Pass microtopography degree grading method, device and equipment and medium

Abstract

The invention relates to a bealock microtopography degree grading method and device, equipment and a medium. The method comprises the following steps: acquiring DEM data of a target area; performing feature recognition on the DEM data to obtain terrain recognition features of the target area; according to the terrain recognition features, a bealock micro-terrain corresponding to the target area is determined, and an elevation value corresponding to the bealock micro-terrain is acquired; according to the elevation value, primary grade classification of the bealock microtopography is determined; and according to historical icing data corresponding to the bealock microtopography, correcting the preliminary grade classification to obtain a target grade classification of the bealock microtopography. Through the method, disaster prevention of the power transmission line can be effectively guided based on target grade classification, and the disaster risk of a power grid is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of degree classification, and in particular to a method, device, equipment and medium for classifying the degree of pass micro-topography. Background Art

[0002] In recent years, the collapse, disconnection and insulator flashover caused by icing of transmission lines have had a great impact on the power supply in many areas of China, resulting in huge economic losses. At present, there have been a lot of basic research on the mechanism of icing of transmission lines at home and abroad. Lu Jiazheng, Zeng Min and others proposed the classification results of 14 "two micro" areas, and proposed a line icing grade discrimination model and icing contrast model based on the degree of influence of each micro-topography on the icing of transmission lines. However, existing studies rarely define the micro-topography area of ​​the pass of power lines from the principle of geomorphology, and there are also few studies on the degree of grading of the pass micro-topography from a quantitative perspective.

[0003] At present, the research on the classification of pass levels by scholars at home and abroad mainly includes engineering geological conditions, geomorphological characteristics, and stability assessment.

[0004] Engineering geological conditions include rock mass properties and groundwater conditions. Rock mass properties are the rock mass properties of the pass that directly affect its stability and engineering properties. For example, the fault fracture zone type pass in the structural pass is not suitable for tunnel excavation and deep and large road cuttings due to severe rock mass fragmentation, low strength, and poor engineering properties. The erosion type pass is formed by strong external erosion as the dominant factor. The loose covering layer is very thin, the rock foundation is mostly exposed, and the stability of the road cutting slope is better. The groundwater condition is that groundwater also has an important influence on the stability of the pass. If the groundwater is shallowly buried in the erosion accumulation type pass, it is not easy to take deep excavation, because the groundwater may cause the slope to become unstable.

[0005] Topographic features include altitude, terrain undulation and degree of weathering and erosion. Altitude and terrain undulation are the main factors affecting the pass. The pass is usually located at the saddle-shaped depression of a tall mountain ridge, which is a saddle-shaped obvious depression on the ridge. Its altitude and terrain undulation directly affect the difficulty and safety of traffic. The degree of weathering and erosion is also an important basis for classifying its degree. The stability and bearing capacity of a severely weathered pass are relatively poor, and more stringent engineering measures need to be taken to ensure safety.

[0006] Stability assessment includes slope stability and bearing capacity. Slope stability is one of the key indicators for evaluating the grade of a pass. The stability of a slope is affected by many factors, such as rock mass properties, groundwater, topography, and landforms. In the stability assessment, these factors need to be considered comprehensively, and corresponding engineering measures need to be taken to ensure the stability of the slope. Bearing capacity is the bearing capacity of a pass and is also an important consideration for classifying its grade. For passes with poor bearing capacity, more caution is needed in human activities such as transportation and construction to avoid safety accidents.

[0007] Therefore, ice cover has become a major threat to the safe operation of power transmission lines. Some sections of power transmission lines are often affected by micro-topography, among which the micro-topography of the pass is one of the main factors for power transmission line accidents. However, based on the accident cases that have occurred, researchers have found that the risks to power transmission lines at different passes in the same area are very different.

[0008] However, there is no effective means in the existing technology to accurately analyze the specific distribution of the micro-topography of the pass. Therefore, it is necessary to conduct a deeper study on the micro-topography of the pass and then summarize a method for classifying the degree of the micro-topography of the pass. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide a method, device, equipment and medium for classifying the degree of micro-topography at a pass, aiming to solve at least one of the above-mentioned technical problems.

[0010] In the first aspect, the technical solution of the present invention to solve the above technical problem is as follows: a method for classifying the degree of micro-topography at a pass, the method comprising:

[0011] Obtain DEM data of the target area;

[0012] Performing feature recognition on the DEM data to obtain terrain recognition features of the target area;

[0013] Determine the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtain the elevation value corresponding to the pass micro-topography;

[0014] Determining a preliminary grade classification of the pass microtopography according to the elevation value;

[0015] The preliminary classification is corrected according to the historical ice cover data corresponding to the micro-topography of the pass to obtain the target classification of the micro-topography of the pass.

[0016] The beneficial effects of the present invention are as follows: terrain recognition features characterizing the terrain features of the target area are obtained based on the DEM data of the target area, the pass microtopography corresponding to the target area can be obtained based on the terrain recognition features, the preliminary grade classification of the pass microtopography can be obtained based on the elevation value corresponding to the pass microtopography, and the preliminary grade classification can be corrected based on the historical ice cover data corresponding to the pass microtopography to obtain the target grade classification of the pass microtopography, so that based on the target grade classification, the disaster prevention of the transmission line can be effectively guided and the disaster risk of the power grid can be reduced.

[0017] Based on the above technical solution, the present invention can also be improved as follows.

[0018] Further, the obtaining of DEM data of the target area includes:

[0019] Select the DEM digital elevation data dataset in the geospatial data cloud;

[0020] According to the target area, the DEM data of the target area is retrieved from the data set and downloaded.

[0021] Further, the performing feature recognition on the DEM data to obtain the terrain recognition features of the target area includes:

[0022] The DEM data is subjected to feature recognition through the geospatial software ArcGis to obtain the terrain recognition features of the target area, wherein the terrain recognition features include slope aspect, slope gradient, slope aspect variation rate, terrain feature lines and terrain feature points, wherein the terrain feature lines include ridge lines and valley lines, and the terrain feature points include ridge points and valley points.

[0023] Further, the elevation values ​​include elevation values ​​ΔH1 and ΔH2 on both sides of the mountain and an elevation value Δh at the bottom of the mountain. The preliminary classification of the micro-topography of the pass is determined based on the elevation values, including:

[0024] According to the ΔH1 or the ΔH2, a difference algorithm is used to obtain a relative height difference H of the mountain;

[0025] Based on the relative height difference H, a preliminary grade classification of the pass microtopography is obtained.

[0026] Further, the preliminary classification is corrected according to the historical ice cover data corresponding to the micro-topography of the pass, and the target classification of the micro-topography of the pass is obtained, including:

[0027] Determining the intermediate level classification of the pass micro-topography according to the historical ice cover data corresponding to the pass micro-topography;

[0028] Determining whether the intermediate level classification and the preliminary level classification are the same level classification;

[0029] If they are the same level classification, the preliminary level classification is determined as the target level classification; if they are not the same level classification, the preliminary level classification is corrected according to the intermediate level classification so that the target level classification is the intermediate level classification.

[0030] In a second aspect, in order to solve the above technical problems, the present invention further provides a device for classifying the degree of micro-topography at a pass, the device comprising:

[0031] Acquisition module, used to obtain DEM data of the target area;

[0032] A feature recognition module is used to perform feature recognition on the DEM data to obtain terrain recognition features of the target area;

[0033] A pass micro-topography determination module, used to determine the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtain the elevation value corresponding to the pass micro-topography;

[0034] A preliminary classification determination module, used to determine the preliminary classification of the pass micro-topography according to the elevation value;

[0035] The target level classification determination module is used to correct the preliminary level classification according to the historical ice cover data corresponding to the pass micro-topography to obtain the target level classification of the pass micro-topography.

[0036] In the third aspect, in order to solve the above-mentioned technical problem, the present invention also provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method for classifying the degree of pass micro-topography of the present application is implemented.

[0037] In a fourth aspect, in order to solve the above-mentioned technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for classifying the degree of pass micro-topography of the present application is implemented.

[0038] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments of the present invention are briefly introduced below.

[0040] Figure 1 A schematic flow chart of a method for grading the degree of micro-topography at a pass provided by an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of a pass map screened out through ArcMap provided by an embodiment of the present invention;

[0042] Figure 3 A topographic diagram of a pass micro-topography provided by an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of the structure of a device for grading the degree of micro-topography at a pass provided by one embodiment of the present invention;

[0044] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0046] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0047] The solution provided by the embodiment of the present invention can be applied to any application scenario where the target level classification of the pass micro-topography needs to be determined. The solution provided by the embodiment of the present invention can be executed by any electronic device, for example, it can be a user's terminal device, including at least one of the following: a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0048] The embodiment of the present invention provides a possible implementation method, such as Figure 1 As shown, a flowchart of a method for classifying the micro-topography degree of a pass is provided. The scheme can be executed by any electronic device, for example, a terminal device, or a terminal device and a server. For the convenience of description, the method provided by the embodiment of the present invention will be described below by taking the terminal device as the execution subject as an example. Figure 1 The method may include the following steps:

[0049] Step S10, obtaining DEM data of the target area;

[0050] Step S20, performing feature recognition on the DEM data to obtain terrain recognition features of the target area;

[0051] Step S30, determining the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtaining the elevation value corresponding to the pass micro-topography;

[0052] Step S40, determining a preliminary grade classification of the pass micro-topography according to the elevation value;

[0053] Step S50, modifying the preliminary classification according to the historical ice cover data corresponding to the pass micro-topography to obtain the target classification of the pass micro-topography.

[0054] Through the method of the present invention, a terrain recognition feature characterizing the terrain features of the target area is obtained based on the DEM data of the target area, a pass microtopography corresponding to the target area can be obtained based on the terrain recognition feature, a preliminary grade classification of the pass microtopography can be obtained based on the elevation value corresponding to the pass microtopography, and the preliminary grade classification can be corrected based on the historical ice cover data corresponding to the pass microtopography to obtain a target grade classification of the pass microtopography, so that based on the target grade classification, disaster prevention of transmission lines can be effectively guided and disaster risks of power grids can be reduced.

[0055] The solution of the present invention is further described below in conjunction with the following specific embodiments. In this embodiment, a method for classifying the degree of pass micro-topography may include the following steps:

[0056] Step S10, obtaining DEM (Digital Elevation Model) data of the target area;

[0057] Among them, the target area refers to the area where the pass is located, and the pass refers to the pass of the power line.

[0058] Optionally, in S10, obtaining DEM data of the target area includes:

[0059] S101, selecting a dataset of DEM digital elevation data in a geospatial data cloud;

[0060] S102: searching from the data set according to the target area, obtaining the DEM data of the target area and downloading it.

[0061] The DEM digital elevation data may be GDEMV3 30M resolution digital elevation data.

[0062] The data set may include DEM data corresponding to different regions, thereby obtaining DEM data of the target region in the data set.

[0063] Step S20, performing feature recognition on the DEM data to obtain terrain recognition features of the target area;

[0064] The terrain identification feature is data that characterizes the terrain features of the target area.

[0065] In the above S20, feature recognition is performed on the DEM data to obtain the terrain recognition features of the target area, including:

[0066] S201, performing feature recognition on the DEM data through the geospatial software ArcGis to obtain terrain recognition features of the target area, wherein the terrain recognition features include slope aspect, slope gradient, slope aspect variation rate, terrain feature lines and terrain feature points, wherein the terrain feature lines include ridge lines and valley lines, and the terrain feature points include ridge points and valley points.

[0067] Optionally, the above S201, performing feature recognition on the DEM data by using the geospatial software ArcGis to obtain the terrain recognition features of the target area, may include:

[0068] Add the downloaded DEM data into ArcGIS, and perform splicing, cropping, and feature annotation operations on the raster images corresponding to the DEM data, and save them as dem layers;

[0069] The DEM data was processed using the slope aspect function under the surface analysis under the Spatial Analyst tool under ArcToolbox to obtain the slope aspect map of the pass microtopography and save it as aspect.tif data.

[0070] The DEM data was processed using the slope function under the surface analysis under the Spatial Analyst tool under ArcToolbox to obtain the slope map of the target pass microtopography and save it as soil.tif data;

[0071] Based on the soil.tif data, the inverse terrain DEM data is obtained through the raster calculator under the map algebra under the Spatial Analyst tool under the ArcToolbox tool (the expression is (1153.79-"dem")) and saved as f_dem.tif data;

[0072] Based on the inverse terrain DEM data, the aspect value (also called slope direction) is obtained through the slope aspect function under the surface analysis under the Spatial Analyst tool under the ArcToolbox tool, and saved as f_aspect.tif data;

[0073] Based on the f_aspect.tif data, the slope value (also called slope) is obtained through the slope corresponding function under the surface analysis under the Spatial Analyst tool under the ArcToolbox tool, and saved as soa2.tif data;

[0074] Based on the obtained soa1.tif data and soa2.tif data, the slope aspect variability of the DEM data without error is calculated in the raster calculator under the map algebra under the SpatialAnalyst tool under ArcToolbox and saved as soa.tif data. The calculation formula of the slope aspect variability soa is:

[0075] soa=(([soa1]+[soa2])-Abs([soa1]-[soa2])) / 2;

[0076] Based on the DEM data, select the focal statistics function under the neighborhood analysis under the ArcToolbox-Spatial Analyst tool in the toolbox, set Statisticstype to mean value (MEAN), obtain the mean value level of the DEM data, and save it as mean.tif data;

[0077] In the toolbox, select the corresponding function of the raster calculator under the map algebra under the ArcToolbox-Spatial Analyst tool, and obtain the positive and negative terrain distribution areas under the raster calculator;

[0078] The formula is: "dem"-"mean", saved as "zhengfudixing.tif" data;

[0079] In the toolbox, select the corresponding function of the raster calculator under the map algebra under the ArcToolbox-Spatial Analyst tool, obtain the ridge line in the raster calculator, and save it as the "shanjixian.tif" data. The calculation formula of the ridge line is: zhengfudixing>0&soa.tif>70; that is, when zhengfudixing>0 and soa.tif>70, the corresponding area is the ridge line;

[0080] In the toolbox, select the corresponding function of the raster calculator under the map algebra under the ArcToolbox-Spatial Analyst tool, obtain the valley line in the raster calculator, and save it as the "shanguxian.tif" data. The calculation formula for the valley line is: shanjixian.tif<0&soa.tif>70; that is, when shanjixian.tif<0 and soa.tif>70, the corresponding area is the valley line.

[0081] Step S30, determining the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtaining the elevation value corresponding to the pass micro-topography;

[0082] On the basis of the extracted terrain identification features, a specific analysis is carried out on the factors contained in each terrain identification feature to further determine the pass microtopography, among which the terrain identification feature factors include slope aspect, slope gradient, slope aspect variation rate, terrain characteristic lines and terrain characteristic points; terrain characteristic lines include ridge lines and valley lines; according to the distribution characteristics of ridge lines and valley lines, combined with the terrain structure characteristics of the pass microtopography, the pass microtopography in the target area can be identified.

[0083] As an example, the pass microtopography can be found in Figure 2 .

[0084] Among them, see Figure 3 The structural characteristics of the pass micro-topography are as follows:

[0085] ΔH1, ΔH2 ≥ 100m; that is, ΔH1 and ΔH2 are greater than 100 meters respectively;

[0086]

[0087] tanα,tanβ≥37%; that is, tanα and tanβ are respectively greater than 0.37.

[0088] L1 and L2 are the horizontal distances from point a and point b to point P, α and β are the angles between the lines connecting point a, point b and point P and the horizontal direction. △H1 and △H2 are the distances between point a and point b and the horizontal plane.

[0089] Step S40, determining a preliminary grade classification of the pass micro-topography according to the elevation value;

[0090] The elevation values ​​include the elevation values ​​ΔH1 and ΔH2 on both sides of the mountain and the elevation value Δh at the bottom of the mountain.

[0091] Optionally, the elevation value corresponding to the micro-topography of the pass can be obtained based on map software (such as Shuijing Micro-map). Map software (such as Shuijing Micro-map) can also be used to mark the identified pass to facilitate subsequent research on the icing problem of the transmission line.

[0092] Optionally, in the above S40, determining the preliminary level classification of the pass micro-topography according to the elevation value includes:

[0093] S401, using a difference algorithm according to the ΔH1 or the ΔH2 to obtain a relative height difference H of the mountain;

[0094] Where, H = ΔH1-Δh;

[0095] H = ΔH2-Δh;

[0096] S402, based on the relative height difference H, obtaining a preliminary grade classification of the pass micro-topography, which may also be called a preliminary pass micro-topography degree classification map.

[0097] Step S50, modifying the preliminary classification according to the historical ice cover data corresponding to the pass micro-topography to obtain the target classification of the pass micro-topography.

[0098] Optionally, the above S50, according to the historical ice cover data corresponding to the pass micro-topography, corrects the preliminary classification to obtain the target classification of the pass micro-topography, including:

[0099] S501, determining an intermediate level classification of the pass micro-topography according to historical ice cover data corresponding to the pass micro-topography;

[0100] S502, determining whether the intermediate level classification and the preliminary level classification are the same level classification;

[0101] S503: If they are the same level classification, the preliminary level classification is determined as the target level classification; if they are not the same level classification, the preliminary level classification is modified according to the intermediate level classification so that the target level classification is the intermediate level classification.

[0102] The analysis found that the pass area with severe ice coverage is relatively large, while the pass area with light ice coverage is relatively small; therefore, different ice cover thicknesses can correspond to different level classifications. Taking the level classification corresponding to historical ice cover data as the target, the preliminary level classification of the pass microtopography can be verified according to the degree of ice cover.

[0103] As an example, from the four sets of data comparison table of ice cover and elevation difference (relative height difference H) in Henan from 2023 to 2024 (Table 1), it can be seen that the ice thickness is closely related to the elevation difference. The specific result is that the elevation difference H is within 70 meters, which is relatively light ice cover. The greater the elevation difference, the thicker the ice cover. Therefore, the ice cover situation verifies the correctness and practical significance of the preliminary classification of the pass micro-topography, and can provide safety guidance for the operation of the power grid.

[0104] Table 1

[0105]

[0106] It should be noted that the above embodiment only describes the pass type among the five major micro-topography, and the degree level research methods of the other four micro-topography are similar to the pass type, which will not be further explained here.

[0107] On the basis of the above scheme, the correspondence between different elevation values ​​and different grade classifications can be established based on the relationship between ice cover conditions, elevation differences and grade classifications (pass degree levels). Based on the elevation values ​​of the pass microtopography and the correspondence, the target grade classification of the pass microtopography (also called a degree grade map) can be obtained.

[0108] Optionally, the method may further include:

[0109] Retrieve and download vector graphics of the target area from the national geographic information system.

[0110] Based on Figure 1 Based on the same principle as the method shown in , the embodiment of the present invention also provides a device 20 for classifying the micro-topography degree of a pass, such as Figure 4 As shown in , the pass micro-topography degree classification device 20 may include an acquisition module 210, a feature recognition module 220, a pass micro-topography determination module 230, a preliminary level classification determination module 240 and a target level classification determination module 250, wherein:

[0111] An acquisition module 210 is used to acquire DEM data of a target area;

[0112] A feature recognition module 220 is used to perform feature recognition on the DEM data to obtain terrain recognition features of the target area;

[0113] A pass micro-topography determination module 230 is used to determine the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtain the elevation value corresponding to the pass micro-topography;

[0114] A preliminary level classification determination module 240 is used to determine the preliminary level classification of the pass micro-topography according to the elevation value;

[0115] The target level classification determination module 250 is used to modify the preliminary level classification according to the historical ice cover data corresponding to the pass micro-topography to obtain the target level classification of the pass micro-topography.

[0116] Optionally, when acquiring the DEM data of the target area, the acquisition module 210 is specifically used to:

[0117] Select the DEM digital elevation data dataset in the geospatial data cloud;

[0118] According to the target area, the DEM data of the target area is retrieved from the data set and downloaded.

[0119] Optionally, when the feature recognition module 220 performs feature recognition on the DEM data to obtain the terrain recognition features of the target area, it is specifically used to:

[0120] The DEM data is subjected to feature recognition through the geospatial software ArcGis to obtain the terrain recognition features of the target area, wherein the terrain recognition features include slope aspect, slope gradient, slope aspect variation rate, terrain feature lines and terrain feature points, wherein the terrain feature lines include ridge lines and valley lines, and the terrain feature points include ridge points and valley points.

[0121] Optionally, the elevation values ​​include elevation values ​​ΔH1 and ΔH2 on both sides of the mountain and an elevation value Δh at the bottom of the mountain. When the preliminary level classification determination module 240 determines the preliminary level classification of the pass micro-topography according to the elevation values, it is specifically used to:

[0122] According to the ΔH1 or the ΔH2, a difference algorithm is used to obtain a relative height difference H of the mountain;

[0123] Based on the relative height difference H, a preliminary grade classification of the pass microtopography is obtained.

[0124] Optionally, when the target level classification determination module 250 amends the preliminary level classification according to the historical ice cover data corresponding to the pass micro-topography to obtain the target level classification of the pass micro-topography, it is specifically used to:

[0125] Determining the intermediate level classification of the pass micro-topography according to the historical ice cover data corresponding to the pass micro-topography;

[0126] Determining whether the intermediate level classification and the preliminary level classification are the same level classification;

[0127] If they are the same level classification, the preliminary level classification is determined as the target level classification; if they are not the same level classification, the preliminary level classification is corrected according to the intermediate level classification so that the target level classification is the intermediate level classification.

[0128] The device for grading the pass micro-topography degree in the embodiment of the present invention can execute the method for grading the pass micro-topography degree provided by the embodiment of the present invention, and the implementation principle is similar. The actions performed by each module and unit in the device for grading the pass micro-topography degree in each embodiment of the present invention correspond to the steps in the method for grading the pass micro-topography degree in each embodiment of the present invention. For the detailed functional description of each module of the device for grading the pass micro-topography degree, please refer to the description in the corresponding method for grading the pass micro-topography degree shown in the previous text, which will not be repeated here.

[0129] Among them, the above-mentioned pass micro-topography degree classification device can be a computer program (including program code) running in a computer device, for example, the pass micro-topography degree classification device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present invention.

[0130] In some embodiments, the pass micro-topography degree grading device provided in the embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the pass micro-topography degree grading device provided in the embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the pass micro-topography degree grading method provided in the embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application specific integrated circuits (ASIC, Application Specific Integrated Circuit), DSP, programmable logic device (PLD, Programmable Logic Device), complex programmable logic device (CPLD, Complex Programmable Logic Device), field programmable gate array (FPGA, Field-Programmable Gate Array) or other electronic components.

[0131] In other embodiments, the device for classifying the pass micro-topography degree provided by the embodiment of the present invention can be implemented in a software manner. Figure 4A pass micro-topography degree grading device stored in a memory is shown, which can be software in the form of a program and a plug-in, and includes a series of modules, including an acquisition module 210, a feature recognition module 220, a pass micro-topography determination module 230, a preliminary grade classification determination module 240 and a target grade classification determination module 250, for implementing the pass micro-topography degree grading method provided in an embodiment of the present invention.

[0132] The modules involved in the embodiments of the present invention may be implemented by software or hardware, wherein the name of a module does not limit the module itself in some cases.

[0133] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0134] In an alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which may be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0135] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0136] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0137] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this.

[0138] The memory 4003 is used to store the application code (computer program) for executing the solution of the present invention, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0139] The electronic device may also be a terminal device. Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0140] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0141] According to another aspect of the present invention, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the implementation modes of the above-mentioned various embodiments.

[0142] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0143] It should be understood that the flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the module, the program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0144] The computer-readable storage medium provided by the embodiment of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0145] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0146] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the disclosure scope involved in the present invention is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present invention (but not limited to) by each other.

Claims

1. A method for classifying the degree of micro-topography at a pass, characterized in that: The following steps are involved: Obtain DEM data of the target area; Performing feature recognition on the DEM data to obtain terrain recognition features of the target area; Determine the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtain the elevation value corresponding to the pass micro-topography; Determining a preliminary grade classification of the pass microtopography according to the elevation value; The preliminary classification is corrected according to the historical ice cover data corresponding to the micro-topography of the pass to obtain the target classification of the micro-topography of the pass.

2. The method according to claim 1, characterized in that The step of obtaining DEM data of the target area includes: Select the DEM digital elevation data dataset in the geospatial data cloud; According to the target area, the DEM data of the target area is retrieved from the data set and downloaded.

3. The method according to claim 1, characterized in that The performing feature recognition on the DEM data to obtain the terrain recognition features of the target area includes: The DEM data is subjected to feature recognition through the geospatial software ArcGis to obtain the terrain recognition features of the target area, wherein the terrain recognition features include slope aspect, slope gradient, slope aspect variation rate, terrain feature lines and terrain feature points, wherein the terrain feature lines include ridge lines and valley lines, and the terrain feature points include ridge points and valley points.

4. The method according to claim 1, characterized in that: The elevation values ​​include the elevation values ​​ΔH1 and ΔH2 on both sides of the mountain and the elevation value Δh at the bottom of the mountain. The preliminary classification of the micro-topography of the pass is determined based on the elevation values, including: According to the ΔH1 or the ΔH2, a difference algorithm is used to obtain a relative height difference H of the mountain; Based on the relative height difference H, a preliminary grade classification of the pass microtopography is obtained.

5. The method according to claim 4, characterized in that The preliminary classification is corrected according to the historical ice cover data corresponding to the micro-topography of the pass to obtain the target classification of the micro-topography of the pass, including: Determining the intermediate level classification of the pass micro-topography according to the historical ice cover data corresponding to the pass micro-topography; Determining whether the intermediate level classification and the preliminary level classification are the same level classification; If they are the same level classification, the preliminary level classification is determined as the target level classification; if they are not the same level classification, the preliminary level classification is corrected according to the intermediate level classification so that the target level classification is the intermediate level classification.

6. A device for grading the micro-topography degree of a pass, characterized in that: include: Acquisition module, used to obtain DEM data of the target area; A feature recognition module is used to perform feature recognition on the DEM data to obtain terrain recognition features of the target area; A pass micro-topography determination module, used to determine the pass micro-topography corresponding to the target area according to the terrain recognition feature, and obtain the elevation value corresponding to the pass micro-topography; A preliminary classification determination module, used to determine the preliminary classification of the pass micro-topography according to the elevation value; The target level classification determination module is used to correct the preliminary level classification according to the historical ice cover data corresponding to the pass micro-topography to obtain the target level classification of the pass micro-topography.

7. The device according to claim 6, characterized in that When acquiring the DEM data of the target area, the acquisition module is specifically used to: Select the DEM digital elevation data dataset in the geospatial data cloud; According to the target area, the DEM data of the target area is retrieved from the data set and downloaded.

8. The device according to claim 6, characterized in that When the feature recognition module performs feature recognition on the DEM data to obtain the terrain recognition features of the target area, it is specifically used to: The DEM data is subjected to feature recognition through the geospatial software ArcGis to obtain the terrain recognition features of the target area, wherein the terrain recognition features include slope aspect, slope gradient, slope aspect variation rate, terrain feature lines and terrain feature points, wherein the terrain feature lines include ridge lines and valley lines, and the terrain feature points include ridge points and valley points.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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