A method, device and equipment for identifying geological disasters of a transmission line

Abstract

The present invention relates to the field of geological identification technology. Specifically, it relates to a method, device, and equipment for identifying geological disasters in transmission lines. The method includes: obtaining the location information of the transmission line, where the transmission line location information is the geographical location information where multiple power towers are erected; making a corresponding first remote sensing image according to the location information of the transmission line, and the first remote sensing image is the original satellite map along the transmission line; calculating a disaster zoning map according to the first remote sensing image, and the disaster zoning map is a map marked with multiple areas of different disaster levels. By making the first remote sensing image around the transmission line and obtaining the disaster zoning map marked with multiple areas of different disaster levels through the first remote sensing image, the present invention can comprehensively master the development trend and damage degree of geological disasters in the transmission line, providing a technical basis for timely taking prevention and control measures and post-disaster emergency disposal.

Description

Technical Field

[0001] The present invention relates to the field of geological identification technology, and more particularly, to a method, device, and equipment for identifying geological disasters of transmission lines. Background Art

[0002] Transmission lines cover a wide range and have a long transmission distance. They pass through many areas with harsh environmental conditions, complex geological terrains, and variable climates. At the same time, affected by human activities, various construction and excavation operations around transmission lines have a great impact on the originally fragile geological environment, easily leading to the inclination, deformation, and settlement of transmission line towers and foundations, seriously threatening the safe and stable operation of transmission lines. The engineering treatment difficulty and cost are huge. In some areas, the soil around power transmission and transformation facilities is loose, and soil erosion is serious, which is extremely likely to cause debris flows or landslides, and may cause tower collapses or line breaks, seriously affecting the reliability and stability of power grid operation. Therefore, comprehensively mastering the development trend and damage degree of geological disasters of transmission lines and providing a technical basis for timely taking prevention and control measures and post-disaster emergency disposal are of great significance for ensuring the safe operation of transmission lines and disaster prevention and mitigation of transmission lines. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, device, equipment, and readable storage medium for identifying geological disasters of transmission lines to improve the above problems.

[0004] To achieve the above purpose, the embodiments of the present application provide the following technical solutions:

[0005] On the one hand, the embodiments of the present application provide a method for identifying geological disasters of transmission lines, and the method includes:

[0006] Obtain the position information of the transmission line, where the transmission line position information is the geographical position information of multiple power towers; make a corresponding first remote sensing image according to the position information of the transmission line, and the first remote sensing image is the original satellite map along the transmission line; calculate a disaster zoning map according to the first remote sensing image, and the disaster zoning map is a map marked with multiple different disaster level areas.

[0007] Optionally, the calculating the disaster zoning map according to the first remote sensing image includes:

[0008] Retrieve the first remote sensing image and set initial segmentation parameters and multiple optical band weight parameters;

[0009] Build a segmentation model, and input the first remote sensing image, the initial segmentation parameters, and multiple optical band weight parameters into the segmentation model to calculate a first segmentation map. The segmentation model is used to adjust the segmentation parameters to accurately distinguish vegetation and landslides on the first remote sensing image. The first segmentation map includes multiple vegetation blocks and multiple landslide blocks;

[0010] Build a determination model, and input each of the vegetation blocks into the determination model respectively to obtain multiple landslide candidate blocks. The determination model is used to screen out the landslide candidate blocks in the vegetation blocks;

[0011] Build a geological disaster risk assessment model, and input each of the landslide blocks into the risk assessment model in sequence to obtain a first disaster area corresponding to each of the landslide blocks respectively. The first disaster area is a block marked with a disaster risk level;

[0012] Input each of the landslide candidate blocks into the risk assessment model in sequence to obtain a second disaster area corresponding to each of the landslide candidate blocks respectively. The second disaster area is a block marked with a disaster risk level;

[0013] Overlay the first disaster area and the second disaster area on the first remote sensing image to obtain the disaster zoning map.

[0014] Optionally, the building of the determination model and inputting each of the vegetation blocks into the determination model respectively to obtain multiple landslide candidate blocks includes:

[0015] Set the normalized difference vegetation index and multiple false positive thresholds. The normalized difference vegetation index is a characterization of the vegetation change caused by landslides, and the false positive thresholds are used to distinguish various building characterization thresholds that are misidentified as landslides;

[0016] Retrieve one of the vegetation blocks, denoted as the first vegetation block;

[0017] First detection and determination operation: Detect whether the index of the first vegetation block is less than the normalized difference vegetation index. If the index of the first vegetation block is less than the normalized difference vegetation index, then determine that this block is a block with a landslide, and divide multiple first landslide areas. The first landslide areas are areas in the first vegetation block where the local area index is less than the normalized difference vegetation index;

[0018] Second detection and determination operation: According to the multiple false positive thresholds, determine whether each of the first landslide areas is a false positive area, mark the first landslide areas determined to be false positives as candidate safe areas, record the remaining first landslide areas that are not determined to be false positive areas as candidate landslide areas, and mark the vegetation areas with at least one of the candidate landslide areas as candidate landslide blocks;

[0019] Retrieve another vegetation block, and sequentially perform the first detection and determination operation and the second detection and determination operation until the first detection and determination operation and the second detection and determination operation are performed on all the vegetation blocks;

[0020] Output multiple candidate landslide blocks.

[0021] Optionally, the constructing a geological disaster risk assessment model and sequentially bringing each of the landslide blocks into the risk assessment model to respectively obtain the first disaster areas corresponding to each of the landslide blocks includes:

[0022] Retrieve one of the landslide blocks, denoted as the first landslide block;

[0023] Retrieving geological data operation: According to the position information corresponding to the first landslide block, obtain the geological data corresponding to the first landslide block in the geological database, where the geological data is multiple geological parameters reflecting the location where the first landslide block is located;

[0024] Retrieving on-site survey data operation: According to the position information corresponding to the first landslide block, obtain the on-site survey data corresponding to the first landslide block in the on-site survey database, where the on-site survey data includes the distance to the river, the vegetation coverage rate, and the human activity rate;

[0025] Retrieving historical data operation: According to the position information corresponding to the first landslide block, obtain the first historical landslide data within the first landslide block;

[0026] Risk assessment operation; Bring the geological data, on-site survey data, and first historical landslide data corresponding to the first landslide block into the risk assessment model, and calculate to obtain the first disaster area corresponding to the first landslide block, where the first disaster area is a block marked with a disaster risk level;

[0027] Retrieve another landslide block, and sequentially perform the retrieving geological data operation, retrieving on-site survey data operation, retrieving historical data operation, and risk assessment operation until after all the landslide blocks are brought into the risk assessment model, multiple first disaster areas are obtained.

[0028] Second, an embodiment of the present application provides a geological disaster identification device for a transmission line, and the device includes:

[0029] A first acquisition module, configured to acquire transmission line location information, where the transmission line location information is geographical location information where a plurality of power towers are erected;

[0030] A first calculation module, configured to produce a corresponding first remote sensing image according to the location information of the transmission line, where the first remote sensing image is an original satellite map along the transmission line;

[0031] A second calculation module, configured to calculate a disaster zoning map according to the first remote sensing image, where the disaster zoning map is a map marked with a plurality of regions with different disaster levels.

[0032] Optionally, the second calculation module includes:

[0033] A first retrieval unit, configured to retrieve the first remote sensing image and set initial segmentation parameters and a plurality of optical band weight parameters;

[0034] A first calculation unit, configured to construct a segmentation model and input the first remote sensing image, the initial segmentation parameters, and the plurality of optical band weight parameters into the segmentation model, and calculate a first segmentation map. The segmentation model is used to adjust the segmentation parameters to accurately distinguish vegetation and landslides on the first remote sensing image. The first segmentation map includes a plurality of vegetation blocks and a plurality of landslide blocks;

[0035] A second calculation unit, configured to construct a determination model and input each of the vegetation blocks into the determination model respectively to obtain a plurality of landslide candidate blocks. The determination model is used to screen out landslide candidate blocks in the vegetation blocks;

[0036] A third calculation unit, configured to construct a geological disaster risk assessment model and input each of the landslide blocks into the risk assessment model in turn to obtain a first disaster area corresponding to each of the landslide blocks respectively. The first disaster area is a block marked with a disaster risk level;

[0037] A fourth calculation unit, configured to input each of the landslide candidate blocks into the risk assessment model in turn to obtain a second disaster area corresponding to each of the landslide candidate blocks respectively. The second disaster area is a block marked with a disaster risk level;

[0038] A fifth calculation unit, configured to superimpose the first disaster area and the second disaster area on the first remote sensing image to obtain the disaster zoning map.

[0039] Optionally, the second calculation unit includes:

[0040] A first calculation subunit for setting a normalized difference vegetation index and setting a plurality of false positive thresholds, where the normalized difference vegetation index is a representation of vegetation changes caused by landslides, and the false positive thresholds are used to distinguish various building representation thresholds that are misidentified as landslides;

[0041] A first retrieval subunit for retrieving one of the vegetation blocks, denoted as the first vegetation block;

[0042] A second calculation subunit for performing a first detection and determination operation: detecting whether the index of the first vegetation block is less than the normalized difference vegetation index. If the index of the first vegetation block is less than the normalized difference vegetation index, it is determined that the block has a landslide, and a plurality of first landslide areas are divided. The first landslide area is an area where the local area index in the first vegetation block is less than the normalized difference vegetation index;

[0043] A third calculation subunit for performing a second detection and determination operation: according to the plurality of false positive thresholds, respectively determining whether each of the first landslide areas is a false positive area, marking the first landslide areas determined to be false positives as candidate safe areas, recording the remaining first landslide areas that are not determined to be false positive areas as candidate landslide areas, and recording the vegetation blocks with at least one of the candidate landslide areas as candidate landslide blocks:

[0044] A fourth calculation subunit for retrieving another vegetation block and sequentially performing the first detection and determination operation and the second detection and determination operation until all of the plurality of vegetation blocks have been subjected to the first detection and determination operation and the second detection and determination operation;

[0045] A first output subunit for outputting a plurality of candidate landslide blocks.

[0046] Optionally, the third calculation unit includes:

[0047] A second retrieval subunit for retrieving one of the landslide blocks, denoted as the first landslide block:

[0048] A fifth calculation subunit for performing an operation of retrieving geological data: according to the location information corresponding to the first landslide block, obtaining geological data corresponding to the first landslide block in a geological database, where the geological data are a plurality of geological parameters reflecting the location where the first landslide block is located;

[0049] A sixth calculation subunit for performing an operation of retrieving on-site survey data: according to the location information corresponding to the first landslide block, obtaining on-site survey data corresponding to the first landslide block in an on-site survey database, where the on-site survey data includes the distance to a river, the vegetation coverage rate, and the human activity rate;

[0050] The seventh computing subunit is used to perform the operation of retrieving historical data: according to the position information corresponding to the first landslide block, obtain the first historical landslide data within the first landslide block;

[0051] The eighth computing subunit is used to perform the risk assessment operation; bring the geological data, on-site survey data, and the first historical landslide data corresponding to the first landslide block into the risk assessment model, and calculate the first disaster area corresponding to the first landslide block, where the first disaster area is a block marked with a disaster risk level;

[0052] The ninth computing subunit is used to retrieve another one of the landslide blocks, and sequentially perform the operations of retrieving geological data, retrieving on-site survey data, retrieving historical data, and risk assessment operations, until after all the multiple landslide blocks are brought into the risk assessment model, multiple first disaster areas are obtained.

[0053] In a third aspect, an embodiment of the present application provides a geological disaster identification device for a transmission line, and the device includes a memory and a processor. The memory is used to store a computer program; the processor is used to implement the steps of the above-mentioned geological disaster identification method for the transmission line when executing the computer program.

[0054] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned geological disaster identification method for the transmission line are implemented.

[0055] The beneficial effects of the present invention are as follows:

[0056] By making the first remote sensing image around the transmission line and preparing a disaster zoning map marked with multiple different disaster level areas through the first remote sensing image, the present invention comprehensively grasps the development trend and damage degree of geological disasters of the transmission line, providing a technical basis for timely taking prevention and control measures and post-disaster emergency disposal.

[0057] Other features and advantages of the present invention will be described in the subsequent description, and, in part, will become apparent from the description or be understood by implementing the embodiments of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0059] Figure 1 It is a schematic flow chart of a method for identifying geological disasters of a transmission line described in an embodiment of the present invention;

[0060] Figure 2 It is a schematic structural diagram of a device for identifying geological disasters of a transmission line described in an embodiment of the present invention;

[0061] Figure 3 It is a schematic structural diagram of a device for identifying geological disasters of a transmission line described in an embodiment of the present invention. Detailed implementation manners

[0062] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the detailed description of the embodiments of the present invention provided herein is not intended to limit the scope of the claimed invention, but is merely representative of selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0063] It should be noted that: similar reference numerals or letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0064] Embodiment 1

[0065] As Figure 1 shown, this embodiment provides a method for identifying geological disasters of a transmission line, and the method includes step S1, step S2 and step S3.

[0066] Step S1. Obtain the position information of the transmission line, where the position information of the transmission line is the geographical location information where a plurality of power towers are erected;

[0067] Step S2. Make a corresponding first remote sensing image according to the position information of the transmission line, where the first remote sensing image is the original satellite map along the transmission line;

[0068] In this embodiment, with the continuous development of remote sensing technology, the technical means of remote sensing image segmentation have also achieved leapfrog development. Many image segmentation algorithms have emerged. This embodiment specifically addresses the deficiency of traditional pixel-based calculations - pixel-independent analysis. This embodiment uses the Fractal Network Evolution Approach (FNEA). Through a bottom-up iterative region merging algorithm, an ascending scale threshold is used and hierarchical segmentation is flexibly established to meet the requirements of features at different scales. This segmentation method merges objects represented by the most suitable pixels in a small area, and combines the geometric shape information and multi-spectral information of high-resolution images. A principle called fuzzy set theory is used to extract the target area in order to adjust parameters and thus achieve multi-scale segmentation.

[0069] In addition, it should be noted that the selection of segmentation parameters in multi-scale image segmentation is very important and directly affects the quality of the segmentation effect. Among them, band weight, segmentation scale, and homogeneity factor are the three main parameters in multi-scale image segmentation.

[0070] Step S3. Based on the first remote sensing image, a disaster zoning map is calculated. The disaster zoning map is a map marked with multiple areas of different disaster levels.

[0071] The present invention makes the first remote sensing image around the transmission line, and obtains a disaster zoning map marked with multiple areas of different disaster levels through the first remote sensing image, thereby comprehensively grasping the development trend and damage degree of geological disasters of the transmission line, and providing a technical basis for timely taking prevention and control measures and post-disaster emergency disposal.

[0072] In a specific implementation manner of the present disclosure, in step S3, it may further include steps S31, S32, S33, S34, S35, and S36.

[0073] Step S31. Retrieve the first remote sensing image, and set initial segmentation parameters and multiple optical band weight parameters.

[0074] The selection of segmentation parameters in multi-scale image segmentation is very important and directly affects the quality of the segmentation effect. Band weight, segmentation scale, and homogeneity factor are the three main parameters in multi-scale image segmentation;

[0075] Step S32. Construct a segmentation model, and input the first remote sensing image, the initial segmentation parameters, and the multiple optical band weight parameters into the segmentation model. Calculate a first segmentation map. The segmentation model is used to adjust the segmentation parameters to accurately distinguish vegetation and landslides on the first remote sensing image. The first segmentation map includes multiple vegetation blocks and multiple landslide blocks;

[0076] After a landslide occurs, rocks and debris will be exposed and vegetation will be damaged, which is one of the most direct features for identifying landslides in remote sensing images. The Normalized Difference Vegetation Index (NDVI) is an obvious manifestation of vegetation changes caused by landslides. If the vegetation normalization index value in a certain area is less than a specific value, then this area can be regarded as a landslide. Selecting an appropriate NDVI value is the first and important step in the extraction process of landslides, which can exclude areas such as forest land and cultivated land that do not need to be considered. In this regard, through the K-means clustering algorithm, the threshold of NDVI can be obtained as 0.032, and the most suitable threshold of slope is 28 degrees. The above two thresholds can screen out places with small vegetation coverage areas as candidate landslide areas.

[0077] Step S33. Construct a determination model, and bring each of the vegetation blocks into the determination model respectively to obtain multiple landslide candidate blocks. The determination model is used to screen out the landslide candidate blocks in the vegetation blocks;

[0078] Step S34. Construct a geological disaster risk assessment model, and bring each of the landslide blocks into the risk assessment model in turn to obtain a first disaster area corresponding to each of the landslide blocks respectively. The first disaster area is a block marked with a disaster risk level;

[0079] In this embodiment, the geological disaster risk assessment model uses spatial overlay including vector and raster analysis functions, and represents each factor affecting landslides with a thematic map. In the vector-based spatial overlay analysis, multiple thematic layers are superimposed on each other to generate a new layer with the attributes of the original multiple thematic maps. In the raster-based spatial overlay analysis, corresponding four arithmetic operations or function operations are performed between the rasters corresponding to each layer to obtain a new raster thematic map. When conducting a landslide geological disaster hazard assessment and analysis study, it is necessary to divide the entire assessment area into regular or irregular units to form a large number of units, and then assign values to each unit in turn according to different assessment factors.

[0080] In this embodiment, the risk assessment model can be a weighted summation model, and the risk level corresponding to each landslide block is obtained according to the weight of each parameter.

[0081] Step S35. Bring each of the landslide candidate blocks into the risk assessment model in turn to obtain a second disaster area corresponding to each of the landslide candidate blocks respectively. The second disaster area is a block marked with a disaster risk level;

[0082] Step S36. Superimpose the first disaster area and the second disaster area on the first remote sensing image to obtain the disaster zoning map.

[0083] In a specific embodiment of the present disclosure, in step S33, steps S331, S332, S333, S334, S335, and S336 may further be included.

[0084] Step S331. Set the normalized difference vegetation index (NDVI) and set multiple false positive thresholds. The normalized difference vegetation index is a characterization of vegetation changes caused by landslides, and the false positive thresholds are used to distinguish various building characterization thresholds that are misidentified as landslides.

[0085] After a landslide occurs, rocks and debris will be exposed and vegetation will be damaged, which is one of the most direct features for judging landslides in remote sensing images. The normalized difference vegetation index (NDVI) is an obvious characterization of vegetation changes caused by landslides. If the vegetation normalization index value of a certain area is less than a specific value, then this area can be regarded as a landslide. Selecting an appropriate NDVI value is the first and important step in the extraction process of landslides. It can exclude areas such as forest land and cultivated land that do not need to be considered. In this regard, the K-means clustering algorithm is adopted in this embodiment, and the threshold value of NDVI is obtained as 0.032, and the most suitable threshold value of slope is 28 degrees. The above two threshold values can screen out places with small vegetation coverage areas as candidate landslide areas.

[0086] There are inevitably some problems with NDVI as the threshold value of the landslide area. In the study area, exposed rocks, rivers, roads, building areas, etc. are all areas without vegetation and will be misidentified as landslides. To eliminate these areas misjudged as landslides, the main method is to use object attribute features. Rely on the background information, morphological features, etc. of object attributes to identify false positive areas where landslides exist. After determining the characteristics of the above areas, further determine various threshold values for distinguishing false positives of landslides. According to the research results of predecessors and expert experience, this application uses brightness, texture, slope, and domain relationship, etc. to determine each threshold value of the landslide false positive area, as shown in Table 1.

[0087]

[0088]

[0089] Table 1 Threshold values of landslide false positive areas.

[0090] Step S332. Retrieve one of the vegetation blocks, denoted as the first vegetation block.

[0091] Step S333. First detection and determination operation: Detect whether the index of the first vegetation block is less than the normalized difference vegetation index. If the index of the first vegetation block is less than the normalized difference vegetation index, determine that the block is a block with landslides, and divide multiple first landslide areas. The first landslide area is the area where the local area index in the first vegetation block is less than the normalized difference vegetation index;

[0092] Step S334. Second detection and determination operation: According to the multiple false positive thresholds, respectively determine whether each of the first landslide areas is a false positive area, mark the first landslide areas determined to be false positives as candidate safe areas, record the remaining first landslide areas not determined to be false positive areas as candidate landslide areas, and record the vegetation blocks with at least one of the candidate landslide areas as candidate landslide blocks;

[0093] Step S335. Retrieve another vegetation block, and sequentially perform the first detection and determination operation and the second detection and determination operation until the first detection and determination operation and the second detection and determination operation are performed on all the vegetation blocks;

[0094] Step S336. Output multiple candidate landslide blocks.

[0095] In a specific embodiment of the present disclosure, in step S34, steps S341, S342, S343, S344, S345, and S346 may further be included.

[0096] Step S341. Retrieve one of the landslide blocks, denoted as the first landslide block;

[0097] Step S342. Geological data retrieval operation: According to the position information corresponding to the first landslide block, obtain the geological data corresponding to the first landslide block in the geological database. The geological data is a plurality of geological parameters reflecting the location of the first landslide block;

[0098] In this embodiment, the geological parameters may be topography, geological structure, and stratigraphic lithology;

[0099] Step S343. Field survey data retrieval operation: According to the position information corresponding to the first landslide block, obtain the field survey data corresponding to the first landslide block in the field survey database. The field survey data includes the distance to the river, vegetation coverage rate, and human activity rate;

[0100] Step S344. Historical data retrieval operation: According to the position information corresponding to the first landslide block, obtain the first historical landslide data within the first landslide block;

[0101] In this embodiment, the first historical landslide data may include landslide density, landslide scale, and annual rainfall over the years.

[0102] Step S345. Risk assessment operation; input the geological data, on-site survey data, and the first historical landslide data corresponding to the first landslide block into the risk assessment model, and calculate the first disaster area corresponding to the first landslide block, where the first disaster area is a block marked with a disaster risk level.

[0103] Step S346. Retrieve another landslide block and sequentially perform the operations of retrieving geological data, retrieving on-site survey data, retrieving historical data, and risk assessment operation until multiple first disaster areas are obtained after all the multiple landslide blocks are input into the risk assessment model.

[0104] Embodiment 2

[0105] As Figure 2 shown, this embodiment provides a geological disaster identification device for a transmission line, which includes a first acquisition module 71, a first calculation module 72, and a second calculation module 73.

[0106] The first acquisition module 71 is used to acquire the position information of the transmission line, where the position information of the transmission line is the geographical location information where multiple power towers are erected.

[0107] The first calculation module 72 is used to create a corresponding first remote sensing image according to the position information of the transmission line, where the first remote sensing image is the original satellite map along the transmission line.

[0108] The second calculation module 73 is used to calculate a disaster zoning map according to the first remote sensing image, where the disaster zoning map is a map marked with multiple areas of different disaster levels.

[0109] In a specific embodiment of the present disclosure, the second calculation module 73 includes a first retrieval unit 731, a first calculation unit 732, a second calculation unit 733, a third calculation unit 734, a fourth calculation unit 735, and a fifth calculation unit 736.

[0110] The first retrieval unit 731 is used to retrieve the first remote sensing image and set initial segmentation parameters and multiple optical band weight parameters.

[0111] The first calculation unit 732 is used to construct a segmentation model and input the first remote sensing image, the initial segmentation parameters, and the multiple optical band weight parameters into the segmentation model to calculate a first segmentation map. The segmentation model is used to adjust the segmentation parameters to accurately distinguish vegetation and landslides on the first remote sensing image. The first segmentation map includes multiple vegetation blocks and multiple landslide blocks.

[0112] A second calculation unit 733, configured to construct a determination model, and respectively input each of the vegetation blocks into the determination model to obtain a plurality of landslide candidate blocks, where the determination model is used to screen out the landslide candidate blocks in the vegetation blocks;

[0113] A third calculation unit 734, configured to construct a geological disaster risk assessment model, and sequentially input each of the landslide blocks into the risk assessment model to respectively obtain a first disaster area corresponding to each of the landslide blocks, where the first disaster area is a block marked with a disaster risk level;

[0114] A fourth calculation unit 735, configured to sequentially input each of the landslide candidate blocks into the risk assessment model to respectively obtain a second disaster area corresponding to each of the landslide candidate blocks, where the second disaster area is a block marked with a disaster risk level;

[0115] A fifth calculation unit 736, configured to superimpose the first disaster area and the second disaster area on the first remote sensing image to obtain the disaster zoning map.

[0116] In a specific implementation manner of the present disclosure, the second calculation unit 733 includes a first calculation subunit 7331, a first retrieval subunit 7332, a second calculation subunit 7333, a third calculation subunit 7334, a fourth calculation subunit 7335, and a first output subunit 7336.

[0117] The first calculation subunit 7331 is configured to set a normalized difference vegetation index and set a plurality of false positive thresholds. The normalized difference vegetation index is a representation of vegetation changes caused by landslides, and the false positive thresholds are used to distinguish various building representation thresholds that are misidentified as landslides;

[0118] The first retrieval subunit 7332 is configured to retrieve one of the vegetation blocks, denoted as the first vegetation block;

[0119] The second calculation subunit 7333 is configured to perform a first detection and determination operation: detect whether the index of the first vegetation block is less than the normalized difference vegetation index. If the index of the first vegetation block is less than the normalized difference vegetation index, it is determined that the block has a landslide, and a plurality of first landslide areas are divided. The first landslide areas are areas in the first vegetation block where the local area index is less than the normalized difference vegetation index;

[0120] The third computing subunit 7334 is configured to perform a second detection and determination operation: based on the multiple false positive thresholds, determine whether each of the first landslide regions is a false positive region, mark the first landslide regions determined to be false positives as candidate safe regions, record the remaining first landslide regions that are not determined to be false positive regions as candidate landslide regions, and record the vegetation blocks with at least one of the candidate landslide regions as candidate landslide blocks;

[0121] The fourth computing subunit 7335 is configured to retrieve another vegetation block and sequentially perform the first detection and determination operation and the second detection and determination operation until the first detection and determination operation and the second detection and determination operation are performed on all the vegetation blocks;

[0122] The first output subunit 7336 is configured to output multiple candidate landslide blocks.

[0123] In a specific implementation manner of the present disclosure, the third computing unit 734 includes a second retrieval subunit 7341, a fifth computing subunit 7342, a sixth computing subunit 7343, a seventh computing subunit 7344, an eighth computing subunit 7345, and a ninth computing subunit 7346.

[0124] The second retrieval subunit 7341 is configured to retrieve one of the landslide blocks, denoted as the first landslide block;

[0125] The fifth computing subunit 7342 is configured to perform an operation of retrieving geological data: according to the location information corresponding to the first landslide block, obtain geological data corresponding to the first landslide block in the geological database, where the geological data is a plurality of geological parameters reflecting the location where the first landslide block is located;

[0126] The sixth computing subunit 7343 is configured to perform an operation of retrieving on-site survey data: according to the location information corresponding to the first landslide block, obtain on-site survey data corresponding to the first landslide block in the on-site survey database, where the on-site survey data includes the distance to the river, the vegetation coverage rate, and the human activity rate;

[0127] The seventh computing subunit 7344 is configured to perform an operation of retrieving historical data: according to the location information corresponding to the first landslide block, obtain the first historical landslide data within the first landslide block;

[0128] The eighth computing subunit 7345 is configured to perform a risk assessment operation; input the geological data, on-site survey data, and first historical landslide data corresponding to the first landslide block into the risk assessment model, and calculate to obtain a first disaster region corresponding to the first landslide block, where the first disaster region is a block marked with a disaster risk level;

[0129] The ninth computing subunit 7346 is configured to retrieve another one of the landslide blocks and sequentially perform the operations of retrieving geological data, retrieving on-site survey data, retrieving historical data, and risk assessment until all the multiple landslide blocks are brought into the risk assessment model, and then multiple first disaster areas are obtained.

[0130] It should be noted that for the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0131] Embodiment 3

[0132] Corresponding to the above method embodiments, the present disclosure embodiments further provide a geological disaster identification device for a transmission line. A geological disaster identification device for a transmission line described below can be correspondingly referred to the geological disaster identification method for a transmission line described above.

[0133] Figure 3 is a block diagram of a geological disaster identification device 800 for a transmission line shown according to an exemplary embodiment. As Figure 3 shown, the electronic device 800 may include: a processor 801, a memory 802. The electronic device 800 may further include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805.

[0134] Among them, the processor 801 is used to control the overall operation of the electronic device 800 to complete all or part of the steps in the above-mentioned geological disaster identification method for transmission lines. The memory 402 is used to store various types of data to support the operation of the electronic device 800. These data may include, for example, instructions for any application or method operating on the electronic device 800, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc. The multimedia component 803 may include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal can be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules. The above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them. Accordingly, the communication component 805 may include: a Wi-Fi module, a Bluetooth module, an NFC module.

[0135] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and is used to execute the above-mentioned geological disaster identification method for transmission lines.

[0136] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned geological disaster identification method for transmission lines are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above program instructions can be executed by the processor 801 of the electronic device 800 to complete the above-mentioned geological disaster identification method for transmission lines.

[0137] Embodiment 4

[0138] Corresponding to the above method embodiment, the present disclosure embodiment also provides a readable storage medium. A readable storage medium described below can be correspondingly referred to with a geological disaster identification method for a transmission line described above.

[0139] A readable storage medium has a computer program stored thereon. When the computer program is executed by a processor, the steps of the geological disaster identification method for transmission lines in the above method embodiment are implemented.

[0140] The readable storage medium can specifically be various readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disc that can store program codes.

[0141] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying geological disasters of a transmission line, characterized in that, Including: Obtaining transmission line location information, where the transmission line location information is the geographical location information of multiple power towers; Making a corresponding first remote sensing image according to the location information of the transmission line, where the first remote sensing image is the original satellite map along the transmission line; Calculating a disaster zoning map according to the first remote sensing image, where the disaster zoning map is a map marked with multiple areas of different disaster levels; The calculating the disaster zoning map according to the first remote sensing image includes: Retrieving the first remote sensing image and setting initial segmentation parameters and multiple optical band weight parameters; Constructing a segmentation model and inputting the first remote sensing image, the initial segmentation parameters and the multiple optical band weight parameters into the segmentation model to calculate a first segmentation map. The segmentation model is used to adjust the segmentation parameters to accurately distinguish vegetation and landslides on the first remote sensing image. The first segmentation map includes multiple vegetation blocks and multiple landslide blocks; Constructing a determination model and bringing each of the vegetation blocks into the determination model to obtain multiple landslide candidate blocks. The determination model is used to screen out landslide candidate blocks in the vegetation blocks; Constructing a geological disaster risk assessment model and sequentially bringing each of the landslide blocks into the risk assessment model to respectively obtain a first disaster area corresponding to each of the landslide blocks. The first disaster area is a block marked with a disaster risk level; Sequentially bringing each of the landslide candidate blocks into the risk assessment model to respectively obtain a second disaster area corresponding to each of the landslide candidate blocks. The second disaster area is a block marked with a disaster risk level; Overlaying the first disaster area and the second disaster area on the first remote sensing image to obtain the disaster zoning map.

2. The geological disaster identification method for a transmission line according to claim 1, characterized in that, The constructing the determination model and bringing each of the vegetation blocks into the determination model to obtain multiple landslide candidate blocks includes: Setting the normalized difference vegetation index and multiple false positive thresholds. The normalized difference vegetation index is a characterization of vegetation change caused by landslides, and the false positive thresholds are used to distinguish various building characterization thresholds that are misidentified as landslides; Retrieving one of the vegetation blocks, denoted as the first vegetation block; First detection and determination operation: Detecting whether the index of the first vegetation block is less than the normalized difference vegetation index. If the index of the first vegetation block is less than the normalized difference vegetation index, then determining that this block is a block with a landslide, and dividing multiple first landslide areas. The first landslide area is the area where the local area index in the first vegetation block is less than the normalized difference vegetation index; Second detection and determination operation: According to the multiple false positive thresholds, respectively determining whether each of the first landslide areas is a false positive area, marking the first landslide areas determined to be false positive as safe area candidate blocks, recording the remaining first landslide areas not determined to be false positive areas as landslide candidate areas, and recording the vegetation blocks with at least one of the landslide candidate areas as landslide candidate blocks; Retrieve another one of the vegetation blocks, and sequentially perform the first detection and determination operation and the second detection and determination operation until the first detection and determination operation and the second detection and determination operation are performed on all the vegetation blocks; Output multiple landslide candidate blocks.

3. The geological disaster identification method for a transmission line according to claim 2, characterized in that, The constructing a geological disaster risk assessment model and sequentially bringing each of the landslide blocks into the risk assessment model to respectively obtain a first disaster area corresponding to each of the landslide blocks includes: Retrieve one of the landslide blocks, denoted as the first landslide block; Retrieve geological data operation: According to the position information corresponding to the first landslide block, obtain geological data corresponding to the first landslide block in the geological database, where the geological data is a plurality of geological parameters reflecting the location of the first landslide block; Retrieve on-site survey data operation: According to the position information corresponding to the first landslide block, obtain on-site survey data corresponding to the first landslide block in the on-site survey database, where the on-site survey data includes the distance to the river, vegetation coverage rate, and human activity rate; Retrieve historical data operation: According to the position information corresponding to the first landslide block, obtain the first historical landslide data within the first landslide block; Risk assessment operation; Bring the geological data, on-site survey data, and first historical landslide data corresponding to the first landslide block into the risk assessment model, and calculate to obtain a first disaster area corresponding to the first landslide block, where the first disaster area is a block marked with a disaster risk level; Retrieve another one of the landslide blocks, and sequentially perform the retrieve geological data operation, retrieve on-site survey data operation, retrieve historical data operation, and risk assessment operation until after all the landslide blocks are brought into the risk assessment model, multiple first disaster areas are obtained.

4. A geological disaster identification device for a transmission line, characterized in that, Including: A first acquisition module for acquiring transmission line position information, where the transmission line position information is the geographical location information where a plurality of power towers are erected; A first calculation module for making a corresponding first remote sensing image according to the position information of the transmission line, where the first remote sensing image is the original satellite map along the transmission line; A second calculation module for calculating a disaster zoning map according to the first remote sensing image, where the disaster zoning map is a map marked with multiple different disaster level areas; The second calculation module includes: A first retrieval unit for retrieving the first remote sensing image and setting initial segmentation parameters and a plurality of optical band weight parameters; A first calculation unit for constructing a segmentation model and inputting the first remote sensing image, the initial segmentation parameters, and the plurality of optical band weight parameters into the segmentation model to calculate a first segmentation map, where the segmentation model is used to adjust the segmentation parameters to accurately distinguish the vegetation and landslides on the first remote sensing image, and the first segmentation map includes a plurality of vegetation blocks and a plurality of landslide blocks; A second calculation unit for constructing a determination model and bringing each of the vegetation blocks into the determination model to obtain a plurality of landslide candidate blocks, where the determination model is used to screen out the landslide candidate blocks in the vegetation blocks; A third computing unit for constructing a geological disaster risk assessment model and sequentially inputting each of the landslide blocks into the risk assessment model to respectively obtain a first disaster area corresponding to each of the landslide blocks, where the first disaster area is a block marked with a disaster risk level; A fourth computing unit for sequentially inputting each of the landslide candidate blocks into the risk assessment model to respectively obtain a second disaster area corresponding to each of the landslide candidate blocks, where the second disaster area is a block marked with a disaster risk level; A fifth computing unit for superimposing the first disaster area and the second disaster area on the first remote sensing image to obtain the disaster zoning map.

5. The geological disaster identification device for a transmission line according to claim 4, characterized in that, The second computing unit includes: A first computing subunit for setting a normalized difference vegetation index and setting a plurality of false positive thresholds, where the normalized difference vegetation index is a representation of vegetation changes caused by landslides, and the false positive thresholds are used to distinguish various building representation thresholds that are misidentified as landslides; A first retrieval subunit for retrieving one of the vegetation blocks, denoted as the first vegetation block; A second computing subunit for performing a first detection and determination operation: detecting whether the index of the first vegetation block is less than the normalized difference vegetation index. If the index of the first vegetation block is less than the normalized difference vegetation index, it is determined that the block has a landslide, and a plurality of first landslide areas are divided, where the first landslide areas are areas in the first vegetation block where the local area index is less than the normalized difference vegetation index; A third computing subunit for performing a second detection and determination operation: according to the plurality of false positive thresholds, respectively determining whether each of the first landslide areas is a false positive area, marking the first landslide areas determined to be false positives as safe area candidate blocks, recording the remaining first landslide areas not determined to be false positives as landslide candidate areas, and recording the vegetation blocks with at least one of the landslide candidate areas as landslide candidate blocks; A fourth computing subunit for retrieving another vegetation block and sequentially performing the first detection and determination operation and the second detection and determination operation until the first detection and determination operation and the second detection and determination operation are performed on all the vegetation blocks; A first output subunit for outputting a plurality of landslide candidate blocks.

6. The geological disaster identification device for a transmission line according to claim 5, characterized in that, The third computing unit includes: A second retrieval subunit for retrieving one of the landslide blocks, denoted as the first landslide block; A fifth computing subunit for performing an operation of retrieving geological data: according to the location information corresponding to the first landslide block, obtaining geological data corresponding to the first landslide block in the geological database, where the geological data are multiple geological parameters reflecting the location where the first landslide block is located; A sixth computing subunit for performing an operation of retrieving on-site survey data: according to the location information corresponding to the first landslide block, obtaining on-site survey data corresponding to the first landslide block in the on-site survey database, where the on-site survey data include the distance to the river, the vegetation coverage rate, and the human activity rate; The seventh computing subunit is configured to perform an operation of retrieving historical data: according to the position information corresponding to the first landslide block, obtain the first historical landslide data within the first landslide block; The eighth computing subunit is configured to perform a risk assessment operation; input the geological data, on-site survey data, and the first historical landslide data corresponding to the first landslide block into the risk assessment model, and calculate to obtain the first disaster area corresponding to the first landslide block, where the first disaster area is a block marked with a disaster risk level; The ninth computing subunit is configured to retrieve another landslide block and sequentially perform the operations of retrieving geological data, retrieving on-site survey data, retrieving historical data, and risk assessment operations until after all the multiple landslide blocks are input into the risk assessment model, multiple first disaster areas are obtained.

7. A geological disaster identification device for a transmission line, characterized in that, Comprising: A memory for storing a computer program; A processor for implementing the steps of the geological disaster identification method for a transmission line according to any one of claims 1 to 3 when executing the computer program.

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