Power transmission line settlement risk evaluation method, storage medium and computer equipment

Abstract

The invention discloses a power transmission line settlement risk evaluation method, a storage medium and computer equipment, and the method comprises the steps: calculating pore water pressure in consideration of rainfall and a dynamic process, calculating slope body sliding speeds of a target area at different times, constructing a landslide dynamic balance differential equation based on a rainfall-driven peristaltic landslide collapse model, and calculating the settlement risk of a power transmission line according to the landslide dynamic balance differential equation. And predicting the time possibly required for the potential landslide in the target area to develop from slow wriggling to collapse by using the equation and the slope body sliding speed, obtaining the slope body collapse rate, obtaining deformation data based on an interferometric synthetic aperture radar deformation method, and taking the deformation data as one of landslide driving factors so as to obtain the landslide driving rate. And inputting the driving factors and other driving factors into three machine learning methods to obtain the landslide susceptibility, namely the landslide probability. The landslide risk along the power transmission tower is evaluated by combining the landslide probability and the slope collapse rate, the interpretability and accuracy of landslide risk evaluation are improved, and powerful support is provided for landslide disaster prevention and post-disaster maintenance of the power transmission line.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a transmission line settlement risk assessment method, storage medium, and computer equipment. Background Art

[0002] Landslides are the most widespread geological hazards in the world. Due to the frequent occurrence of landslides caused by climate change, earthquakes, human engineering activities, etc., they pose a serious threat to human life, property and infrastructure. According to the scale of displacement rate or movement speed, the movement of landslides can be generally defined as extremely slow, relatively slow, medium, relatively fast and extremely fast. Unlike fast-collapse landslides, slow-moving landslides usually evolve at a rate of millimeters to several meters per year within a few years to decades, and are dominated by friction sliding and / or viscoplastic flow along discrete sliding zones. Although slow-moving landslides rarely claim lives, they can cause serious damage to terrain, houses, infrastructure and agricultural production. It is worth noting that slow-moving landslides may also accelerate to rapid movement and collapse due to pore water pressure disturbance caused by rainfall, resulting in loss of life and safety.

[0003] In reactivated slow-moving landslides, the prediction of velocity and displacement is a key issue in understanding landslide kinematics and implementing early warning systems. Various data-based numerical models have been used to predict landslide displacement; however, dynamics-based models are relatively limited, and these models consider the shear behavior of the soil within the sliding zone that fundamentally controls landslide movement. The ability to reliably predict the velocity and displacement of landslides is of great significance for predicting the movement trend and state of landslides, as well as for early warning of slow-moving landslides. However, such predictions of slip rate and state are often based on potential landslide areas, which can only be predicted for the included potential landslide areas, and cannot be used for overall risk assessment of a large-scale study area.

[0004] Landslide susceptibility assessment is a process of comprehensively studying factors such as geology, hydrology, and geomorphology in landslide areas to evaluate the probability of landslides occurring in target areas. With the development and application of remote sensing, GIS and other technologies, the research methods of landslide susceptibility assessment have also been greatly expanded and improved. Among the technologies related to susceptibility assessment, machine learning methods can make adaptive adjustments based on different data types, better balance evaluation accuracy and operating efficiency, and discover hidden patterns and relationships in the data. However, landslide susceptibility assessment based on machine learning is based on static evaluation factors, often lacks time series and dynamic information, and cannot take into account the characteristics of continuous changes in surface conditions over a period of time. Summary of the invention

[0005] In view of this, the present application provides a transmission line settlement risk assessment method and storage medium, computer equipment, taking rainfall and dynamic processes into consideration to calculate pore water pressure, and then calculating the slope sliding speed at different times in the target area, and constructing a landslide dynamic equilibrium differential equation based on a rainfall-driven creeping landslide collapse model, and using the equation and slope sliding speed to predict the time that may be required for a potential landslide in the target area to develop from slow creeping to collapse, and obtain the slope collapse rate, and obtain deformation data based on an interferometric synthetic aperture radar deformation method, and use the deformation data as one of the landslide driving factors, and input it into three machine learning methods together with other driving factors to obtain landslide susceptibility, that is, landslide probability. The landslide risk along the transmission tower is assessed by combining the landslide probability with the slope collapse rate, which improves the interpretability and accuracy of the landslide risk assessment and provides strong support for landslide disaster prevention and post-disaster maintenance of transmission lines.

[0006] According to one aspect of the present application, a method for assessing the risk of transmission line subsidence is provided, the method comprising:

[0007] Based on the needs of evaluating the transmission lines with settlement risks, determining the target area, and obtaining the original elevation information of the target area;

[0008] Acquire single-view complex images of the target area at different times within a preset evaluation period, pair the single-view complex images to obtain a plurality of candidate interference image pairs, and optimize the candidate interference image pairs to obtain a plurality of target interference image pairs;

[0009] For any target interference image pair, an interference fringe pattern is generated based on the target interference image pair, and the interference phase in the interference fringe pattern is converted into elevation information by using a phase-elevation conversion method. The terrain phase is simulated and removed in the interference fringe pattern by using the converted elevation information and the original elevation information to obtain a deformation phase pattern, and the deformation phase patterns of each target interference image pair are combined to obtain deformation data of the target area within a preset evaluation period, wherein the interference fringe pattern includes an interference phase, and the interference phase is composed of a terrain phase and a deformation phase;

[0010] Calculate the pore water pressure of the target area within a preset evaluation period, calculate the slope sliding speed of the target area at different times within the preset evaluation period based on the pore water pressure and the landslide dynamic equilibrium differential equation, reclassify the slope sliding speed at different times based on the natural discontinuity method, and obtain the slope collapse rate of the target area at different times within the preset evaluation period, wherein the slope collapse rate includes extremely fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and extremely slow collapse;

[0011] Acquire settlement driving factor data of the target area within a preset evaluation period, and predict the probability of landslide in the target area within the preset evaluation period based on the deformation data, the settlement driving factor data and a preset landslide probability prediction model to obtain a landslide probability prediction result, wherein the settlement driving factor data includes data of multiple preset settlement driving factors;

[0012] The landslide probability prediction results of the target area within the preset evaluation period and the slope collapse rates at different times are combined to obtain the settlement risk assessment results of the target area within the preset evaluation period.

[0013] According to another aspect of the present application, a storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned transmission line settlement risk assessment method is implemented.

[0014] According to another aspect of the present application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the above-mentioned transmission line settlement risk assessment method when executing the program.

[0015] By means of the above technical scheme, the present application provides a transmission line settlement risk assessment method and storage medium, computer equipment, which considers rainfall and dynamic processes to calculate pore water pressure, and then calculates the slope sliding speed at different times in the target area. Based on the rainfall-driven creeping landslide collapse model, a landslide dynamic equilibrium differential equation is constructed, and the equation and slope sliding speed are used to predict the time required for the potential landslide in the target area to develop from slow creeping to collapse, and the slope collapse rate is obtained. The deformation data is obtained based on the interferometric synthetic aperture radar deformation method, and the deformation data is used as one of the landslide driving factors. Together with other driving factors, it is input into three machine learning methods to obtain the landslide susceptibility, that is, the landslide probability. The landslide risk along the transmission tower is evaluated by combining the landslide probability with the slope collapse rate, which improves the interpretability and accuracy of the landslide risk assessment and provides strong support for the landslide disaster prevention and post-disaster maintenance of the transmission line.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic diagram of a flow chart of a transmission line settlement risk assessment method provided in an embodiment of the present application is shown;

[0019] Figure 2 A schematic diagram of a flow chart of another transmission line settlement risk assessment method provided in an embodiment of the present application is shown;

[0020] Figure 3 A schematic diagram of an SBAS deformation inversion process provided by an embodiment of the present application is shown;

[0021] Figure 4 A schematic diagram showing a flow chart of another transmission line settlement risk assessment method provided in an embodiment of the present application;

[0022] Figure 5 A schematic diagram of a transmission line slope collapse time estimation process provided by an embodiment of the present application is shown;

[0023] Figure 6 A schematic diagram of a landslide susceptibility assessment process along a transmission line provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0025] In this embodiment, a transmission line settlement risk assessment method is provided. Figure 1 As shown, the method includes:

[0026] Step 101, based on the transmission lines with subsidence risk that need to be evaluated, a target area is determined, and original elevation information of the target area is obtained.

[0027] In the above embodiment of the present application, based on the transmission line with the risk of settlement of the demand evaluation, the target area is determined, and then the original elevation information of the target area is obtained. For example, the SR TMDEM data of the target area can be obtained. The SR TMDEM data is the digital elevation model (Digital Elevation Model) data collected by the Shuttle Radar Topography Mission (Shuttle Radar Topography Mission), which is obtained by NASA and the National Bureau of Surveying and Mapping of the Ministry of National Defense, etc., using the radar equipment on the space shuttle to perform high-precision remote sensing measurements of the earth's surface. These data can be used to create a global digital elevation model to provide accurate elevation information on the earth's surface.

[0028] Step 102, obtaining single-view complex images of the target area at different times within a preset evaluation period, pairing the single-view complex images to obtain a plurality of candidate interference image pairs, and optimizing the candidate interference image pairs to obtain a plurality of target interference image pairs.

[0029] Step 103, for any target interference image pair, an interference fringe pattern is generated based on the target interference image pair, and the interference phase in the interference fringe pattern is converted into elevation information using a phase-elevation conversion method, and the terrain phase is simulated and removed in the interference fringe pattern using the converted elevation information and the original elevation information to obtain a deformation phase pattern, and the deformation phase patterns of each target interference image pair are combined to obtain deformation data of the target area within a preset evaluation period, wherein the interference fringe pattern includes an interference phase, and the interference phase is composed of a terrain phase and a deformation phase.

[0030] Next, obtain the single-view complex images of the target area at different times within a preset evaluation period (for example, within two months), perform pairing processing on the single-view complex images, obtain multiple candidate interference image pairs, optimize the candidate interference image pairs, and obtain multiple target interference image pairs. Single-view complex images, such as Sentinel-1A SLC data, Sentinel-1A, or "Sentinel 1A", is an earth observation satellite in the Copernicus program of the European Space Agency. It carries a C-band synthetic aperture radar and can provide all-weather and all-time earth surface imaging services. SLC data is a data product of Sentinel-1A, which contains amplitude and phase information. In particular, satellite orbit data covering the target area can also be downloaded so that it can be used to assist in monitoring and analyzing the topography of the target area. Next, the acquired Sentinel-1A images can be subjected to interference pair combination, interference processing, orbit refinement and re-flattening, deformation inversion, and geocoding to obtain the final deformation data.

[0031] Step 104, calculate the pore water pressure of the target area within a preset evaluation period, calculate the slope sliding speed of the target area at different times within the preset evaluation period based on the pore water pressure and the landslide dynamic equilibrium differential equation, reclassify the slope sliding speed at different times based on the natural discontinuity method, and obtain the slope collapse rate of the target area at different times within the preset evaluation period, wherein the slope collapse rate includes extremely fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and extremely slow collapse.

[0032] Next, collect rainfall data such as the groundwater level of the target area soil, the distance of the groundwater below the surface from the surface, the steady-state friction coefficient, the inclination of the sliding surface, etc., and use the rainfall data to calculate the pore water pressure in the soil. Combined with the steady-state equation of the landslide (dynamic equilibrium differential equation of the landslide), calculate the slope sliding speed at different times in the target area within the preset evaluation period. Based on the natural break method, the slope sliding speed at different times is reclassified to obtain the slope collapse rate at different times in the target area within the preset evaluation period. The Natural Breaks Method is a statistical method for data classification and grouping, which is particularly suitable for geographic information systems (GIS) and spatial data analysis. It is based on the natural distribution characteristics of the data and identifies the natural break points in the data by analyzing the numerical distribution in the data set. These break points are places where the data values ​​change significantly. The specific classification method is as follows:

[0033] 1. Collect, organize and quantify the data to be classified (i.e. slope sliding speed at different times);

[0034] 2. Select how many categories (k) to divide the data into, which is usually determined based on the characteristics of the data and the purpose of analysis. In the above embodiment of the present application, the categories can be set to 5, namely, very fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and very slow collapse;

[0035] 3. For each possible grouping scheme, calculate the within-group variance (Within-group variance), the formula is:

[0036]

[0037] k is the number of categories, n is j is the number of data of the jth category, x ij is the i-th data point in the j-th class, is the mean of the j-th class of data.

[0038] 4. Calculate the between-group variance (Between-group variance), the formula is:

[0039]

[0040] is the overall mean of all data.

[0041] 5. The goal is to minimize the within-group variance W or maximize the between-group variance B. The optimal grouping can be found by iterating different grouping schemes.

[0042] Step 105, obtaining the settlement driving factor data of the target area within a preset evaluation period, and predicting the probability of landslide occurring in the target area within the preset evaluation period based on the deformation data, the settlement driving factor data and a preset landslide probability prediction model to obtain a landslide probability prediction result, wherein the settlement driving factor data includes data of multiple preset settlement driving factors.

[0043] Step 106, combining the landslide probability prediction results of the target area within the preset evaluation period and the slope collapse rates at different times, to obtain the settlement risk assessment results of the target area within the preset evaluation period.

[0044] Next, the settlement driving factor data of the target area is obtained, and the probability of landslide in the target area is predicted based on the deformation data, settlement driving factor data and the preset landslide probability prediction model, and the landslide probability prediction result of the target area is obtained, wherein the settlement driving factor data includes data of multiple preset settlement driving factors. Finally, the settlement risk evaluation result of the target area within the preset evaluation period is obtained by combining the landslide probability prediction results of the target area within the preset evaluation period and the slope collapse rate at different times, and the evaluation result is more reliable.

[0045] By applying the technical solution of this embodiment, the pore water pressure is calculated by considering rainfall and dynamic processes, and then the slope sliding speed in the target area at different times is calculated. Based on the creeping landslide collapse model driven by rainfall, the dynamic equilibrium differential equation of the landslide is constructed. The equation and the slope sliding speed are used to predict the time that may be required for the potential landslide in the target area to develop from slow creeping to collapse, and the slope collapse rate is obtained. The deformation data is obtained based on the interferometric synthetic aperture radar deformation method, and the deformation data is used as one of the landslide driving factors. Together with other driving factors, it is input into three machine learning methods to obtain the landslide susceptibility, that is, the landslide probability. The landslide risk along the transmission tower is evaluated by combining the landslide probability with the slope collapse rate, which improves the interpretability and accuracy of the landslide risk assessment and provides strong support for the landslide disaster prevention and post-disaster maintenance of the transmission line.

[0046] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another transmission line settlement risk assessment method is provided, such as Figure 2 As shown, the method includes:

[0047] Step 201, determine the transmission lines that need to be evaluated for settlement risk, expand the transmission lines that need to be evaluated for settlement risk to a preset width to obtain a target area, and obtain original elevation information of the target area.

[0048] Step 202, obtaining single-view complex images of the target area at different times within a preset evaluation period, and determining two single-view complex images whose time interval does not exceed the preset time and whose spatial distance does not exceed the preset distance as a candidate interference image pair, until multiple candidate interference image pairs are determined.

[0049] In the above implementation of the present application, the transmission lines for which the settlement risk needs to be evaluated are determined, such as Figure 3 As shown, the transmission line vector data can be obtained, and the target area can be obtained by expanding the preset width (for example, 10 km) around the transmission line vector data, that is, within the coverage of the data, a buffer zone of a certain width around the transmission line is selected as the target area, and the original elevation information (SRTMDEM data) of the target area is obtained. At the same time, the Sentinel-1A single-view complex images (Sentinel-1A SLC data) of the target area at different times within the preset evaluation period are obtained, and the satellite orbit data is downloaded, and two single-view complex images with a time interval not exceeding the preset time and a spatial distance not exceeding the preset distance are obtained, and are determined as a candidate interference image pair until multiple candidate interference image pairs are determined. For example, the time baseline threshold of the SBAS-InSAR process is set to 60 days, and the space baseline threshold is set to 150 meters, and candidate interference pairs that meet the above thresholds are generated, so that the settlement risk of the target area can be predicted in combination with InSAR deformation in the future. SBAS, namely Satellite-Based Augmentation System, can enhance the positioning accuracy of satellite navigation systems such as GPS. InSAR deformation is the abbreviation of "Interferometric Synthetic Aperture Radar deformation" or "Synthetic Aperture Radar Interferometry Deformation". "InSAR" is the abbreviation of "Interferometric Synthetic Aperture Radar", which means the interferometric synthetic aperture radar technology, and "deformation" refers to the small deformation or displacement of the surface or objects monitored by this technology. After interferometric combination, such as Figure 3 As shown, interference processing, track refinement and re-leveling, and deformation inversion geocoding can be performed to facilitate data clipping and transmission line settlement analysis. Finally, transmission line settlement analysis, Kriging interpolation, and deformation rate result verification can be performed.

[0050] Step 203, for any candidate interference image pair, calculate the similarity between the two single-view complex images in the candidate interference image pair to obtain a coherence coefficient map, perform conjugate multiplication on the two single-view complex images in the candidate interference image pair to obtain an interference fringe map, and use the minimum cost flow algorithm of the Delaunay triangulation network to phase unwrap the interference fringe map to obtain an unwrap map.

[0051] Step 204, among multiple candidate interference image pairs, remove the candidate interference image pairs corresponding to the coherence coefficient map with low correlation, remove the candidate interference image pairs corresponding to the interference fringe map with low interference quality, and remove the candidate interference image pairs corresponding to the detangling map with low detangling quality, to obtain multiple target interference image pairs.

[0052] Then, the minimum cost flow (MCF) algorithm of the Delaunay triangulation can be used for phase unwrapping. After the interference processing is completed, the quality of the interference pair is judged according to the coherence coefficient diagram, interference diagram and unwrapping diagram, and the interference pairs with low coherence and poor unwrapping quality are removed to avoid affecting the inversion effect.

[0053] Step 205, for any target interference image pair, an interference fringe pattern is generated based on the target interference image pair, and the interference phase in the interference fringe pattern is converted into elevation information using a phase-elevation conversion method, and the terrain phase is simulated and removed in the interference fringe pattern using the converted elevation information and the original elevation information to obtain a deformation phase pattern, and the deformation phase patterns of each target interference image pair are combined to obtain deformation data of the target area within a preset evaluation period, wherein the interference fringe pattern includes an interference phase, and the interference phase is composed of a terrain phase and a deformation phase.

[0054] Then, the acquired original SRTM data can be used as an external DEM (digital elevation model) to eliminate the terrain phase of the interference pair. For example, a multi-view operation of 8 pixels in the distance and 2 pixels in the azimuth is performed during the interference processing to suppress noise and improve the signal-to-noise ratio of the interference pattern. In particular, after obtaining the interference fringe pattern, GACOS data can also be used for data processing and interpretation to eliminate or reduce the phase change caused by the atmospheric delay in the source area of ​​the Yellow River, reduce the impact of atmospheric delay, and use Kriging interpolation to interpolate and supplement the decoherence in the deformation rate results. At the same time, the deformation rate results are verified in combination with the measured slip data along the transmission tower to verify the reliability of the deformation rate results. GACOS data, namely Generic Atmospheric Correction Online Service for InSAR, is specifically used for atmospheric correction. It can separate the stratification and turbulence signals from the total tropospheric delay based on the tropospheric iterative decomposition model, and then generate a high spatial resolution zenith total delay map, so that atmospheric correction can be performed for applications such as InSAR measurement.

[0055] Step 206, calculate the pore water pressure of the target area within the preset evaluation period, calculate the slope sliding speed of the target area at different times within the preset evaluation period based on the pore water pressure and the landslide dynamic equilibrium differential equation, reclassify the slope sliding speed at different times based on the natural discontinuity method, and obtain the slope collapse rate of the target area at different times within the preset evaluation period, wherein the slope collapse rate includes extremely fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and extremely slow collapse.

[0056] Step 207, obtaining the settlement driving factor data of the target area within a preset evaluation period, and predicting the probability of landslide occurring in the target area within the preset evaluation period based on the deformation data, the settlement driving factor data and a preset landslide probability prediction model, to obtain a landslide probability prediction result, wherein the settlement driving factor data includes data of multiple preset settlement driving factors.

[0057] Next, the pore water pressure of the target area within the preset evaluation period is calculated. Based on the pore water pressure and the landslide dynamic equilibrium differential equation, the slope sliding velocity of the target area at different times within the preset evaluation period is calculated. Based on the natural discontinuity method, the slope sliding velocity at different times is reclassified to obtain the slope collapse rate of the target area at different times within the preset evaluation period. The settlement driving factor data of the target area within the preset evaluation period is obtained. Based on the deformation data, the settlement driving factor data and the preset landslide probability prediction model, the probability of landslide in the target area within the preset evaluation period is predicted to obtain the landslide probability prediction result, in preparation for the subsequent settlement risk assessment.

[0058] Step 208: If the landslide probability prediction result shows that the probability of landslide occurring in the target area belongs to a preset small probability range, then the settlement risk assessment result of the target area is low settlement risk.

[0059] Step 209, if the landslide probability prediction result shows that the probability of landslide in the target area does not fall within the preset small probability range, the settlement risk assessment result of the target area is that there is a settlement risk, and based on the slope collapse rate of the target area at different times within the preset evaluation period, a settlement risk warning information is generated and sent to a preset receiving terminal.

[0060] Since the susceptibility evaluation based on rainfall and dynamics (the calculated slope collapse rate of the target area at different times within the preset evaluation period) is based on the premise that the target is a potential landslide body, and the result of the machine learning susceptibility evaluation (landslide probability prediction result) is the possibility of a landslide occurring in the target, when comprehensively optimizing the two risks, we cannot simply compare the two and take the maximum value. When the probability of a landslide occurring in the target is extremely small (belonging to the preset small probability range), it is meaningless to discuss the possible sliding speed after the landslide occurs. Therefore, when merging, the landslide risk prediction results based on machine learning need to be mainly used. Therefore, it can solve the problem that the current landslide risk assessment method based on machine learning only considers a single static index and cannot obtain time series and dynamic information.

[0061] By applying the technical solution of this embodiment, it is possible to take into account the continuous changes in surface conditions over a period of time, as well as the dependence of the rainfall and dynamics-based method on the information of the potential landslide area, and to obtain more complete and more accurate risk assessment results with a larger buffer range than the traditional method. That is, based on rainfall and dynamics and a variety of machine learning, combined with nine surface settlement driving factors, the terrain change rate spatial simulation can be performed on the transmission line corridor, such as a 10km buffer zone, and by comparing a variety of machine learning methods, the algorithm with the highest regression accuracy is determined. At the same time, the rainfall and dynamics methods are combined to assess the landslide risk. Thereby optimizing the landslide susceptibility results of machine learning and obtaining a more accurate risk assessment.

[0062] Further, as a refinement and extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another transmission line settlement risk assessment method is provided, such as Figure 4 As shown, the method includes:

[0063] Step 301, based on the transmission lines with subsidence risk that need to be evaluated, a target area is determined, and original elevation information of the target area is obtained.

[0064] Step 302, obtaining single-view complex images of the target area at different times within a preset evaluation period, pairing the single-view complex images to obtain a plurality of candidate interference image pairs, and optimizing the candidate interference image pairs to obtain a plurality of target interference image pairs.

[0065] Step 303, for any target interference image pair, an interference fringe pattern is generated based on the target interference image pair, and the interference phase in the interference fringe pattern is converted into elevation information using a phase-elevation conversion method, and the terrain phase is simulated and removed from the interference fringe pattern using the converted elevation information and the original elevation information to obtain a deformation phase pattern, and the deformation phase patterns of each target interference image pair are combined to obtain deformation data of the target area within a preset evaluation period, wherein the interference fringe pattern includes an interference phase, and the interference phase is composed of a terrain phase and a deformation phase.

[0066] In the above embodiment of the present application, based on the transmission line with the risk of settlement that needs to be evaluated, the target area is determined and the original elevation information of the target area is obtained. The single-view complex images of the target area at different times within the preset evaluation period are obtained, and the single-view complex images are paired to obtain multiple candidate interference image pairs, and the candidate interference image pairs are optimized to obtain multiple target interference image pairs. SBAS deformation inversion is performed based on the target interference image pairs to obtain the deformation data of the target area within the preset evaluation period, in preparation for the subsequent landslide probability, that is, the landslide susceptibility evaluation.

[0067] Step 304, based on the pore water pressure calculation formula, the pore water pressure of the target area within the preset evaluation period is calculated, and based on the pore water pressure and the landslide dynamic equilibrium differential equation formula, the slope sliding speed of the target area at different times within the preset evaluation period is calculated, wherein the pore water pressure calculation formula is:

[0068] U=γ w h w ,

[0069] The landslide dynamic equilibrium differential equation formula is:

[0070]

[0071] U is the pore water pressure, γ w is the unit weight of pore water, h w is the height of pore water in the soil, α is the inclination of the sliding surface of the landslide, γ* is the equivalent unit weight of the landslide, H is the thickness of the landslide, μ ss is the steady-state friction coefficient of the sliding surface of the landslide, g is the acceleration of gravity, v is the sliding velocity of the slope relative to the land surface, and t is the time.

[0072] Step 305, reclassifying the slope sliding speed at different times based on the natural discontinuity method, and obtaining the slope collapse rate of the target area at different times within a preset evaluation period, wherein the slope collapse rate includes extremely fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and extremely slow collapse.

[0073] Then, if Figure 5 As shown in the figure, the time series rainfall data covering the transmission line is obtained, and the pore water pressure of the transmission line slope is calculated in combination with the soil moisture content of the transmission line slope. For example, the daily precipitation in the target area in a year can be obtained as a basis to calculate the pore water pressure within the target area, and provide the pre-data for the steady-state equation. Then, a rainfall intrusion model (dynamic equilibrium differential equation of landslide) is established. The slope sliding velocity at different times is derived through the dynamic equilibrium differential equation of landslide, that is, the final sliding velocity of the landslide body after the stress disturbance can be calculated according to the differential equation. In particular, the sliding velocity can also be predicted based on the landslide sliding rate prediction model (established based on the random Senli model), that is, after determining the dynamic parameters of the landslide, the landslide sliding rate prediction model is used to estimate the landslide sliding rate. Finally, the collapse rate of the transmission line slope is reclassified to obtain the five categories of extremely fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and extremely slow collapse.

[0074] Step 306: Based on the historical settlement driving factor data and historical deformation data of the target area, a random forest model, a support vector machine model and an extreme gradient boosting model are trained respectively, and the model with the highest prediction accuracy is used as the landslide probability prediction model.

[0075] Preset surface subsidence driving factors such as Figure 6 As shown in the figure, including slope, lithology, profile curvature, plane curvature, distance from roads, vegetation coverage, intensity of human activities, distance from rivers and precipitation (amount), Figure 6 In the process, based on the historical settlement driving factor data and historical deformation data of the target area, the random forest model (RF, RandomForest), support vector machine model (SVM, Support VectorMachine) and extreme gradient boosting model (XGBoost, eXtremeGradientBoosting), or back propagation neural network (BPNN, BackpropagationNeuralNetwork) are trained respectively, and the model with the highest prediction accuracy is used as the landslide probability prediction model. Combined with the machine learning model selection, the landslide hazard evaluation index (slope collapse rate) is output, and the evaluation index is reclassified into extremely low risk, relatively low risk, moderate risk, relatively high risk and relatively high risk, corresponding to extremely slow collapse, relatively slow collapse, moderate collapse, relatively fast collapse and extremely fast collapse respectively.

[0076] Specifically, before training the three models, collinearity analysis can be performed on the data of the nine surface settlement driving factors and the deformation data to ensure that there is no multicollinearity between the factors during modeling. After the collinearity analysis, 70% of the data is used as the training set to train the random forest, support vector machine, and extreme gradient boosting models and simulate the landslide susceptibility risk of the target area, and the other 30% of the data is used as the test set to test the accuracy of the model prediction. During the training process, the parameters of the three models are optimized, and the ROC (Receiver Operating Characteristic curve) accuracy curves of the three models are calculated based on the training results of the models under the optimal parameter conditions. The model with the highest accuracy is selected and reclassified according to the results of its landslide susceptibility evaluation to obtain the landslide risk prediction results based on machine learning. In particular, when using the data of the nine surface settlement driving factors and the deformation data to train the model, the nine factor data and soil data can be clipped to the same range as the target area to improve the accuracy of model training.

[0077] When performing collinearity analysis, the tolerance (Tolerance, TOL) and variance inflation factor (VarianceInflationFactor, VIF) of 10 factors are calculated. The specific calculation method is as follows:

[0078]

[0079] TOL is the tolerance, VIF is the variance inflation factor, and the tolerance and variance inflation factor are a pair of reciprocals. is the coefficient of determination of the residual factor regression model.

[0080] For the factors of calculated TOL and VIF, if the variance inflation factor value is >5 or the tolerance is <0.2, it proves that there is collinearity between the input factors and some factors need to be eliminated. After elimination, the data of the nine factors are re-obtained and re-analyzed until all factors meet the variance inflation factor value <5 and the tolerance >0.2.

[0081] The process of training a random forest model is as follows: N estimators are extracted from the original data set as training sets using the bootstrap sampling technique. The size of each training set is about two-thirds of the original data set. During the training process, each bootstrap sample of RF will have about one-third of the data not drawn, and this part of the data is called out-of-bag data. Next, a regression tree is created for each training set, forming a total of N regression trees for estimators, and finally forming a "forest", but these regression trees are not "pruned". During the growth of each tree, not all optimal attributes are selected as internal nodes of the branch, but the optimal attributes are selected from the randomly selected maximum depth (Maxdepth) attributes for branching. Therefore, the random algorithm increases the differences between regression models by constructing different training sets, thereby improving the extrapolation prediction ability of the combined regression model.

[0082] After n times of model training, we get the regression model sequence {t 1 (x), t 2 (x), t 3 (x),…,t k (x)}, used to form a multivariate regression model system. Then collect the prediction results of the regression trees of N estimators, and use a simple average strategy to calculate the value of the new sample. The final regression decision formula is:

[0083]

[0084] represents the combined regression model, t i is a single decision tree regression model and K is the number of regression trees (N estimators).

[0085] The process of training the support vector machine model is as follows: First, define a set of data points P = (x i ,a i ), i = 1, ... n, where x i Input vector for data point i, a i is the actual value, and n is the number of data points. For a linear function f, the hyperplane constructed by SVR (Support Vector Regression) is determined as: f(x) = wx + b, where the predicted value f(x) depends on the slope w and the intercept b.

[0086] In general, one wants to strike a balance between learning the relationship between input and output while maintaining good generalization behavior. Excessive focus on minimizing training error may lead to overfitting. Models with low complexity are restricted in terms of decision boundaries, but are less likely to overfit. In Cortes and Vapnik (the two providers of Support Vector Machines, SVM for short) it was shown that the probability of test error depends on two factors, the frequency of training errors and the confidence interval, where these two factors form a trade-off. The confidence interval is related to the dimensionality of the SVM (Support Vector Machine model), which can be thought of as the complexity of the learning model. Therefore, improved generalization can be obtained by improving the confidence interval at the cost of additional training errors. In the following formula, R represents the compound risk caused by training errors and model complexity. Of course, the risk R needs to be kept as low as possible.

[0087]

[0088] The formula produces estimates of w and b and consists of two main parts:

[0089] Part I It consists of training risk or empirical risk and contains the loss function, L ∈ This function (a,y) means that if the difference between the predicted value f(x) and the actual value a is less than ∈, the prediction error will be ignored. The loss function is formally defined as follows:

[0090]

[0091] The second part of the formula 1 / 2W 2 is a regularization term, which is related to the complexity of the model.

[0092] C controls the trade-off between the regularization term and training accuracy. Larger values ​​of C mean that more weight is placed on correctly predicting training points, at the expense of higher generalization error. The problem of finding the optimal hyperplane is a convex optimization problem. For nonlinear relationships between input vectors and outputs, it is necessary to define a mapping The training point x i Convert to high-dimensional feature space. Calculate Φ(x i ) and Φ(x). Function Φ(x i )Φ(x) is usually defined as K(x i ,x), and is called a kernel function, which attempts to achieve linear separability between training points in a high-dimensional feature space.

[0093] The process of training the XGBoost model is as follows:

[0094] The output of XGBoost can be expressed as the sum of the predictions of all trees f k (x i ):

[0095]

[0096] Where Γ is the space of regression trees, K is the number of regression trees, and x i Represents the feature corresponding to sample i.

[0097] The process of improving the machine learning algorithm needs to continue until the objective function is reduced to a certain limit. In order to approximate the set of functions used in the model, the function set is defined as follows:

[0098]

[0099] Where n is the data sample, It is the training loss function, which is used to describe how well the model fits the training data.

[0100]

[0101] is a regularization term used to penalize model complexity. In the regularization term, γ is the complexity cost of introducing additional leaves, λ is a regularization hyperparameter, is the L2 norm of the weight of leaf node j.

[0102] In additive learning, all trees are built sequentially, and each newly added tree learns from the previous trees and updates the residuals in the predictions. has already contained the iteration results of all trees. Therefore, for the kth iteration, Can be expressed Objective function Φ (k) is written as:

[0103] To effectively optimize the general setting objective of the first loss training function, a second-order Taylor expansion is used to approximate it:

[0104]

[0105] Where: and are the first-order gradient statistics and second-order gradient statistics of the loss function respectively. In step k, the constant term can be removed to obtain the following approximate target:

[0106]

[0107] Here, a vector of leaf scores is used to define the tree, and a leaf index mapping function is used to map an instance to a leaf j. This process can be expressed as:

[0108]

[0109] Given a fixed tree structure, the optimal leaf weight on each leaf node is solved by simple quadratic programming as well as The extreme value of:

[0110]

[0111] The mean absolute error (MAE), root mean square error (RMSE) and determination coefficient (R2) of the three methods were calculated, and based on this, the accuracy of the three algorithms was compared to determine the best algorithm. According to the characteristics of the three models, several sets of parameter values ​​were preset for the three models in combination with the characteristics of the study area. For machine learning models, there are often more than one adjustable parameter, which requires permutations and combinations, and all parameter combinations should be tested as comprehensively as possible to find the optimal parameter settings.

[0112] The training set is used to train machine learning models based on various parameter groups. The evaluation accuracy of each model is verified using the data of the validation set, and the optimal parameter combination of the three models is selected.

[0113] Subsequently, the data of the training set were placed into three machine learning models based on the optimal parameter combination for modeling. The data of the training set was used for modeling, and then the data of the validation set was input into the model. The output data was used to calculate the ROC accuracy curve and AUC of the three models. The AUC (Area Under Curve) indicator is a super important indicator for evaluating the performance of the binary classification model.

[0114] ROC curve is a common tool for evaluating the performance of classification models and is widely used in landslide susceptibility assessment. It shows the performance of the model under different thresholds by plotting the relationship between the true positive rate (TruePositiveRate, TPR) and the false positive rate (FalsePositiveRate, FPR). The horizontal axis is generally FPR and the vertical axis is TPR. The calculation formula is as follows:

[0115] FPR=FP / (FP+TN), TPR=TP / (TP+FN),

[0116] In the formula, FP stands for FalsePositives, TP stands for TruePositives, FN stands for FalseNegatives, and TN stands for TrueNegatives. For the ROC curve, the closer it is to the upper left corner, the better the classification ability of the model. The closer it is to the diagonal, the model has no classification ability and its classification performance is equivalent to random guessing.

[0117] The significance of the AUC indicator is to quantify the performance of the ROC curve. The value range of AUC is between 0 and 1. The closer to 1, the better the performance of the classifier. Specifically, the size of the AUC value can be used to evaluate the classification ability of the classifier: when AUC is equal to 0.5, it means that the performance of the classifier is equivalent to random guessing; when AUC is greater than 0.5, the performance of the classifier is better than random guessing;

[0118] Based on the AUC index, the model with the highest evaluation accuracy (the model with the highest AUC value) was selected as the optimal machine learning model. The landslide susceptibility of the entire study area was evaluated. The evaluation results were reclassified based on the natural break method to obtain the landslide risk prediction results based on machine learning.

[0119] Step 307, obtaining the settlement driving factor data of the target area within a preset evaluation period, and predicting the probability of landslide occurring in the target area within the preset evaluation period based on the deformation data, the settlement driving factor data and a preset landslide probability prediction model, to obtain a landslide probability prediction result, wherein the settlement driving factor data includes data of multiple preset settlement driving factors, and the preset surface settlement driving factors include slope, lithology, profile curvature, plane curvature, distance from road, vegetation coverage, human activity intensity, distance from river and precipitation.

[0120] Step 308, combining the landslide probability prediction results of the target area within the preset evaluation period and the slope collapse rates at different times, to obtain the settlement risk assessment results of the target area within the preset evaluation period.

[0121] Finally, the settlement driving factor data of the target area within the preset evaluation period is obtained. Based on the deformation data, the settlement driving factor data and the preset landslide probability prediction model, the probability of landslide in the target area within the preset evaluation period is predicted to obtain the landslide probability prediction result. The landslide probability prediction result of the target area within the preset evaluation period and the slope collapse rate at different times are combined to obtain the settlement risk assessment result of the target area within the preset evaluation period.

[0122] By applying the technical solution of this embodiment, the pore water pressure is calculated by considering rainfall and dynamic processes, and then the slope sliding speed in the target area at different times is calculated. Based on the creeping landslide collapse model driven by rainfall, the dynamic equilibrium differential equation of the landslide is constructed. The equation and the slope sliding speed are used to predict the time that may be required for the potential landslide in the target area to develop from slow creeping to collapse, and the slope collapse rate is obtained. The deformation data is obtained based on the interferometric synthetic aperture radar deformation method, and the deformation data is used as one of the landslide driving factors. Together with other driving factors, it is input into three machine learning methods to obtain the landslide susceptibility, that is, the landslide probability. The landslide risk along the transmission tower is evaluated by combining the landslide probability with the slope collapse rate, which improves the interpretability and accuracy of the landslide risk assessment and provides strong support for the landslide disaster prevention and post-disaster maintenance of the transmission line.

[0123] It should be noted that for other corresponding descriptions of the functional units involved in the transmission line settlement risk assessment device provided in the embodiment of the present application, reference can be made to Figure 1 , Figure 2 and Figure 4 The corresponding description in the method will not be repeated here.

[0124] Based on the above Figure 1 , Figure 2 and Figure 4 The method shown in the embodiment of the present application is accordingly provided with a storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Figure 1 , Figure 2 and Figure 4 The transmission line settlement risk assessment method shown in Figure 2 is shown in Figure 2.

[0125] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0126] Based on the above Figure 1 , Figure 2 and Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the present application also provides a computer device, which can be a personal computer, a server, a network device, etc. The computer device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 , Figure 2 and Figure 4 The transmission line settlement risk assessment method shown in Figure 2 is shown in Figure 2.

[0127] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0128] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not limit the computer device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0129] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages and saves the hardware and software resources of the computer device, and supports the operation of information processing programs and other software and / or programs. The network communication module is used to realize communication between the components inside the storage medium, and communication with other hardware and software in the physical device.

[0130] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware platforms, or by hardware implementation to calculate the pore water pressure by considering rainfall and dynamic processes, and then calculate the slope sliding speed at different times in the target area, and construct a landslide dynamic equilibrium differential equation based on the rainfall-driven creeping landslide collapse model, and use the equation and slope sliding speed to predict the time that may be required for the potential landslide in the target area to develop from slow creeping to collapse, and obtain the slope collapse rate, and obtain deformation data based on the interferometric synthetic aperture radar deformation method, and use the deformation data as one of the landslide driving factors, and input it into three machine learning methods together with other driving factors to obtain the landslide susceptibility, that is, the landslide probability. The landslide risk along the transmission tower is evaluated by combining the landslide probability with the slope collapse rate, which improves the interpretability and accuracy of the landslide risk assessment and provides strong support for the landslide disaster prevention and post-disaster maintenance of the transmission line.

[0131] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0132] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A method for assessing the risk of transmission line settlement, characterized in that: The method comprises: Based on the needs of evaluating the transmission lines with settlement risks, determining the target area, and obtaining the original elevation information of the target area; Acquire single-view complex images of the target area at different times within a preset evaluation period, pair the single-view complex images to obtain a plurality of candidate interference image pairs, and optimize the candidate interference image pairs to obtain a plurality of target interference image pairs; For any target interference image pair, an interference fringe pattern is generated based on the target interference image pair, and the interference phase in the interference fringe pattern is converted into elevation information by using a phase-elevation conversion method. The terrain phase is simulated and removed in the interference fringe pattern by using the converted elevation information and the original elevation information to obtain a deformation phase pattern, and the deformation phase patterns of each target interference image pair are combined to obtain deformation data of the target area within a preset evaluation period, wherein the interference fringe pattern includes an interference phase, and the interference phase is composed of a terrain phase and a deformation phase; Calculate the pore water pressure of the target area within a preset evaluation period, calculate the slope sliding speed of the target area at different times within the preset evaluation period based on the pore water pressure and the landslide dynamic equilibrium differential equation, reclassify the slope sliding speed at different times based on the natural discontinuity method, and obtain the slope collapse rate of the target area at different times within the preset evaluation period, wherein the slope collapse rate includes extremely fast collapse, relatively fast collapse, medium-speed collapse, relatively slow collapse and extremely slow collapse; Acquire settlement driving factor data of the target area within a preset evaluation period, and predict the probability of landslide in the target area within the preset evaluation period based on the deformation data, the settlement driving factor data and a preset landslide probability prediction model to obtain a landslide probability prediction result, wherein the settlement driving factor data includes data of multiple preset settlement driving factors; The landslide probability prediction results of the target area within the preset evaluation period and the slope collapse rates at different times are combined to obtain the settlement risk assessment results of the target area within the preset evaluation period.

2. The method according to claim 1, characterized in that The landslide probability prediction results of the target area within the preset evaluation period and the slope collapse rate at different times are integrated to obtain the settlement risk assessment results of the target area within the preset evaluation period, including: If the landslide probability prediction result shows that the probability of landslide in the target area belongs to the preset small probability range, the settlement risk assessment result of the target area is low settlement risk; If the landslide probability prediction result shows that the probability of landslide in the target area does not fall within the preset small probability range, the settlement risk assessment result of the target area is that there is a settlement risk, and based on the slope collapse rate of the target area at different times within the preset evaluation period, settlement risk warning information is generated and sent to the preset receiving terminal.

3. The method according to claim 1, characterized in that Before predicting the probability of landslide in the target area within a preset evaluation period based on the deformation data, the settlement driving factor data and the preset landslide probability prediction model, the method further includes: Based on the historical settlement driving factor data and historical deformation data of the target area, a random forest model, a support vector machine model and an extreme gradient boosting model are trained respectively, and the model with the highest prediction accuracy is used as the landslide probability prediction model.

4. The method according to claim 1, characterized in that: The step of optimizing the candidate interference image pairs to obtain a plurality of target interference image pairs includes: For any candidate interference image pair, the similarity between two single-view complex images in the candidate interference image pair is calculated to obtain a coherence coefficient map, and the two single-view complex images in the candidate interference image pair are conjugate multiplied to obtain an interference fringe map, and the interference fringe map is phase unwrapped using a minimum cost flow algorithm of a Delaunay triangulation network to obtain an unwrapped map; Among multiple candidate interference image pairs, candidate interference image pairs corresponding to coherence coefficient maps with low correlation, candidate interference image pairs corresponding to interference fringe maps with low interference quality, and candidate interference image pairs corresponding to disentanglement maps with low disentanglement quality are removed to obtain multiple target interference image pairs.

5. The method according to claim 1, characterized in that The pairing of the single-view complex images to obtain a plurality of candidate interference image pairs includes: Two single-view complex images acquired with a time interval not exceeding a preset time and a spatial distance not exceeding a preset distance are determined as a candidate interference image pair, until a plurality of candidate interference image pairs are determined.

6. The method according to claim 1, characterized in that The transmission line with subsidence risk based on demand assessment determines the target area, including: The transmission lines for which the risk of subsidence needs to be evaluated are determined, and a target area is obtained by expanding the transmission lines for which the risk of subsidence needs to be evaluated to a preset width.

7. The method according to claim 1, characterized in that The step of calculating the pore water pressure of the target area within a preset evaluation period and calculating the slope sliding speed of the target area at different times within the preset evaluation period based on the pore water pressure and the landslide dynamic equilibrium differential equation includes: The pore water pressure of the target area within the preset evaluation period is calculated based on the pore water pressure calculation formula, and the slope sliding speed of the target area at different times within the preset evaluation period is calculated based on the pore water pressure and the landslide dynamic equilibrium differential equation formula, wherein the pore water pressure calculation formula is: U=γ w h w , The landslide dynamic equilibrium differential equation formula is: U is the pore water pressure, γ w is the unit weight of pore water, h w is the height of pore water in the soil, α is the inclination of the sliding surface of the landslide, γ* is the equivalent unit weight of the landslide, H is the thickness of the landslide, μ ss is the steady-state friction coefficient of the sliding surface of the landslide, g is the acceleration of gravity, v is the sliding velocity of the slope relative to the land surface, and t is the time.

8. The method according to claim 1, characterized in that The preset surface subsidence driving factors include slope, lithology, profile curvature, plane curvature, distance from roads, vegetation coverage, intensity of human activities, distance from rivers and precipitation.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the risk of transmission line subsidence as described in any one of claims 1 to 8 is implemented.

10. A computer device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method for assessing the risk of transmission line subsidence as described in any one of claims 1 to 8 is implemented.

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