit 403 is configured to filter the initial slope units based on the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units; determine the local average similarity of each candidate slope unit in the plurality of candidate slope units; classify the plurality of candidate slope units based on the local average similarity to determine a slope unit to be optimized; and perform subdivision or merging processing on the slope unit to be optimized to determine a final slope unit.

[0098] In some embodiments of the present application, the determination unit 403 is further configured to determine the object consistency error by performing operations based on the reference slope unit and the initial slope unit; and determine a candidate slope unit by performing initial filtering on the initial slope unit based on the object consistency error. The calculation unit 402 is further configured to calculate the comprehensive heterogeneity score of the candidate slope unit. The determination unit 403 is further configured to determine the plurality of candidate slope units by performing secondary filtering on the candidate slope unit based on the comprehensive heterogeneity score.

[0099] In some embodiments of the present application, the determination unit 403 is further configured to determine the internal similarity and the adjacent difference degree of each candidate slope unit in the plurality of candidate slope units; determine the local heterogeneity and the local uniformity of each candidate slope unit based on the internal similarity and the adjacent difference degree; and determine the local average similarity of each candidate slope unit by performing normalization processing on the local heterogeneity and the local uniformity.

[0100] ​In some embodiments of the present application, the determining unit 403 is further configured to determine an area value and a boundary value of each of the candidate slope units; determine the local heterogeneity of each of the candidate slope units based on the adjacent difference and the area value; and determine the local uniformity of each of the candidate slope units based on the internal similarity and the boundary value.

[0101] In some embodiments of the present application, the determining unit 403 is further configured to, for each of the candidate slope units, if the local average similarity is less than a first similarity threshold and the area value is greater than an area threshold, determine the candidate slope unit as a first unreasonable slope unit; wherein the first unreasonable slope unit represents that the refinement degree of the current slope unit is less than a preset refinement threshold; if the local average similarity is greater than a second similarity threshold and the boundary value is less than a boundary threshold, determine the candidate slope unit as a second unreasonable slope unit; wherein the second unreasonable slope unit represents that the refinement degree of the current slope unit is greater than the preset refinement threshold; and determine the first unreasonable slope unit and the second unreasonable slope unit as the slope unit to be optimized.

[0102] In some embodiments of the present application, the determining unit 403 is further configured to, if the slope unit to be optimized is the first unreasonable slope unit, perform a subdivision process on the slope unit to be optimized by a slope unit with the highest comprehensive heterogeneity score to determine the final slope unit; if the slope unit to be optimized is the second unreasonable slope unit, perform a merging process based on the local average similarity and the local heterogeneity of the slope unit to be optimized to obtain a merged slope unit; determine the internal uniformity of the merged slope unit and the local average similarity of the merged slope unit; and in a case where the local average similarity of the merged slope unit is less than a local similarity threshold and the internal uniformity of the merged slope unit is greater than an internal uniformity threshold, determine the merged slope unit as the final slope unit.

[0103] In some embodiments of the present application, the determining unit 403 is further configured to perform an operation based on the reference slope unit and the initial slope unit to determine a first partial error and a second partial error; wherein the first partial error is a partial error of the reference slope unit and the initial slope unit; the second partial error is a partial error of the initial slope unit and the reference slope unit; and determine the object consistency error based on the first partial error and the second partial error. and / or, The calculating unit 402 is further configured to calculate a global Moran's I index and a global variance of the candidate slope unit. The determination unit 403 is further configured to determine the comprehensive heterogeneity score based on the global Moran's I and the global variance.

[0104] Based on the slope unit optimization method of the above embodiments, the embodiments of the present application further provide a slope unit optimization device. Figure 5 As shown in the figure, Figure 5 A structural schematic diagram of a slope unit optimization device provided by the embodiments of the present application is shown in the figure. The slope unit optimization device 5 includes a processor 501 and a memory 502. The memory 502 is configured to store a computer program. The processor 501 is configured to call and run the computer program from the memory to perform the slope unit optimization method as described in the above embodiments.

[0105] In the embodiments of the present application, the above processor 501 can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It can be understood that, for different devices, the electronic device for realizing the above processor function can also be other, and the embodiments of the present application are not limited specifically.

[0106] The embodiments of the present application provide a computer readable storage medium storing a computer program, which is used to realize the slope unit optimization method as described in any of the above embodiments when executed by a processor.

[0107] For example, the program instructions corresponding to the slope unit optimization method in the embodiments of the present application can be stored on a storage medium such as an optical disc, a hard disk, a U disk, etc. When the program instructions corresponding to the slope unit optimization method in the storage medium are read by an electronic device or executed, the slope unit optimization method as described in any of the above embodiments can be realized.

[0108] In addition, each functional module in the embodiments of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional module.

[0109] If the integrated unit is implemented in the form of a software function module and is not sold or used as an independent product, it can be stored in a computer readable storage medium based on such understanding. The technical solutions of the embodiments can essentially or contribute to the prior art or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the embodiments. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0110] It should be understood that the "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiments are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" or "in some embodiments" appearing throughout the specification does not necessarily mean the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the execution order, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The sequence number of the above embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. The above description of each embodiment tends to emphasize the differences between each embodiment, and the same or similar parts can be referred to each other. For the sake of brevity, this paper will not be repeated here.

[0111] The above-mentioned modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules; they can be located in one place or distributed on multiple network units; part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0112] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each module can be a separate unit, or two or more modules can be integrated in one unit; the integrated module can be realized in the form of hardware or hardware plus software function unit.

[0113] Those skilled in the art can understand that all or part of the steps of the foregoing method embodiments can be completed by relevant hardware of program instructions, and the foregoing program can be stored in a computer readable storage medium. When the program is executed, the program executes the steps of the foregoing method embodiments. The foregoing storage medium includes a mobile storage device, a read only memory (ROM), a magnetic disc or an optical disc, and various media that can store program codes.

[0114] The methods disclosed in the several method embodiments provided by the embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments.

[0115] The features disclosed in the several product embodiments provided by the embodiments of the present application can be combined arbitrarily without conflict to obtain new product embodiments.

[0116] The features disclosed in the several method or device embodiments provided by the embodiments of the present application can be combined arbitrarily without conflict to obtain new method embodiments or device embodiments.

[0117] The foregoing is only a manner of implementing the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application should be subject to the protection scope of the claims.

Claims

1. A slope unit optimization method, characterized in that: The method comprises: Divide the acquired digital terrain to obtain initial slope units; Calculating an object consistency error and a comprehensive heterogeneity score based on a reference slope unit and the initial slope unit; and screening the initial slope unit by using the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units; Determining a local average similarity of each of the plurality of candidate slope units; Classifying the plurality of slope units to be selected based on the local average similarity to determine a slope unit to be optimized; The slope units to be optimized are subdivided or merged to determine final slope units.

2. The method according to claim 1, characterized in that The step of calculating the object consistency error and the comprehensive heterogeneity score based on the reference slope unit and the initial slope unit, and screening the initial slope unit by using the object consistency error and the comprehensive heterogeneity score to determine a plurality of slope units to be selected comprises: performing calculations based on the reference slope unit and the initial slope unit to determine an object consistency error; Performing an initial screening of the initial slope units based on the object consistency error to determine candidate slope units; Calculating the comprehensive heterogeneity scores of the candidate slope units; and performing secondary screening on the candidate slope units based on the comprehensive heterogeneity scores to determine the plurality of slope units to be selected.

3. The method according to claim 1, characterized in that Determining the local average similarity of each of the plurality of candidate slope units includes: For each of the plurality of candidate slope units, determining an internal similarity and an adjacent difference degree of each candidate slope unit; Determining the local heterogeneity and local uniformity of each of the to-be-selected slope units based on the internal similarity and the adjacent difference; Normalization is performed on the local heterogeneity and the local uniformity to determine the local average similarity of each of the to-be-selected slope units.

4. The method according to claim 3, characterized in that The determining of the local heterogeneity and the local uniformity of each of the to-be-selected slope units based on the internal similarity and the adjacent difference includes: Determining the area value and boundary value of each slope unit to be selected; Determining the local heterogeneity of each of the candidate slope units based on the adjacent difference and the area value; The local uniformity of each of the candidate slope units is determined based on the internal similarity and the boundary value.

5. The method according to claim 1, wherein The classifying the plurality of slope units to be selected based on the local average similarity to determine the slope unit to be optimized includes: For each of the plurality of candidate slope units, if the local average similarity is less than a first similarity threshold and the area value is greater than an area threshold, determining the candidate slope unit as a first unreasonable slope unit; wherein the first unreasonable slope unit indicates that the refinement degree of the current slope unit is less than a preset refinement threshold; If the local average similarity is greater than a second similarity threshold and the boundary value is less than the boundary threshold, the selected slope unit is determined to be a second unreasonable slope unit; wherein the second unreasonable slope unit indicates that the refinement degree of the current slope unit is greater than a preset refinement threshold; The first unreasonable slope unit and the second unreasonable slope unit are determined as the slope units to be optimized.

6. The method according to any one of claims 1 to 5, characterized in that The step of subdividing or merging the slope units to be optimized to determine final slope units includes: If the slope unit to be optimized is the first unreasonable slope unit, subdividing the slope unit to be optimized by using the slope unit with the highest comprehensive heterogeneity score to determine the final slope unit; If the slope unit to be optimized is the second unreasonable slope unit, merging the slope units to be optimized based on the local average similarity and local heterogeneity to obtain a merged slope unit; Determining the internal uniformity of the merged slope units and the local average similarity of the merged slope units; When the local average similarity of the merged slope unit is less than a local similarity threshold and the internal uniformity of the merged slope unit is greater than an internal uniformity threshold, the merged slope unit is determined as the final slope unit.

7. The method according to claim 2, characterized in that The performing calculation based on the reference slope unit and the initial slope unit to determine the object consistency error includes: performing a calculation based on the reference ramp unit and the initial ramp unit to determine a first partial error and a second partial error; wherein the first partial error is a partial error between the reference ramp unit and the initial ramp unit; and the second partial error is a partial error between the initial ramp unit and the reference ramp unit; determining the object consistency error based on the first partial error and the second partial error; and / or, Calculating the comprehensive heterogeneity score of the candidate slope unit includes: Calculating the global Moran index and global variance of the slope unit to be selected; The comprehensive heterogeneity score is determined based on the global Moran's index and the global variance.

8. A slope unit optimization system, characterized in that: include: A division unit, a calculation unit and a determination unit, wherein, The division unit is used to divide the acquired digital terrain to obtain initial slope units; The calculation unit is used to calculate the object consistency error and the comprehensive heterogeneity score based on the reference slope unit and the initial slope unit; The determination unit is configured to screen the initial slope units using the object consistency error and the comprehensive heterogeneity score to determine a plurality of candidate slope units; determine a local average similarity of each of the plurality of candidate slope units; classify the plurality of candidate slope units based on the local average similarity to determine a slope unit to be optimized; and subdivide or merge the slope units to be optimized to determine a final slope unit.

9. A slope unit optimization device, characterized in that: include: processor and memory, wherein The memory is used to store computer programs; The processor is configured to call and run the computer program from the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Executable instructions are stored, which are used to cause a processor to execute and implement the method according to any one of claims 1 to 7.

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