Method and device for monitoring environmental and water conservation of power transmission and transformation lines based on high-resolution remote sensing

Abstract

The present application discloses a method and device for monitoring environmental water conservation of power transmission and transformation lines based on high-resolution remote sensing, which relates to the field of image processing technology; high-resolution remote sensing image data of a target area in a sampling period is obtained along the time axis to obtain first image data of the sampling period; the first image data includes high-resolution remote sensing images obtained from multiple perspectives; based on the first image data, a digital elevation model (DEM) of the target area in the current sampling period is constructed; based on the difference in terrain data between the first model and the benchmark model, it is determined whether there is a surface erosion defect in the target area; when there is a surface erosion defect in the target area, the surface erosion characteristics of the surface erosion position are extracted based on the first model; based on the surface erosion characteristics, an environmental water conservation management strategy corresponding to the surface erosion position is configured. The technical solution provided by the present application can improve the accuracy and reliability of the monitoring results of environmental water conservation of power transmission and transformation lines.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and specifically to a method and device for monitoring environmental and water conservation of power transmission and transformation lines based on high-resolution remote sensing. Background Art

[0002] The construction of power transmission and transformation lines often needs to cross complex terrain, including ecological areas such as forests, wetlands, and rivers. Affected by climate and human interference, these areas may have environmental problems such as surface erosion and low vegetation coverage. These environmental problems will not only affect the ecological balance, but also pose safety hazards to power transmission and transformation line equipment. Therefore, in order to ensure the smooth operation of power transmission and transformation line equipment, it is necessary to conduct regular environmental and water conservation monitoring of the power transmission and transformation lines.

[0003] High-resolution remote sensing technology is to obtain high-resolution images through satellites or drones, such as Figure 1 As shown by the white lines from left to right in the figure, high-resolution images can present the topographical features of the transmission line structure and its surroundings, thereby identifying vegetation coverage, water distribution and boundaries, soil erosion and other information based on these features. However, traditional remote sensing monitoring technology only relies on remote sensing images with a single perspective to analyze the environmental and water conservation of transmission lines. However, remote sensing images with a single perspective cannot fully reflect the impact between different terrain areas, especially for terrain areas with three-dimensional structures such as hills and basins. Therefore, it is impossible to fully and accurately reflect the actual situation of the environmental and water conservation monitoring area, resulting in low accuracy and reliability of the monitoring structure.

[0004] Therefore, how to improve the accuracy and reliability of environmental and water conservation monitoring results of transmission and transformation lines is an urgent problem to be solved. Summary of the invention

[0005] The purpose of this application is to provide a method and device for monitoring environmental and water conservation of power transmission and transformation lines based on high-resolution remote sensing, so as to solve the problems raised in the above-mentioned background technology.

[0006] The technical solution of the embodiment of the present application is implemented as follows:

[0007] The present application provides a method for monitoring environmental water conservation of power transmission and transformation lines based on high-resolution remote sensing, the method comprising:

[0008] Acquire a high-resolution remote sensing image of a target area in at least one sampling period along a time axis to obtain first image data of each sampling period; the target area is determined according to the position of the power transmission and transformation line; the first image data includes high-resolution remote sensing images acquired from multiple viewing angles;

[0009] Based on the first image data, construct a digital elevation model (DEM) of the target area in the current sampling period;

[0010] Based on the difference in terrain data between the first model and the benchmark model, determining whether there are surface erosion defects in the target area; the first model is the DEM of the current sampling period, and the benchmark model is the DEM of the first sampling period;

[0011] When the judgment result indicates that there is a surface erosion defect in the target area, extracting the surface erosion characteristics of the surface erosion location based on the first model;

[0012] Based on the surface erosion characteristics, an environmental and water conservation management strategy corresponding to the surface erosion location is configured.

[0013] In the above solution, judging whether there is a surface erosion defect in the target area based on the difference in terrain data between the first model and the reference model includes:

[0014] Based on the difference in terrain data of the target location in the first model and the reference model, determining whether each target location has a surface erosion defect; the target location is a location in the target area where there is a risk of surface erosion; the target location includes at least one pre-configured first location;

[0015] In the case where the judgment result indicates that at least one target location has a surface erosion defect, determining that the target area has a surface erosion defect;

[0016] When the judgment result indicates that there are no surface erosion defects in all target areas, it is determined that there are no surface erosion defects in the target areas.

[0017] In the above scheme, the method further includes:

[0018] Based on the benchmark model, performing terrain analysis on the target area to obtain actual terrain features;

[0019] At least one target location is configured in the target area based on the actual terrain features.

[0020] In the above solution, the terrain analysis of the target area is performed based on the benchmark model to obtain actual terrain features, including:

[0021] Based on the reference model, determining the elevation change rate of adjacent pixel units to obtain elevation change information;

[0022] Based on the elevation change information, determine the value of each terrain indicator of a plurality of preset terrain indicators of the target area to obtain terrain indicator information;

[0023] Based on the terrain indicator information, actual terrain features of the target area are determined.

[0024] In the above solution, configuring at least one target position in the target area based on the actual terrain features includes:

[0025] Based on the actual terrain features, clustering multiple pixel units in the target area to obtain a clustering result;

[0026] Based on the clustering result, the target area is topographically divided to obtain a plurality of sub-areas;

[0027] At least one target location is determined from the plurality of sub-regions based on the clustering feature of each sub-region.

[0028] In the above solution, the target position also includes at least one second position; the second position satisfies one of the following conditions:

[0029] Surface erosion occurred in the first time period before the current moment;

[0030] The presence of geographical features that match the pre-set erosion risk indicators;

[0031] There are climate features that match the preset erosion risk indicators.

[0032] In the above scheme, the method further includes:

[0033] Based on the image acquisition range of the remote sensing satellite, the area along the power transmission and transformation line to be tested is divided into at least one monitoring area;

[0034] Based on the geomorphic features and soil features of each monitoring area, the at least one monitoring area is classified to obtain at least one key area and at least one non-key area, and the at least one key area is used as a target area.

[0035] In the above scheme, the method further includes:

[0036] Periodically obtain the climate data of each non-critical area to obtain the climate characteristics of each non-critical area;

[0037] Based on the climate characteristics, determine whether extreme weather exists in each non-critical area during a preset sampling period; the preset sampling period includes a current sampling period and a next sampling period;

[0038] When extreme climate exists during the preset sampling period, the corresponding non-critical areas will be marked as critical areas.

[0039] In the above scheme, the configuration of the environmental and water conservation management strategy corresponding to the surface erosion location based on the surface erosion characteristics includes:

[0040] Based on the surface erosion characteristics, determining a surface erosion type corresponding to a surface erosion position from at least one preset surface erosion type; the surface erosion type includes gully erosion, sheet erosion, watershed erosion, riverbank erosion or slope erosion;

[0041] Based on the surface erosion type, configure corresponding environmental and water conservation management strategies.

[0042] The embodiment of the present application also provides a transmission and transformation line environmental water conservation monitoring device based on high-resolution remote sensing, the device comprising:

[0043] A data acquisition unit, used to acquire a high-resolution remote sensing image of a target area in at least one sampling period along a time axis to obtain first image data of each sampling period; the target area is determined according to the position of the power transmission and transformation line; the first image data includes high-resolution remote sensing images acquired from multiple viewing angles;

[0044] A model building unit, used for building a DEM of the target area in a current sampling period based on the first image data;

[0045] A calculation unit, used to determine whether there are surface erosion defects in the target area based on the difference in terrain data between a first model and a reference model; the first model is the DEM of the current sampling period, and the reference model is the DEM of the first sampling period;

[0046] The processing unit is used to extract the surface erosion characteristics of the surface erosion location based on the first model when the judgment result indicates that there are surface erosion defects in the target area; and configure the environmental and water conservation management strategy corresponding to the surface erosion location based on the surface erosion characteristics.

[0047] The high-resolution remote sensing-based environmental and water conservation monitoring method and device for power transmission and transformation lines provided in the embodiments of the present application obtain multi-perspective high-resolution remote sensing images of the same area, construct a DEM model of the area, and perform time series analysis on the DEM model, so as to judge whether surface erosion occurs in the area according to the difference in terrain data in the DEM model. Compared with the traditional remote sensing monitoring technology, the comprehensiveness of terrain data is improved due to the use of multi-perspective remote sensing images for analysis, thereby improving the accuracy and reliability of the analysis results, that is, improving the accuracy and reliability of the environmental and water conservation monitoring results. Furthermore, since the change of terrain data is determined according to the results of time series analysis to judge whether surface erosion occurs, it is possible to timely detect areas with unclear erosion characteristics. Compared with the use of a single image to judge the erosion results, the timeliness of detection is improved, so that prevention and repair can be carried out in advance to ensure the safety of the environment around the power transmission and transformation lines, thereby ensuring the safe and stable operation of the power transmission and transformation lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of a remote sensing image of a power transmission line in the prior art;

[0049] Figure 2 A schematic flow chart of a method for monitoring environmental water conservation of power transmission and transformation lines based on high-resolution remote sensing provided in an embodiment of the present application;

[0050] Figure 3 This is a schematic diagram of the configuration process of the target area in the environmental and water conservation monitoring method of the power transmission and transformation line based on high-resolution remote sensing in the embodiment of the present application;

[0051] Figure 4 A schematic diagram of a process for monitoring environmental water conservation in a non-critical area of ​​a power transmission and transformation line in a method for monitoring environmental water conservation in a power transmission and transformation line based on high-resolution remote sensing in an embodiment of the present application;

[0052] Figure 5 This is a schematic diagram of another process flow of environmental water conservation monitoring in non-critical areas of a power transmission and transformation line in the method for environmental water conservation monitoring of a power transmission and transformation line based on high-resolution remote sensing in an embodiment of the present application;

[0053] Figure 6 A schematic diagram of the DEM process for constructing a water and environment protection monitoring method for power transmission and transformation lines based on high-resolution remote sensing in an embodiment of the present application;

[0054] Figure 7 This is a schematic diagram of a process for determining whether there are surface erosion defects in a target area in a method for monitoring environmental water conservation of power transmission and transformation lines based on high-resolution remote sensing in an embodiment of the present application;

[0055] Figure 8 This is a schematic diagram of the structure of a power transmission and transformation line environmental and water conservation monitoring device based on high-resolution remote sensing according to an embodiment of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] The present application embodiment provides a method for monitoring environmental water conservation of power transmission and transformation lines based on high-resolution remote sensing, such as Figure 2 As shown, the method may include:

[0058] S201: Acquire a high-resolution remote sensing image of a target area in at least one sampling period along a time axis to obtain first image data of each sampling period; the target area is determined according to the position of the power transmission and transformation line; the first image data includes high-resolution remote sensing images acquired from multiple viewing angles;

[0059] S202: constructing a DEM of the target area in a current sampling period based on the first image data;

[0060] S203: judging whether there is a surface erosion defect in the target area based on the difference in terrain data between the first model and the benchmark model; the first model is the DEM of the current sampling period, and the benchmark model is the DEM of the first sampling period;

[0061] S204: if the judgment result indicates that there is a surface erosion defect in the target area, extracting surface erosion characteristics of the surface erosion position based on the first model;

[0062] S205: Based on the surface erosion characteristics, configure an environmental and water conservation management strategy corresponding to the surface erosion location.

[0063] In actual application, before executing S201, the area along the power transmission and transformation line may be divided into multiple sub-areas in advance, and modeling and analysis may be performed separately for each sub-area.

[0064] Based on this, in one embodiment, the method may further include a configuration process of the target area; Figure 3 As shown in the figure, the configuration process of the target area includes:

[0065] S301: Divide the area along the power transmission and transformation line to be measured into at least one monitoring area based on the image acquisition range of the remote sensing satellite;

[0066] S302: Based on the geomorphic features and soil features of each monitoring area, classify the at least one monitoring area to obtain at least one key area and at least one non-key area, and use the at least one key area as a target area.

[0067] It should be noted that S301 and S302 may be executed before S201.

[0068] In actual application, each monitoring area includes a section of the power transmission and transformation line, and the power transmission and transformation line sections in all monitoring areas are spliced ​​into a complete power transmission and transformation line.

[0069] Here, in S301, the image acquisition range based on the remote sensing satellite can be understood as the image acquisition range of the remote sensing satellite in the high-resolution imaging mode.

[0070] In actual application, in S302, the landform feature may include at least one of the following:

[0071] There are steep slopes;

[0072] Located near water;

[0073] It belongs to the area with sparse vegetation cover;

[0074] It is a geologically unstable area.

[0075] In actual application, areas with large slopes are prone to landslides or soil erosion, threatening the safety of power transmission and transformation lines; it can be determined whether the slope of the area exceeds the preset slope threshold, and if it exceeds, it is determined that there is a steep slope in the area.

[0076] In actual applications, areas close to rivers or lakes may have unstable foundations due to floods or water erosion, thus threatening power transmission and transformation line equipment. It can be determined whether the distance from the area to the nearest water area (such as a river or lake) is lower than a preset distance threshold, and if it is lower than the preset distance threshold, it is determined that the area is near the water area.

[0077] In actual application, areas with less vegetation coverage are more susceptible to wind erosion and water erosion; here, it can be determined whether the vegetation coverage rate of the area is lower than the preset coverage rate threshold, and if it is lower than the preset coverage rate threshold, the area is determined to be an area with sparse vegetation coverage.

[0078] In practical applications, geologically unstable areas may be areas with complex geological structures or near fault zones, which are more prone to surface changes. Here, geological information of the area can be obtained from address databases, academic research institutions and other institutions, and then it can be determined whether the area is an unstable address area based on the geological information.

[0079] In actual application, in S302, the soil characteristics may include at least one of the following:

[0080] The soil texture is loose;

[0081] Poor soil structure;

[0082] The soil moisture content is high.

[0083] In actual application, the soil texture is loose and can be judged by the soil texture type. For example, when the soil texture is sandy soil or peat soil, the soil texture in this area is loose.

[0084] In actual application, the soil structure is poor, which can be judged based on the soil structure density. Specifically, it can be judged whether the soil structure density is lower than a threshold. If it is lower than the threshold, it is determined that the soil structure in this area is dense. Otherwise, it is determined that the soil structure in this area is loose. When the soil structure is transported, the soil structure in this area is poor.

[0085] In actual application, if the soil moisture content is higher than the preset moisture content threshold, it is determined that the soil moisture content in the area is high, otherwise, the soil moisture content is low.

[0086] In practical application, based on the geomorphic features and soil features of each monitoring area, the at least one monitoring area is classified to obtain at least one key area and at least one non-key area, and one of the following methods can be used:

[0087] When the monitoring area has any of the above-mentioned geomorphic features or any of the above-mentioned soil features, the monitoring area is determined to be a key area, otherwise, it is a non-key area;

[0088] When the monitoring area has any of the above-mentioned geomorphic features and any of the above-mentioned soil features, the monitoring area is determined to be a key area, otherwise, it is a non-key area.

[0089] Of course, multiple features of the above-mentioned geomorphic features and the above-mentioned soil features can also be selected as judgment conditions according to the application scenario. When the monitored area has features that match the judgment conditions, the area is a key area, otherwise, it is a non-key area. How to set the judgment conditions can be determined according to the actual application scenario, and the embodiments of the present application are not limited to this.

[0090] It should be pointed out that the thresholds for determining the above-mentioned geomorphic features and soil features can be determined according to the standards set by the data provider, or can be determined by technical personnel based on historical experience, and the embodiments of the present application do not limit this.

[0091] After determining the target area, images can be collected and modeled for each target area separately, thereby achieving targeted monitoring of each area.

[0092] In actual application, the time axis can be understood as a time series; the time series can be set according to a preset sampling period, and each time point in the time series is a sampling time point.

[0093] In actual application, when obtaining high-resolution remote sensing images of the target area in at least one sampling period along the time axis, multiple sampling time points can be pre-set according to the time axis, and the period between two adjacent sampling time points is a sampling period; here, the sampling period can be set to the same time, that is, the duration of each sampling period is the same; different sampling periods can also be set according to actual monitoring needs; for example, the sampling period of the rainy season is set to T1, and the sampling period of the non-rainy season is set to T2, T1<T2, that is, the sampling frequency of the rainy season is higher than that of the non-rainy season.

[0094] Of course, the sampling cycle can also be set manually; for example, before entering each sampling cycle, an alarm can be issued to remind people to set the parameters of the next sampling cycle manually; for example, sampling can be performed according to a pre-configured sampling cycle, and when the difference between the climate in the current sampling cycle and the historical climate is greater than a preset threshold, the climate in the current sampling cycle is judged to be abnormal. At this time, an alarm can be issued and the sampling cycle parameters can be updated manually.

[0095] In actual applications, high-resolution remote sensing images can provide precise data, but this comes with a huge amount of computing, which will put a lot of pressure on the processor of the monitoring system and affect the stable and reliable operation of the processor; therefore, a single-perspective image comparison can be performed on non-critical areas to identify surface changes in non-critical areas.

[0096] Based on this, in one embodiment, the method may also include a monitoring process of environmental and water conservation in non-critical areas of the power transmission and transformation line, such as Figure 4 As shown, the process includes:

[0097] S401: Acquire remote sensing image data of a non-critical area in at least one sampling period along a time axis to obtain a second image of each sampling period;

[0098] S402: Determine whether there is a surface erosion defect in the non-critical area based on a difference between the second image and a reference image; the reference image is a remote sensing image acquired in the first sampling period;

[0099] S403: When the non-critical area has surface erosion defects, the non-critical area is marked as a target area.

[0100] In actual application, high-resolution remote sensing images of key areas (that is, target areas) are collected, and low-resolution remote sensing images of non-key areas are collected. This can reduce the overall data processing volume while ensuring effective and accurate monitoring of high-risk areas for surface erosion, thereby reducing the processing pressure of the data processor and improving the reliability of the entire monitoring system. Here, the spatial resolution of high-resolution remote sensing images can be 1m to 3m, and the spatial resolution of low-resolution remote sensing images can be 10m to 100m.

[0101] Here, a balance between monitoring accuracy and data processing volume can also be achieved by reducing the sampling frequency in non-critical areas.

[0102] Based on this, Figure 5 As shown, in one embodiment, the environmental and water conservation monitoring process of non-critical areas of power transmission and transformation lines may include:

[0103] S501: Acquire a high-resolution remote sensing image of a non-critical area in at least one sampling period along a time axis to obtain a third image of each sampling period; the sampling period of the non-critical area is greater than the sampling period of the critical area;

[0104] S502: Determine whether there is a surface erosion defect in the non-critical area based on a difference between the third image and a reference image; the reference image is a remote sensing image acquired in the first sampling period;

[0105] S503: When there are surface erosion defects in the non-critical area, mark the non-critical area as a target area.

[0106] Here, the amount of image data processing is reduced by lowering the sampling frequency in non-critical areas. At the same time, by comparing the differences in high-resolution remote sensing images from a single perspective to determine whether surface erosion has occurred, compared to using high-resolution remote sensing images from different perspectives to build a three-dimensional model, the amount of data processing is further reduced, thereby reducing the pressure on the monitoring system data processor and ensuring the smooth and reliable operation of the entire monitoring system.

[0107] It should be noted that, after the non-critical area is marked as the target area, erosion features can be extracted therefrom according to the process from S201 to S205.

[0108] In practical applications, considering that the risk of surface erosion in non-critical areas may increase under special climatic conditions, the occurrence of special climate can be predicted based on the current climate changes, so that the monitoring intensity can be increased in non-critical areas with higher risks in a timely manner.

[0109] Based on this, in one embodiment, the method may further include:

[0110] Periodically obtain the climate data of each non-critical area to obtain the climate characteristics of each non-critical area;

[0111] Based on the climate characteristics, determine whether extreme weather exists in each non-critical area during a preset sampling period; the preset sampling period includes a current sampling period and a next sampling period;

[0112] When extreme climate exists during the preset sampling period, the corresponding non-critical area is marked as a critical area.

[0113] It should be noted that, after the non-critical area is marked as the target area, erosion features can be extracted therefrom according to the process from S201 to S205.

[0114] Here, historical climate data of each non-critical area can be collected, and the weather prediction model can be trained using the historical climate data. Then, the climate data of N weeks before the current moment (N≥2) can be used to predict the probability of abnormal weather in the current sampling period and the next sampling period (i.e., the preset sampling period) using the weather prediction model. When the probability is greater than the threshold, it is judged that extreme climate may occur in the preset sampling period. At this time, the non-critical area can be marked as a critical area; the climate data can include temperature, precipitation, wind speed, wind direction, humidity, air pressure, and atmospheric stability, etc.

[0115] It should be pointed out that the process of establishing a weather prediction model using historical climate data, and predicting the weather using the weather prediction model and recent climate data, belongs to existing mature technologies and will not be described in detail in the embodiments of the present application.

[0116] In practical applications, after obtaining high-resolution remote sensing images of the target area from multiple perspectives in each sampling period, the three-dimensional model of the target area, namely DEM, can be established using the acquired images. Figure 6 As shown, for each target area, the process of constructing a DEM for a sampling period may include:

[0117] S601: Image preprocessing; the image preprocessing includes radiation correction and geometric correction;

[0118] S602: performing feature point matching on the preprocessed image; determining corresponding feature points between multiple high-resolution remote sensing images with different viewing angles, and performing feature point matching on feature points in different remote sensing images;

[0119] S603: triangulation: using the feature point matching results to perform triangulation and calculate the three-dimensional coordinates of the feature points;

[0120] S604: Construct DEM; convert the three-dimensional coordinates of the feature points into a DEM grid.

[0121] Exemplarily, the process of constructing a DEM may specifically include:

[0122] S1: Acquire multi-view remote sensing images;

[0123] Here, n high-resolution remote sensing images with different viewing angles are obtained. , each image covers a portion of this critical area.

[0124] S2: Image preprocessing;

[0125] Here, preprocessing can ensure accurate registration between images, and the preprocessing process includes radiation correction, geometric correction, etc.

[0126] S3: feature point matching;

[0127] Here, feature point matching algorithms such as SIFT and SURF are used to find corresponding feature points between remote sensing images; remote sensing images The feature points are represented as .

[0128] S4: triangulation;

[0129] Here, the feature point matching results are used for triangulation to calculate the three-dimensional coordinates of the feature points; where the three-dimensional coordinates X It is expressed using the following formula:

[0130] ;

[0131] in, is the camera intrinsic parameter matrix, is the rotation matrix, For feature points In the image The coordinates in is the translation matrix.

[0132] S5: construct DEM;

[0133] Convert the three-dimensional coordinates calculated by S4 into a DEM grid;

[0134] Specifically, first determine the DEM grid size and resolution , and then map the 3D coordinates into a grid.

[0135] In actual application, after the DEM is constructed, S203 is executed to compare the two DEMs.

[0136] In one embodiment, if Figure 7 As shown, judging whether there is a surface erosion defect in the target area based on the difference in terrain data between the first model and the reference model may include:

[0137] S701: judging whether each target location has a surface erosion defect based on a difference in terrain data of the target location in the first model and the reference model; the target location is a location in the target area where there is a risk of surface erosion; the target location includes at least one pre-configured first location;

[0138] S702: When the judgment result indicates that at least one target location has a surface erosion defect, determining that the target area has a surface erosion defect;

[0139] S703: When the judgment result indicates that all target areas do not have surface erosion defects, determine that the target area does not have surface erosion defects.

[0140] In actual application, when judging whether there is a surface erosion defect at each target position based on the difference in terrain data between the target position in the first model and the benchmark model, it can be understood that based on the position of each target position in the first model and the benchmark model, an image of each target position in the first model and the benchmark model is obtained, and then, based on the obtained image, the terrain difference of each target position in the first model and the benchmark model is determined, and based on the determined terrain difference, it is judged whether there is a surface erosion defect at each target position.

[0141] In practical applications, methods such as image difference analysis, principal component analysis (PCA), and normalized difference index (NDI) can be used to compare the acquired images to identify areas of surface change.

[0142] In actual application, the target position may be understood as a position where there are terrain fluctuations in the target area, such as gullies, slopes, etc. Here, the target position may be configured in advance.

[0143] Based on this, in one embodiment, the method may further include:

[0144] Based on the benchmark model, performing terrain analysis on the target area to obtain actual terrain features;

[0145] At least one target location is configured in the target area based on the actual terrain features.

[0146] In actual application, the target area is subjected to terrain analysis based on the reference model to obtain actual terrain features, that is, the terrain of the target area is analyzed based on the initial terrain of the target area, thereby obtaining locations in the target area with large terrain fluctuations.

[0147] Based on this, in one embodiment, performing terrain analysis on the target area based on the reference model to obtain actual terrain features may include:

[0148] Based on the reference model, determining the elevation change rate of adjacent pixel units to obtain elevation change information;

[0149] Based on the elevation change information, determine the value of each terrain indicator of a plurality of preset terrain indicators of the target area to obtain terrain indicator information;

[0150] Based on the terrain indicator information, actual terrain features of the target area are determined.

[0151] In actual application, the pixel unit represents the pixel unit in DEM; the DEM data is expressed as a two-dimensional array, each pixel unit represents a small area on the ground, and its value represents the elevation of the area (that is, the height of the ground); the elevation change rate of adjacent pixel units can reflect the height difference of the ground.

[0152] In practical applications, the terrain indicators may include slope, aspect and terrain curvature.

[0153] In practical applications, pixel units with similar terrain features can be clustered according to the similarity of pixel units in DEM, thereby achieving the division of the area corresponding to the target location.

[0154] Based on this, in one embodiment, configuring at least one target position in the target area based on the actual terrain features may include:

[0155] Based on the actual terrain features, clustering multiple pixel units in the target area to obtain a clustering result;

[0156] Based on the clustering result, the target area is topographically divided to obtain a plurality of sub-areas;

[0157] At least one target location is determined from the plurality of sub-regions based on the clustering feature of each sub-region.

[0158] In actual application, based on the clustering result, the target area is topographically divided to obtain multiple sub-areas, which can be understood as clustering and splicing pixel units with similar and adjacent terrain features into one sub-area; based on the clustering characteristics of each sub-area, at least one target position is determined from the multiple sub-areas, which can be understood as determining whether the sub-area is a region of severe fluctuations based on the terrain characteristics of the pixel units in each sub-area, and setting the sub-area with severe fluctuations as the target position.

[0159] In actual application, on the basis of setting the location where the terrain fluctuates violently as the target location, the risk of surface erosion in each sub-area can be predicted according to historical data to determine the target location.

[0160] Based on this, in one embodiment, the target position also includes at least one second position; the second position may satisfy one of the following conditions:

[0161] Surface erosion occurred in the first time period before the current moment;

[0162] The presence of geographical features that match the pre-set erosion risk indicators;

[0163] There are climate features that match the preset erosion risk indicators.

[0164] In actual application, after the location where surface erosion occurs is repaired, the risk of surface erosion occurring at this location is still high due to specific factors such as terrain and climate; therefore, when surface erosion has occurred within a first time period before the current moment, the second location can be set as the target location; here, the first time period can be set according to the actual application scenario, for example, the first time period can be one year.

[0165] In actual application, erosion risk indicators can be set in advance, and the erosion risk indicators include geographical indicators and climate indicators; the existence of geographical features that match the preset erosion risk indicators can be understood as the existence of geographical features that match the geographical indicators, and the existence of climate features that match the preset erosion risk indicators can be understood as the existence of geographical features that match the climate indicators; wherein the geographical indicators may include a slope greater than a slope threshold (for example, greater than 5°), a curvature greater than a curvature threshold (for example, greater than 0.5 / m), a vegetation coverage rate lower than a threshold (for example, lower than 30%), a soil roughness lower than a threshold (for example, lower than 0.1 / m), etc.; climate indicators may include rainfall greater than a threshold (for example, annual rainfall greater than 1000mm), an average rainfall intensity greater than a threshold (for example, the average rainfall intensity within 40 days exceeds 50mm / h), an average temperature greater than a threshold (for example, the annual average temperature exceeds 20°), etc.

[0166] In practical application, after determining that the target area has surface erosion defects, an image of the surface erosion area can be obtained from the first model, and surface erosion features can be extracted from the image.

[0167] In actual application, the model can be trained with machine learning algorithms to identify surface erosion features, thereby automatically extracting surface erosion features such as gullies and landslides; for example, machine learning algorithms such as support vector machines (SVM) and random forests (RF) can be used.

[0168] In actual application, after determining that surface erosion has occurred, the corresponding environmental and water conservation management strategy can be configured according to the type of surface erosion.

[0169] Based on this, in one embodiment, configuring the environmental and water conservation management strategy corresponding to the surface erosion location based on the surface erosion characteristics may include:

[0170] Based on the surface erosion characteristics, determining a surface erosion type corresponding to a surface erosion position from at least one preset surface erosion type; the surface erosion type includes gully erosion, sheet erosion, watershed erosion, riverbank erosion or slope erosion;

[0171] Based on the surface erosion type, configure corresponding environmental and water conservation management strategies.

[0172] In actual application, typical surface erosion types can be pre-configured, and corresponding governance strategies can be configured for each typical surface erosion type; in actual application, the erosion type of the target area is determined based on the extracted surface erosion characteristics, and the corresponding governance strategy is called.

[0173] In actual application, the control strategy can also be called the restoration strategy; different environmental and water conservation control strategies can be configured for different types of surface erosion.

[0174] For example, gully erosion means that there are obvious gullies or grooves on the ground, which are caused by concentrated water flow eroding the soil, usually distributed along hillsides or rivers. For gully erosion, the control strategies that can be adopted include: strengthening the banks of the gully, setting up diversion dams, blocking the gully, restoring vegetation, etc.

[0175] Sheet erosion means that a thin layer of soil surface is evenly removed without obvious gullies, which is caused by rainwater directly washing away the exposed soil surface. The strategies that can be adopted to control sheet erosion include: crop rotation, stubble cultivation, increasing vegetation cover, and improving soil.

[0176] Watershed erosion refers to the widespread loss of soil in the entire watershed, which is caused by heavy rainfall and the lack of effective soil and water conservation measures in the watershed. For watershed erosion, the management strategies that can be adopted include: planning watershed management, building water conservation forests at the source of the watershed and important water sources, and ecological restoration.

[0177] Riverbank erosion refers to the erosion of the soil on both sides of the river, which causes the river bank to retreat. It is caused by the river flow eroding the river bank. The strategies that can be adopted to deal with riverbank erosion include: strengthening the river bank, setting up diversion dams, and restoring vegetation.

[0178] Slope erosion means that the soil on the hillside is washed away, forming gullies or bare land. It is caused by heavy rainfall and lack of vegetation cover on the slope. For slope erosion, the control strategies that can be adopted include: increasing vegetation cover, building terraces and drainage facilities, etc.

[0179] In summary, the high-resolution remote sensing-based environmental and water conservation monitoring method for power transmission and transformation lines provided in the embodiment of the present application obtains multi-perspective high-resolution remote sensing images of the same area, constructs a DEM model of the area, and performs time series analysis on the DEM model, thereby judging whether surface erosion occurs in the area based on the difference in terrain data in the DEM model. Compared with traditional remote sensing monitoring technology, the use of multi-perspective remote sensing images for analysis improves the comprehensiveness of terrain data, thereby improving the accuracy and reliability of the analysis results, that is, improving the accuracy and reliability of environmental and water conservation monitoring results. Furthermore, since the changes in terrain data are determined based on the results of time series analysis to determine whether surface erosion occurs, areas with unclear erosion characteristics can be detected in a timely manner. Compared with using a single image to judge the erosion results, the timeliness of detection is improved, so that prevention and repair can be carried out in advance to ensure the safety of the environment around the power transmission and transformation lines, thereby ensuring the safe and stable operation of the power transmission and transformation lines.

[0180] In order to implement the power transmission and transformation line environmental water conservation monitoring method based on high-resolution remote sensing of the present application, the present application embodiment also provides a power transmission and transformation line environmental water conservation monitoring device based on high-resolution remote sensing, such as Figure 8 As shown, the device may include:

[0181] The data acquisition unit 801 is used to acquire a high-resolution remote sensing image of a target area in at least one sampling period along a time axis to obtain first image data of each sampling period; the target area is determined according to the position of the power transmission and transformation line; the first image data includes high-resolution remote sensing images acquired from multiple viewing angles;

[0182] A model building unit 802 is used to build a DEM of the target area in a current sampling period based on the first image data;

[0183] A calculation unit 803 is used to determine whether there is a surface erosion defect in the target area based on the difference in terrain data between the first model and the benchmark model; the first model is the DEM of the current sampling period, and the benchmark model is the DEM of the first sampling period;

[0184] The processing unit 804 is used to extract the surface erosion characteristics of the surface erosion location based on the first model when the judgment result indicates that there are surface erosion defects in the target area; and configure the environmental and water conservation management strategy corresponding to the surface erosion location based on the surface erosion characteristics.

[0185] In one embodiment, the calculation unit 803 may be specifically used for:

[0186] Based on the difference in terrain data of the target location in the first model and the reference model, determining whether each target location has a surface erosion defect; the target location is a location in the target area where there is a risk of surface erosion; the target location includes at least one pre-configured first location;

[0187] In the case where the judgment result indicates that at least one target location has a surface erosion defect, determining that the target area has a surface erosion defect;

[0188] When the judgment result indicates that there are no surface erosion defects in all target areas, it is determined that there are no surface erosion defects in the target areas.

[0189] In one embodiment, the processing unit 804 may also be used for:

[0190] Based on the benchmark model, performing terrain analysis on the target area to obtain actual terrain features;

[0191] At least one target location is configured in the target area based on the actual terrain features.

[0192] In one embodiment, performing terrain analysis on the target area based on the reference model to obtain actual terrain features may include:

[0193] Based on the reference model, determining the elevation change rate of adjacent pixel units to obtain elevation change information;

[0194] Based on the elevation change information, determine the value of each terrain indicator of a plurality of preset terrain indicators of the target area to obtain terrain indicator information;

[0195] Based on the terrain indicator information, actual terrain features of the target area are determined.

[0196] In one embodiment, configuring at least one target location in the target area based on the actual terrain features may include:

[0197] Based on the actual terrain features, clustering multiple pixel units in the target area to obtain a clustering result;

[0198] Based on the clustering result, the target area is topographically divided to obtain a plurality of sub-areas;

[0199] At least one target location is determined from the plurality of sub-regions based on the clustering feature of each sub-region.

[0200] In one embodiment, the target position may further include at least one second position; the second position satisfies one of the following conditions:

[0201] Surface erosion occurred in the first time period before the current moment;

[0202] The presence of geographical features that match the pre-set erosion risk indicators;

[0203] There are climate features that match the preset erosion risk indicators.

[0204] In one embodiment, the processing unit may also be used to:

[0205] Based on the image acquisition range of the remote sensing satellite, the area along the power transmission and transformation line to be tested is divided into at least one monitoring area;

[0206] Based on the geomorphic features and soil features of each monitoring area, the at least one monitoring area is classified to obtain at least one key area and at least one non-key area, and the at least one key area is used as a target area.

[0207] In one embodiment, the processing unit 804 may also be used for:

[0208] Periodically obtain the climate data of each non-critical area to obtain the climate characteristics of each non-critical area;

[0209] Based on the climate characteristics, determine whether extreme weather exists in each non-critical area during a preset sampling period; the preset sampling period includes a current sampling period and a next sampling period;

[0210] When extreme climate exists during the preset sampling period, the corresponding non-critical areas will be marked as critical areas.

[0211] In one embodiment, the processing unit 804 may be specifically configured to:

[0212] Based on the surface erosion characteristics, determining a surface erosion type corresponding to a surface erosion position from at least one preset surface erosion type; the surface erosion type includes gully erosion, sheet erosion, watershed erosion, riverbank erosion or slope erosion;

[0213] Based on the surface erosion type, configure corresponding environmental and water conservation management strategies.

[0214] It should be noted that: the power transmission and transformation line environmental water and water conservation monitoring device based on high-resolution remote sensing provided in the above embodiment only uses the division of the above-mentioned program modules as an example when performing power transmission and transformation line environmental water and water conservation monitoring based on high-resolution remote sensing. In actual applications, the above-mentioned processing can be assigned to different program modules as needed, that is, the internal structure of the device can be divided into different program modules to complete all or part of the processing described above. In addition, the power transmission and transformation line environmental water and water conservation monitoring device based on high-resolution remote sensing provided in the above embodiment and the power transmission and transformation line environmental water and water conservation monitoring method embodiment based on high-resolution remote sensing belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0215] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0216] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0217] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A method for monitoring environmental and water conservation of power transmission and transformation lines based on high-resolution remote sensing, characterized in that: The method comprises: Acquire a high-resolution remote sensing image of a target area in at least one sampling period along a time axis to obtain first image data of each sampling period; the target area is determined according to the position of the power transmission and transformation line; the first image data includes high-resolution remote sensing images acquired from multiple viewing angles; Based on the first image data, constructing a DEM of the target area in the current sampling period; Based on the difference in terrain data between the first model and the benchmark model, determining whether there are surface erosion defects in the target area; the first model is the DEM of the current sampling period, and the benchmark model is the DEM of the first sampling period; When the judgment result indicates that there is a surface erosion defect in the target area, extracting the surface erosion characteristics of the surface erosion location based on the first model; Based on the surface erosion characteristics, an environmental and water conservation management strategy corresponding to the surface erosion location is configured; wherein: The method of judging whether a target area has a surface erosion defect based on the difference in terrain data between the first model and the reference model includes: judging whether each target location has a surface erosion defect based on the difference in terrain data between the target location in the first model and the reference model; the target location is a location in the target area where there is a risk of surface erosion; the target location includes at least one pre-configured first location; when the judgment result indicates that at least one target location has a surface erosion defect, determining that the target area has a surface erosion defect; when the judgment result indicates that all target areas do not have a surface erosion defect, determining that the target area does not have a surface erosion defect; The method further comprises: performing terrain analysis on the target area based on the reference model to obtain actual terrain features; configuring at least one target position in the target area based on the actual terrain features; wherein, The step of performing terrain analysis on the target area based on the reference model to obtain actual terrain features includes: determining the elevation change rate of adjacent pixel units based on the reference model to obtain elevation change information; determining the value of each terrain indicator among a plurality of preset terrain indicators of the target area based on the elevation change information to obtain terrain indicator information; and determining the actual terrain features of the target area based on the terrain indicator information; The configuring at least one target position in the target area based on the actual terrain features comprises: clustering a plurality of pixel units in the target area based on the actual terrain features to obtain a clustering result; dividing the target area into terrains based on the clustering result to obtain a plurality of sub-areas; and determining at least one target position from the plurality of sub-areas based on the clustering features of each sub-area.

2. The method according to claim 1, characterized in that The target position also includes at least one second position; the second position satisfies one of the following conditions: Surface erosion occurred in the first time period before the current moment; The presence of geographical features that match the pre-set erosion risk indicators; There are climate features that match the preset erosion risk indicators.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Based on the image acquisition range of the remote sensing satellite, the area along the power transmission and transformation line to be tested is divided into at least one monitoring area; Based on the geomorphic features and soil features of each monitoring area, the at least one monitoring area is classified to obtain at least one key area and at least one non-key area, and the at least one key area is used as a target area.

4. The method according to claim 3, characterized in that: The method further comprises: Periodically obtain the climate data of each non-critical area to obtain the climate characteristics of each non-critical area; Based on the climate characteristics, determine whether extreme weather exists in each non-critical area during a preset sampling period; the preset sampling period includes a current sampling period and a next sampling period; When extreme climate exists during the preset sampling period, the corresponding non-critical areas will be marked as critical areas.

5. The method according to claim 1 or 2, characterized in that: The configuring of an environmental and water conservation management strategy corresponding to the surface erosion location based on the surface erosion characteristics includes: Based on the surface erosion characteristics, determining a surface erosion type corresponding to a surface erosion position from at least one preset surface erosion type; the surface erosion type includes gully erosion, sheet erosion, watershed erosion, riverbank erosion or slope erosion; Based on the surface erosion type, configure corresponding environmental and water conservation management strategies.

6. A high-resolution remote sensing-based environmental and water conservation monitoring device for power transmission and transformation lines, characterized in that: The device comprises: A data acquisition unit, used to acquire a high-resolution remote sensing image of a target area in at least one sampling period along a time axis to obtain first image data of each sampling period; the target area is determined according to the position of the power transmission and transformation line; the first image data includes high-resolution remote sensing images acquired from multiple viewing angles; A model building unit, used for building a DEM of the target area in a current sampling period based on the first image data; A calculation unit, used to determine whether there are surface erosion defects in the target area based on the difference in terrain data between a first model and a reference model; the first model is the DEM of the current sampling period, and the reference model is the DEM of the first sampling period; The processing unit is used to extract the surface erosion characteristics of the surface erosion location based on the first model when the judgment result indicates that the target area has surface erosion defects; and configure the environmental and water conservation management strategy corresponding to the surface erosion location based on the surface erosion characteristics; wherein, The calculation unit is specifically used to: determine whether each target position has a surface erosion defect based on the difference in terrain data of the target position in the first model and the reference model; the target position is a position where there is a risk of surface erosion in the target area; the target position includes at least one pre-configured first position; when the judgment result indicates that at least one target position has a surface erosion defect, determine that the target area has a surface erosion defect; when the judgment result indicates that all target areas do not have a surface erosion defect, determine that the target area does not have a surface erosion defect; The processing unit is further used to: perform terrain analysis on the target area based on the reference model to obtain actual terrain features; and configure at least one target position in the target area based on the actual terrain features; wherein The step of performing terrain analysis on the target area based on the reference model to obtain actual terrain features includes: determining the elevation change rate of adjacent pixel units based on the reference model to obtain elevation change information; determining the value of each terrain indicator among a plurality of preset terrain indicators of the target area based on the elevation change information to obtain terrain indicator information; and determining the actual terrain features of the target area based on the terrain indicator information; The configuring at least one target position in the target area based on the actual terrain features comprises: clustering a plurality of pixel units in the target area based on the actual terrain features to obtain a clustering result; dividing the target area into terrains based on the clustering result to obtain a plurality of sub-areas; and determining at least one target position from the plurality of sub-areas based on the clustering features of each sub-area.

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