Fusion method and system for slope factor

Abstract

The application discloses a slope factor fusion method and system, wherein the method comprises the following steps: obtaining a slope factor according to grid DEM data; performing extreme value normalization processing on the slope factor to obtain a normalized terrain factor; performing multi-factor fusion on the normalized terrain factor to obtain a plurality of slope fusion factors; and comprehensively analyzing and evaluating the plurality of slope fusion factors to screen out a target slope fusion factor. The application also discloses a slope factor fusion system. The application aims to obtain a new slope fusion factor according to a slope factor, so as to improve the application limitation of the original slope comprehensive expression factor and solve the problems that the terrain implicit information analysis and mining are not comprehensive and fine enough.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital terrain analysis, and particularly relates to a slope factor fusion method and system. BACKGROUND

[0002] Automatic extraction and calculation of terrain factors based on grid digital elevation model (Digital Elevation Model, hereinafter referred to as DEM) is an important research content of digital terrain analysis (Digital Terrain Analysis, hereinafter referred to as DTA), and has a broad application prospect in soil and water conservation, urban and rural construction, civil engineering and military fields. The mutual combination and structure of terrain factors to a certain extent reflect the development mechanism of the landform in the area. Therefore, mining the combination characteristics and expression mode of terrain factors of different landforms and the correlation and distribution characteristics between terrain factors have important significance for exploring deep-seated landform characteristics and processes, and revealing the evolution law and driving mechanism of landform development. Only relying on single or simple terrain factors cannot effectively depict the complex changes of the ground, and the multi-factor comprehensive evaluation research selects the preliminary target terrain factor from multiple angles, and better expresses the terrain information based on the multi-factor fusion theory and analysis method.

[0003] Slope is the basic component unit of natural terrain entity and the basic element of the ground. The change of landform is actually the change of slope characteristics and their combination. The complete expression of a slope should cover three slope terrain factors of slope, slope length and slope width. Slope terrain factors can reflect the shape of the slope and its oxidation process, and play an important role in loess landform, soil erosion, ecological construction and other aspects. Common single slope factors can be directly or indirectly calculated from grid DEM, which is the basis for terrain analysis and simulation. Current slope research usually selects S factor, L factor, LS factor, slope shape index and curvature as single or combined expression to represent the slope shape. Landform research, hydrological erosion and address disaster research all take slope width as a key element to describe the shape of the slope. At present, only the LS factor is used as an important expression parameter for terrain calculation, which is widely used to describe the slope shape and predict the slope erosion. Slope width has not been included in the slope comprehensive expression system. SUMMARY

[0004] The main purpose of the present application is to provide a slope factor fusion method and system, which aims to solve the problems that the existing slope comprehensive expression factor LS does not include the slope width factor in the comprehensive expression system, has application limitations, and is not comprehensive and fine in terrain implicit information analysis and mining.

[0005] To achieve the above object, the application provides a slope factor fusion method and system, wherein the slope factor fusion method comprises the following steps:

[0006] S1, obtaining slope factors from grid DEM data, wherein the slope factors comprise L factor, S factor, LS factor and W factor;

[0007] S2, performing extreme value normalization processing on the slope factors to obtain normalized terrain factors;

[0008] S3, performing multi-factor fusion on the normalized terrain factors to obtain multiple slope fusion factors;

[0009] S4, performing comprehensive analysis and evaluation on the multiple slope fusion factors to screen out target slope fusion factors.

[0010] Preferably, the L factor is a slope length factor, the S factor is a slope factor, the LS factor is a slope length factor, and the W factor is a slope width factor.

[0011] Preferably, the specific steps for obtaining the W factor in the step S1 are as follows:

[0012] S111, performing depression filling processing on the grid DEM data to obtain non-depression grid DEM data;

[0013] S112, obtaining a flow accumulation value according to the non-depression grid DEM data;

[0014] S113, determining a target threshold value of the flow accumulation value by using a river network density second-order derivative analysis method;

[0015] S114, extracting a grid river network at the target threshold value of the flow accumulation value, and dividing sub-basins by using a Strahler method to grade the river network;

[0016] S115, performing slope surface generalization on the divided basin data by using a Water Erosion Prediction Project (WEPP) / Geo-spatial interface for WEPP (GeoWEPP) model to finally determine the W factor.

[0017] Preferably, the target threshold value is 1800.

[0018] Preferably, the specific steps for obtaining the L factor, the S factor and the LS factor in the step S1 are as follows:

[0019] S121, generating a multi-flow direction grid layer by using a hydrological analysis tool according to the no-depression grid DEM data;

[0020] S122, performing a loop iteration processing on the grid layer to obtain a slope length λ and an L factor;

[0021] S123, extracting a slope θ according to the no-depression grid DEM data, and calculating an S factor according to a slope classification function;

[0022] S124, obtaining an LS factor by extracting the L factor and the S factor.

[0023] Preferably, before the filling depression processing on the grid DEM data, the method further comprises:

[0024] judging whether the grid DEM data has a depression, and if so, filling the depression of the grid DEM data to obtain no-depression grid DEM data.

[0025] Preferably, the step S122 is specifically:

[0026] According to the multi-flow direction grid layer, defining a local high point as a slope length cumulative calculation starting point, continuously extracting a runoff ending point, performing a loop iteration processing, and obtaining a slope length λ and an L factor by a slope length λ calculation formula and an L factor calculation formula.

[0027] Preferably, the specific steps of the extreme value normalization processing of the slope factor in the step S2 are:

[0028] calculating the slope factor obtained in the step S1 by using an extreme value normalization method to obtain a value of the normalized terrain factor;

[0029] mapping the value of the normalized terrain factor to 0-1.

[0030] Preferably, the slope factor and the target slope fusion factor are both grid DEM numerical matrices.

[0031] A slope factor fusion system, comprising a processor, a memory, and an application program of slope factor fusion stored on the memory and executable on the processor, the application program of slope factor fusion implements the steps of the slope factor fusion method when executed.

[0032] In the technical scheme of the present application, the slope factor fusion method obtains the slope factor from the grid DEM data, and the slope factor includes L factor, S factor, LS factor and W factor; the extreme value of the slope factor is normalized to obtain normalized terrain factors; the normalized terrain factors are fused to obtain multiple slope fusion factors; the multiple slope fusion factors are comprehensively analyzed and evaluated to screen out target slope fusion factors. The present application integrates slope width into the original slope comprehensive expression factor to form a new slope fusion factor, solves the limitations and insufficient precision of the existing slope comprehensive expression factor LS, and realizes the analysis and mining of terrain implicit information through the slope fusion factor, which is helpful for the analysis and planning of regional topography and geomorphology.

[0033] In the present application, the target threshold of the flow accumulation is determined by using the river network density second derivative analysis method, the generated river network is consistent with the actual river network of the basin, the sub-basins are efficiently and accurately divided, and the error is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed to be used in the following embodiment or prior art description will be briefly introduced. 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 based on the drawings shown.

[0035] Figure 1 The flowchart of the slope factor fusion method of the embodiment of the present application is shown in the figure.

[0036] Figure 2 The structure diagram of the slope factor fusion system of the embodiment of the present application is shown in the figure.

[0037] Figure 3 The diagram of the relationship between the threshold of flow accumulation and the second derivative of river network density in the slope factor fusion method of the embodiment of the present application is shown in the figure.

[0038] The structure diagram of the river network extracted under different thresholds in the slope factor fusion method of the embodiment of the present application is shown in the figure.

[0039] Figure 5 The flowchart of the W factor acquisition in the slope factor fusion method of the embodiment of the present application is shown in the figure.

[0040] Figure 6 The diagram of the relationship between the slope fusion factor and the normalized terrain factor in the slope factor fusion method of the embodiment of the present application is shown in the figure.

[0041] Figure 7A DEM grid chart for the slope factor fusion method of the embodiment of the present application in an experimental area;

[0042] Figure 8 A DEM rendering chart for the slope factor fusion method of the embodiment of the present application in an experimental area;

[0043] Figure 9-A , B, C, D are respectively the slope L, S, LS, W factor structure schematic diagram for the slope factor fusion method of the embodiment of the present application in an experimental area;

[0044] Figure 10-a , b, c, d are all the LSW factor schematic diagram after fusion for the slope factor fusion method of the embodiment of the present application.

[0045] The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0047] The technical solutions among the various embodiments of the present application can be combined with each other, but must be based on the fact that a person skilled in the art can realize it. When the combination of technical solutions appears to be contradictory or unachievable, it should be considered that the combination of technical solutions does not exist and is not within the protection scope required by the present application.

[0048] Embodiment 1:

[0049] Referring to Figure 1 According to an aspect of the present application, the present application provides a slope factor fusion method and system, wherein the slope factor fusion method comprises the following steps:

[0050] S1, obtaining slope factors according to grid DEM data, wherein the slope factors comprise L factor, S factor, LS factor and W factor;

[0051] S2, performing extreme value normalization processing on the slope factors to obtain normalized terrain factors;

[0052] S3, performing multi-factor fusion on the normalized terrain factors to obtain a plurality of slope fusion factors;

[0053] S4, comprehensively analyzing and evaluating the plurality of slope fusion factors to screen out target slope fusion factors.

[0054] In the embodiment, the slope body factor is obtained according to the grid DEM data, and the slope body factor is fused, analyzed and processed to obtain a target slope body fusion factor. The method solves the limitations and insufficient precision of the existing slope comprehensive expression factor LS, and can realize analysis and mining of terrain implicit information, which is helpful for analysis and planning of regional topography and geomorphology.

[0055] Specifically, in the embodiment, the slope body factor includes an L factor, an S factor, an LS factor and a W factor. The L factor is a slope length factor. The S factor is a slope factor. The LS factor is a slope length factor. The W factor is a slope width factor.

[0056] Specifically, in the embodiment, the specific steps of obtaining the L factor, the S factor and the LS factor in the step S1 are as follows.

[0057] S121, generating a multi-flow direction grid layer by using a hydrological analysis tool for the depression-free grid DEM data;

[0058] S122, performing a loop iteration processing on the grid layer to obtain a slope length λ and the L factor;

[0059] S123, extracting a slope θ according to the depression-free grid DEM data, and calculating the S factor according to a slope grading function;

[0060] S124, obtaining the LS factor by extracting terrain indexes of the L factor and the S factor.

[0061] The depression-free grid DEM data is generated by filling the depression of the grid DEM data with high resolution as a data source. The depression-free grid DEM data is used to generate a multi-flow direction grid layer by using a hydrological analysis tool. According to the multi-flow direction grid layer, a local high point is defined as a slope length accumulation starting point, and a continuous runoff ending point is extracted to perform a loop iteration processing. The slope length λ and the L factor are obtained by using a slope length λ calculation formula (1) and an L factor calculation formula (2). The slope θ is extracted by using a PLANAR algorithm according to the generated depression-free grid DEM data, and the S factor is obtained by calculating the slope θ according to a S factor calculation formula (3) by using a slope grading function. The LS factor is obtained by extracting terrain indexes of the L factor and the S factor according to a formula (4).

[0062]

[0063]

[0064]

[0065] LS = L * S (4)

[0066] Specifically, in the embodiment, before the depression filling processing is performed on the grid DEM data, the method further includes:

[0067] S110, determining whether the grid DEM data has a depression, and if so, filling the depression of the grid DEM data to obtain non-depression grid DEM data.

[0068] Specifically, in the embodiment, as shown in Figure 6 The specific steps of obtaining the W factor in step S1 are:

[0069] S111, performing depression filling processing on the grid DEM data to obtain non-depression grid DEM data;

[0070] S112, obtaining the flow accumulation according to the non-depression grid DEM data;

[0071] S113, determining the target threshold of the flow accumulation by using the river network density second-order derivative analysis method;

[0072] S114, extracting the grid river network under the target threshold of the flow accumulation, and using the Strahler method to grade the river network and divide the sub-basins;

[0073] S115, using the WEPP / GeoWEPP model to perform slope surface generalization on the divided basin data to finally determine the W factor.

[0074] Specifically, in the embodiment, first, the flow accumulation is obtained based on the non-depression grid DEM data generated in the LS factor stage, and the target threshold of the flow accumulation is determined by using the river network density second-order derivative analysis method. The GIS hydrological analysis tool is used to set 600, 800, 1000, 1200, 1400, 1600, 1800, 2000, 2200, and 2400 as 10 flow accumulation threshold values to generate grid river networks. The initial setting principle of the threshold value follows the similar size research area target threshold value result and is supplemented by the large research area proportionality similarity setting. The river network of the test area basin under different threshold values is extracted from the flow accumulation layer, and the river density, river density drop rate, river length, river length drop rate, river source quantity, and river source quantity drop rate are calculated through the attribute table, as shown in Table 1:

[0075]

[0076] Table 1 Relationship between basin river network characteristics and flow accumulation threshold value

[0077] With the increase of threshold, the river length, river network density and the number of river sources all show a downward trend, but the downward trend slows down and eventually becomes the main river, tending to be stable. When the threshold increases from 600 to 800, the river network density, river length and the number of river sources decrease by 18.29%, 18.29% and 40.63% respectively; when the threshold increases from 600 to 1000, the river network density, river length and the number of river sources decrease by 7.53%, 7.53% and 17.11% respectively, with the increase of threshold, the river network characteristics are in slow decline, and finally tend to be stable; when the threshold is 1600-1800, the downward rate suddenly increases and then decreases smoothly; when the threshold increases to 2400, many tributaries disappear, leaving only the main river, which is not consistent with the actual situation.

[0078] Specifically, as shown in Figure 3 , the fitting function is y = 4.7048x -0.285 , the fitting degree R 2 is 0.991, y is the river network density (km / km 2 ), x is the threshold, the second derivative of the power function relationship of the river network density is Y” = 1.723X -2.285 , the fitting degree is 0.997, the threshold is substituted into Y” = 1.723X -2.285 , the relationship between the threshold of the confluence accumulation and the second derivative of the river network density is obtained, as shown in Figure 3 , the river network density decreases from the threshold of 600, and finally tends to be flat, when the threshold is 1800, the inflection point appears, the river network density and the number of river sources tend to be stable, when the threshold is 1800, the river network density is flat, and the generated river network is consistent with the actual river network of the basin, therefore, the threshold of 1800 is selected as the target threshold, the grid river network is extracted under the target threshold, the Strahler method is used to classify the river network and divide the sub-basins, on the basis of the division of the basin, the divided basin data is imported into the WEPP / GeoWEPP model, the two parameters of the key source area CSA and the minimum source channel length MSCL are determined, the slope flow process is simulated to generalize the slope, the slope width factor is input manually, and the slope width visualization is realized through the GIS software.

[0079] Specifically, in this embodiment, the step of normalizing the extreme value of the slope factor in step S2 specifically includes:

[0080] The slope factors L, S, LS and W factor values obtained in step S1 are normalized, that is, the normalized values of the terrain factors are calculated by using the extreme value normalization method of each slope factor, and then each terrain factor is mapped to 0-1; the calculation formula is G = (X-X min ) / (X max -X min), to obtain the value G of the normalized terrain factor of the S factor S , to obtain the value G of the normalized terrain factor of the L factor S , to obtain the value G of the normalized terrain factor of the LS factor LS , and to obtain the value G of the normalized terrain factor of the W factor W .

[0081] Specifically, in the present embodiment, the slope factor is a grid DEM value matrix, and there is no special logical correlation between each other, and the weight fusion method needs to consider the correlation of specific applications, but the geometric algebra operation fusion method can not only complete the operation of two or more grid units, but also the fused comprehensive evaluation factor meets the condition of the original slope form information amount, therefore, the extreme value normalized terrain factor is fused by using the four ways of L*S*W, LS*W, L+S+W and LS+W, and the calculation formula is as follows:

[0082] LSW=G s *G l *G w (5)

[0083] LSW=G ls *G w (6)

[0084] LSW=G s +G l +G w (7)

[0085] LSW=G ls +G w (8)

[0086] In the formula, G s , G L , G LS and G W are the values of the normalized terrain factors of the S factor, the L factor, the LS factor and the W factor respectively, and LSW is the slope fusion factor.

[0087] Specifically, in the embodiment, the plurality of slope fusion factors are comprehensively analyzed and evaluated in step S4, and a target slope fusion factor is screened out. Specifically, the fused slope factor is still a grid DEM value matrix, and the target slope fusion factor is a grid DEM value matrix. A large enough statistical sample is set, and all statistical data are normally distributed. The variance matrix value and the standard deviation matrix mean are used as mathematical statistical analysis indexes to represent the deviation degree of the original factor and the fusion factor. The original factor is the slope factor, and the fusion factor is the slope fusion factor. The greater the mean value is, the greater the deviation between the fusion factor and the original factor is. The information entropy is used to represent the sum of information provided by each grid, which can effectively evaluate the amount of information carried by the fusion factor. The information entropy calculation formula is H=-∑P i (x)lnP i (x), wherein H represents information entropy, x is a random variable, and P is the probability of the occurrence of the random variable.

[0088] Specifically, it is assumed that the contribution degrees of the L factor, the S factor, the LS factor and the W factor are consistent, and the normalized slope factor value range is 0-1. The relationship between the slope fusion factor and the slope factor obtained by the analysis software is as shown in Figure 6 , i represents LSW=G s +G l +G w , ii represents LSW=G ls +G w , iii represents LSW=G ls +G w , and iv represents LSW=G s +G l +G w . It can be known from Figure 6 that the slope factors obtained by LSW=G ls +G w and LSW=G s +G l +G w suffer from compression to different degrees, which is not conducive to balancing the contribution of each slope factor to the slice slope fusion factor. However, the slope fusion factors obtained by LSW=G s +G l +G w and LSW=G ls +G w will not suffer from compression. Combined with the statistical analysis of the LSW factors fused by the four methods by using the analysis software, it can be obtained that the slope fusion factor obtained by LSW=G s +Gl +G w The slope fusion factor obtained by this method carries the most information. That is, by comprehensively analyzing and evaluating the slope factors and slope fusion factors through the calculation and comparison of information entropy, the mean of variance and standard deviation matrices, the relationship between slope factors and slope fusion factors, and other basic statistics, the target slope fusion factor can be screened out through the L+S+W method.

[0089] Specifically, in this embodiment, a 5m raster DEM is used as the basic data source, with a raster row and column of 400×400. A small watershed in the Loess Plateau is selected as a typical example for research and analysis. The DEM and shaded rendering of the experimental area are shown below. Figure 7 , 8 As shown, the maximum elevation of the experimental area is 1062.6m, the minimum is 851.4m, the elevation difference is 211.3m, the raster DEM resolution is 5m, with a total of 160,000 pixels, and the total area of ​​the experimental area is 4km². 2 The slope factors were obtained from the raster DEM data, as shown in Figure 9. Here, A represents the L factor of the experimental area, B represents the S factor, C represents the LS factor, and D represents the W factor. The obtained slope factors were then normalized and multi-factor fusion was performed. The slope fusion factor was obtained according to four fusion methods: L*S*W, LS*W, L+S+W, and LS+W, as shown in Figure 10. Here, a represents LSW = G. s +G l +G w The result of LSW obtained by the fusion method, where b represents LSW = G ls +G w The result of LSW obtained by the fusion method, where c represents LSW = G s *G l *G w The result of LSW obtained by the fusion method, where d represents LSW = G ls *G w The LSW results obtained by the fusion methods are analyzed and evaluated comprehensively. Information entropy is used to represent the sum of information provided by each raster. Statistical analysis is performed on the LSW factors extracted using the four fusion methods to obtain the pixel statistics of each slope raster and the statistical regularity of the slope fusion factor LSW. Specific values ​​are shown in Table 2.

[0090]

[0091] Table 2 shows the statistical results of LSW factors extracted using four fusion methods.

[0092] via LSW=G s +G l +G w and LSW=G ls +Gw The extreme mean difference of the slope body fusion factor obtained is larger than LSW=G s *G l *G w , LSW=G ls *G w The extreme mean difference of the slope body fusion factor obtained in this way is larger than LSW=G s +G l +G w , and LSW=G ls +G w The data distribution of the slope body fusion factor obtained is more uniform, and for LSW=G s *G l *G w , and LSW=G ls *G w In this way, the slope body fusion factor value is compressed to varying degrees in the overall, and the fusion slope body factor statistical value is not conducive to balancing the contribution of each factor to the slope body fusion factor. As can be seen from the grid data distribution and fusion result diagram effect represented by FIG. 10, in the LSW=G ls +G w way, the slope width factor occupies the dominant position, leading to the fusion result being almost the same as the slope width factor, and the standard deviation of the grid data is larger than that obtained in other ways, that is, the slope body fusion factor obtained in the way has greater data volatility and is not representative. Therefore, the grid data obtained by LSW=G s +G l +G w fusion method conforms to the normal distribution, the standard deviation is small, is relatively stable, and is relatively strong in representation. The smaller the mean value of the variance matrix, the higher the correlation between the slope body fusion factor obtained after fusion and the original terrain factor, and the more effectively and comprehensively the multiple original terrain factors can be represented. On the contrary, the larger the mean value, the greater the deviation of the fusion factor from the multiple characteristics of the original factor, and the original terrain factor cannot be balanced. The slope body fusion factor obtained by LSW=G s +G l +G w fusion method has the largest H value of 1.0003, indicating that it contains the most information. In summary, the target slope body fusion factor can be obtained by LSW=G s +G l +G w fusion method, which is a balanced fusion slope factor magnitude. The slope body fusion factor value is equal to the normalized and average value of the slope factor, LSW=1 / 3(G s +G l +G w ).

[0093] Example 2:

[0094] As Figure 2 shown, Figure 2 is a structural diagram of a slope factor fusion system involved in an embodiment of the present application.

[0095] As Figure 2 shown, the system can include a processor 10, such as a CPU, a communication bus 12, a user interface 13, a network interface 14, and a memory 15. The communication bus 12 is used to realize the connection communication between the components. The user interface 13 can be an infrared receiving module for receiving control instructions triggered by a user through a remote control. The user interface 13 can also include a standard wired interface and a wireless interface. The network interface 14 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 15 can be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 15 can also be a storage device independent of the aforementioned processor 11.

[0096] Those skilled in the art can understand that the structure of the slope factor fusion system shown in Figure 2 does not constitute a limitation on the slope factor fusion system, and can include more or fewer components than shown, or combine certain components, or different component arrangements.

[0097] The specific embodiments of the slope factor fusion system of the present application are basically the same as the above-mentioned slope factor fusion method, and will not be repeated here.

[0098] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made under the inventive concept of the present application, or direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. A method of fusing slope factors, characterized by, The slope factor fusion method comprises the following steps: S1, obtaining slope factors from grid DEM data, wherein the slope factors comprise L factor, S factor, LS factor and W factor; the specific steps for obtaining W factor in step S1 are as follows: S111, performing depression filling on the grid DEM data to obtain non-depression grid DEM data; S112, obtaining flow accumulation from the non-depression grid DEM data; S113, determining a target threshold of the flow accumulation by using a river network density second-order derivative analysis method; S114, extracting a grid river network at the target threshold of the flow accumulation, and dividing sub-basins by using a Strahler method to grade the river network; S115, performing slope surface generalization on the divided basin data by using a WEPP / GeoWEPP model to finally determine the W factor; The specific steps for obtaining L factor, S factor and LS factor in step S1 are as follows: S121, generating a multi-flow direction grid layer by using a hydrological analysis tool according to the non-depression grid DEM data; S122, the grid layer is processed by cyclic iteration to obtain the slope length and L factor; specifically: according to the multi-flow direction grid layer, define the local high point as the slope length accumulation calculation starting point, continuously extract the runoff ending point, process by cyclic iteration to obtain the slope length and L factor; S123、extracting slope from the no-depression grid DEM data and calculating S factor according to slope classification function; S124, obtaining LS factor by extracting terrain indexes of the L factor and the S factor; the LS factor is: ； wherein is a factor, is the L factor, is a factor; S2, the slope factor extreme value normalization processing, get normalized terrain factor; Specifically: the slope factor obtained in step S1 is calculated by using the extreme value normalization method to obtain the value of the normalized terrain factor; the value of the normalized terrain factor is mapped to 0-1; finally, the value of the normalized terrain factor of the S factor is obtained , the value of the normalized terrain factor of the L factor , the value of the normalized terrain factor of the LS factor , and the value of the normalized terrain factor of the W factor ; S3, the normalized terrain factors are fused by multiple factors to obtain multiple slope body fusion factors; specifically, the extreme value normalized terrain factors are fused by four ways of 、 、 and ​ S4, comprehensively analyzing and evaluating a plurality of slope fusion factors to screen out a target slope fusion factor; the slope factor is a grid DEM value matrix; In step S4, the fused slope factor is a grid DEM value matrix, the target slope fusion factor is a grid DEM value matrix, a statistical sample is set, all statistical data are normally distributed, a variance matrix value and a standard deviation matrix mean are used as mathematical statistical analysis indexes to represent the deviation degree of an original factor and a fused factor, the original factor is a slope factor, the fused factor is a slope fusion factor, and the greater the mean value is, the greater the deviation between the fused factor and the original factor is; information entropy is used to represent the sum of information provided by each grid, to evaluate the amount of information carried by the fused factor, and the information entropy is: ； Wherein H represents information entropy, x is a random variable, and P is the probability of occurrence of the random variable.

2. The method of fusing slope factors according to claim 1, wherein, Before the depression filling on the grid DEM data, the following steps are further included: Judging whether the grid DEM data has depression or not, and if so, filling the depression of the grid DEM data to obtain non-depression grid DEM data.

3. The method of fusing slope factors of claim 1, wherein, The target threshold is 1800.

4. A fusion system of slope factors, characterized by, The application comprises a processor, a memory, and an application program of slope factor fusion stored on the memory and executable on the processor, and the application program of slope factor fusion implements the steps of the slope factor fusion method in any one of claims 1 to 3 when executed.

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