Risk assessment method for electric tower in easily landslide area based on digital elevation model
By constructing a slope stability and risk assessment model based on a digital elevation model, the problems of accuracy and adaptability in the risk assessment of power poles in landslide-prone areas were solved, achieving precise and dynamic adaptability in pole risk assessment.
Patent Information
- Application Number
- CN202511869726.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-11
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies for risk assessment of power poles in landslide-prone areas suffer from insufficient deep integration of high-resolution DEMs and physical models, lack of modeling of the 'two-way interaction' between poles and slopes, and weak dynamic scenario simulation capabilities, resulting in low assessment accuracy and poor adaptability.
By constructing a slope stability evaluation model and a power pole risk assessment model based on a digital elevation model, and combining multi-source data, including digital elevation model, geological data, meteorological data and pole data, the target area is divided into multiple sub-regions. A risk assessment model is constructed, risk value ranges and levels are set, and the pole risk value is calculated using slope stability data and meteorological data.
It has improved the accuracy of tower risk assessment to the tower point level, significantly reduced the probability of local high-risk points being smoothed out, and can more accurately reflect the evolution law of slope instability under extreme rainfall and other conditions, providing efficient risk identification and assessment.
Smart Images
Figure CN121706378A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a risk assessment method for power poles in landslide-prone areas based on digital elevation models, belonging to the field of geographic monitoring technology. Background Technology
[0002] Landslide-prone areas, also known as landslide-prone zones, refer to natural or man-made slopes that are prone to landslides due to the combined effects of topography, geology, climate, and human activities. A landslide is a geological phenomenon in which soil and rock masses move downwards and outwards along one or more sliding surfaces under the influence of gravity. Four key conditions contribute to the formation of landslide-prone areas: 1. Slope. Slopes with a gradient between 15° and 45° are high-risk areas for landslides.
[0003] 2. Strata. Commonly slippery strata include (1) loose deposits, such as residual soil, colluvial deposits, and fill. (2) weak rock strata, such as shale, mudstone, and tuff. (3) the contact surface between soft soil and hard bedrock.
[0004] 3. Water. Water can undermine slope stability in several ways. (1) Increase weight. Rainwater seeps into the soil, increasing the weight of the slope's rock and soil mass. (2) Lubrication. Water lubricates potential sliding surfaces, greatly reducing frictional resistance. (3) Erosion. River erosion and ocean waves erode the slope toe, weakening the slope's support.
[0005] 4. Human activities. For example, improper slope cutting: excavation at the foot of a slope during road or house construction destroys the slope's support. Overloading: piling up waste soil or heavy materials at the top of a slope increases the sliding force. Destroying vegetation: tree roots help stabilize the soil; deforestation removes the slope's natural protection. Agricultural irrigation / pipeline leakage: large amounts of water seep into the ground, softening the soil.
[0006] Before constructing infrastructure such as power poles, roads, and houses in mountainous areas, it is crucial to accurately identify and assess the risks of landslide-prone areas.
[0007] The current development of risk assessment technology for power poles in landslide-prone areas exhibits three main characteristics: 1. Enhanced multi-source data fusion. Integrating DEM topographic data, real-time monitoring data (deformation, hydrology, meteorology), and tower structural parameters to replace the traditional reliance on single historical data.
[0008] 2. Combining physical mechanisms with intelligent algorithms. A hydro-mechanical coupling model is used to reveal the nature of landslides, supplemented by machine learning to optimize assessment accuracy and efficiency.
[0009] 3. Full lifecycle adaptation. Covering the entire process from site selection and planning to in-operation monitoring and disaster management, breaking through the limitations of traditional technologies' "single-point application".
[0010] However, existing technologies still have three major gaps. First, the deep integration of high-resolution DEMs and physical models is insufficient, resulting in low accuracy of local point assessments. Second, the modeling of the "two-way interaction" between towers and slopes is lacking, making it difficult to reflect the impact of engineering disturbances. Third, the dynamic scenario simulation capability is weak and cannot adapt to the trend of frequent extreme weather events. Summary of the Invention
[0011] To address the problems existing in the prior art, this invention proposes a risk assessment method for power poles in landslide-prone areas based on digital elevation models.
[0012] The technical solution of the present invention is as follows: On the one hand, this invention provides a risk assessment method for power poles in landslide-prone areas based on digital elevation models, comprising the following steps: Collect digital elevation model data, geological data, meteorological data, pole data, and historical landslide location data of the target area and preprocess them; Divide the target area into multiple sub-areas, and ensure that each sub-area includes one power pole; A slope stability evaluation model was constructed, and the slope stability of each sub-region was evaluated based on the digital elevation model data, geological data, and tower data corresponding to each sub-region. A risk assessment model for power poles was constructed, and the risk value of the corresponding power poles in each sub-region was calculated based on slope stability data, meteorological data, pole data, and historical landslide location data. Multiple different risk value ranges are set, and each risk value range corresponds to a risk level. The corresponding risk level is obtained based on the range in which the risk value of the power pole in the current sub-region is located.
[0013] Preferably, the digital elevation model data for the region includes slope, aspect, slope length, curvature, and confluence accumulation.
[0014] Preferably, the digital elevation model data is scaled and normalized, and the digital elevation model data is compared with the actual GPS control points to calculate the standard deviation of the error and correct the error. Geological data, meteorological data, tower data, and historical landslide location data are standardized and outliers are removed. At the same time, missing data are supplemented using the Kriging interpolation algorithm.
[0015] Preferably, the target area is divided into multiple sub-regions using a slope unit partitioning method.
[0016] Preferably, the slope stability evaluation model is as follows:
[0017] in: This represents the slope stability coefficient of the sub-region. Indicates the natural unit weight of the soil in the sub-region; Indicates the effective slope height of the sub-region; Indicates the slope of the sub-region; Indicates the effective internal friction angle of the sub-region; Indicates the soil cohesion in the sub-region; Indicates the length of the sliding surface in the sub-region; This indicates the total load of power poles and towers in the sub-region; This represents the load-bearing area of the power pole base in the sub-region.
[0018] Preferably, a risk assessment model for power poles is constructed, as shown in the following formula:
[0019] in: This indicates the risk value of power poles in the sub-region; Indicates the rainfall intensity in the sub-region; Indicates the rainfall intensity threshold; Indicates the ultimate bearing capacity of the slope in the sub-region; Indicates the weight of the stability factor; Indicates the weight of the rainfall factor; This indicates the load factor weight.
[0020] On the other hand, the present invention provides a risk assessment system for power poles in landslide-prone areas based on a digital elevation model, including a data acquisition module, a partitioning module, a stability assessment module, a risk assessment module, and a risk classification module. The data acquisition module is used to collect digital elevation model data, geological data, meteorological data, pole data, and historical landslide location data of the target area and perform preprocessing. The partitioning module is used to divide the target area into multiple sub-regions and ensure that each sub-region includes a power pole. The stability assessment module is used to construct a slope stability evaluation model and assess the slope stability of each sub-region based on the digital elevation model data, geological data, and tower data corresponding to each sub-region. The risk assessment module is used to construct a risk assessment model for power poles and towers, and calculates the risk value of the corresponding power poles and towers in each sub-region based on slope stability data, meteorological data, pole and tower data and historical landslide location data. The risk grading module is used to set multiple different risk value ranges, and each risk value range corresponds to a risk level. The corresponding risk level is obtained based on the range of risk values of the power poles in the current sub-region.
[0021] In another aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the present invention.
[0022] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the present invention.
[0023] The present invention has the following beneficial effects: 1. Based on DEM data, this invention divides the target area into several homogeneous slope units and ensures that each tower location corresponds to an independent evaluation unit. This avoids the scale degradation problems caused by traditional evaluations based on large grids or administrative units, such as "multiple towers in one unit" or "towers located exactly at the unit boundary." This improves the accuracy of landslide risk identification from the regional level to the tower location level, significantly reducing the probability of local high-risk points being smoothed out.
[0024] 2. This invention constructs a slope stability evaluation model and obtains the safety factor variation curve of each slope unit over time. Compared with statistical models based solely on empirical factor weights, this model can more accurately reflect the slope instability evolution under conditions such as extreme rainfall and long-duration rainfall. Attached Figure Description
[0025] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation
[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0027] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.
[0028] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0029] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.
[0030] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.
[0031] See Figure 1 In some embodiments, a risk assessment method for power poles in landslide-prone areas based on digital elevation models is proposed, including the following steps: Collect digital elevation model data, geological data, meteorological data, pole data, and historical landslide location data of the target area and preprocess them; Divide the target area into multiple sub-areas, and ensure that each sub-area includes one power pole; A slope stability evaluation model was constructed, and the slope stability of each sub-region was evaluated based on the digital elevation model data, geological data, and tower data corresponding to each sub-region. A risk assessment model for power poles was constructed, and the risk value of the corresponding power poles in each sub-region was calculated based on slope stability data, meteorological data, pole data, and historical landslide location data. Multiple different risk value ranges are set, and each risk value range corresponds to a risk level. The corresponding risk level is obtained based on the range in which the risk value of the power pole in the current sub-region is located.
[0032] In some embodiments, the digital elevation model data for the region includes slope, aspect, slope length, curvature, and confluence accumulation.
[0033] In one specific embodiment, the resolution of the acquired digital elevation model data is greater than or equal to 5m.
[0034] In one specific embodiment, the geological data includes soil type, lithological distribution, soil cohesion, internal friction angle, etc.; the meteorological data includes rainfall time series data for the past 10 years, extreme rainfall thresholds, etc.; and the tower data includes tower structure, tower base weight, foundation depth, load distribution, etc.
[0035] In some embodiments, the digital elevation model data is scaled and normalized, and the digital elevation model data is compared with the actual GPS control points to calculate the standard deviation of the error and correct the error. Geological data, meteorological data, tower data, and historical landslide location data are standardized and outliers are removed. At the same time, missing data are supplemented using the Kriging interpolation algorithm.
[0036] In some embodiments, the target region is divided into multiple sub-regions using a slope unit partitioning method.
[0037] In one specific embodiment, the slope surface is initially divided into multiple initial sub-regions based on the water system and watershed using a slope unit partitioning method. For each initial sub-region, the slope of all raster cells within it is statistically analyzed using circular methods to obtain the circular standard deviation of each initial sub-region. Set the circular standard deviation threshold to 0.8 rad. If the circular standard deviation of the initial sub-region is greater than 0.8 rad, it indicates that the slope aspect is particularly dispersed and the slope aspect is not uniform within the slope. Then, further subdivide the region (using "region growth + slope aspect difference threshold" or "subdivision along the watershed", etc.) to form multiple sub-regions. Then, repeat the circular standard deviation calculation for each sub-region until the circular standard deviation of all sub-regions is greater than the circular standard deviation threshold. Set a minimum planar area threshold. For each sub-region obtained in the previous step, calculate the planar area. If it is greater than the minimum planar area threshold, retain the sub-region. If it is less than the minimum planar area threshold, obtain the adjacent sub-regions that border the sub-region and construct a set of candidate sub-regions. For each candidate sub-region, merge it with the current sub-region and calculate the circular standard deviation. Finally, select the candidate sub-region with the smallest circular standard deviation that is still less than 0.8 rad for merging. If all candidate sub-regions are greater than 0.8 rad after merging, relax the rule: select the candidate sub-region with the smallest increase for merging. After completing the above slope unit, overlay the tower location layer: for each tower location, use the point landing area to obtain the slope unit in which it is located. If a slope unit contains multiple tower locations, you can perform Voronoi / Thiessen polygon segmentation on these towers within the unit, and then trim them with the slope unit to obtain multiple sub-units, so that each sub-unit contains only one tower.
[0038] In some embodiments, the slope stability evaluation model is specifically shown in the following formula:
[0039] in: This represents the slope stability coefficient of the sub-region. This indicates the natural unit weight of the soil in the sub-region, derived from geological data. This represents the effective slope height (soil thickness above the sliding surface) of a sub-region, derived from DEM data. The slope of the sub-region (angle between the sliding surface and the horizontal plane) is represented by DEM data. Indicates the effective internal friction angle of the sub-region; The soil cohesion (interparticle bonding, a component of anti-sliding force) in the sub-region is represented by geological data. This represents the length of the sliding surface in the sub-region (the horizontal projection length of the sliding surface per unit width of the slope). ); The total load of power poles in the sub-region (structural self-weight + conductor tension + wind load, including safety factor) is derived from pole data; This represents the stress-bearing area of the power pole base in the sub-region (the contact area between the bottom of the foundation and the soil), which is derived from pole data.
[0040] In one specific embodiment, three scenario modes (normal rainfall, extreme rainfall, and continuous rainfall) are set, and the slope stability evaluation model is input to calculate the time-series variation curve of the slope stability coefficient for each sub-region.
[0041] In one specific embodiment, model parameters are calibrated using historical landslide data, and the weighting coefficients of cohesion and internal friction angle are adjusted.
[0042] In some embodiments, a risk assessment model for power poles is constructed, as shown in the following formula:
[0043] in: This indicates the risk value of power poles in the sub-region; This indicates the rainfall intensity in a sub-region, representing the rainfall collected per unit time by the monitoring station; This represents the rainfall intensity threshold. Once this threshold is exceeded, the probability of landslides increases sharply, and it is the core criterion for classifying rainfall scenarios. This represents the ultimate bearing capacity of the slope in a sub-region, which is the maximum pressure per unit area that the slope can withstand, as determined by geotechnical tests (such as load tests). The stability factor weight reflects the degree to which the slope's own stability contributes to the total risk. It can be adjusted according to the actual situation. For example, the weight increases during normal rainfall (the risk is mainly based on the background) and decreases during extreme rainfall (the risk is mainly based on rainfall). The weight of the rainfall factor reflects the contribution of rainfall-induced effects to the total risk and changes dynamically with rainfall intensity. This indicates the load factor weight, reflecting the degree of contribution of the engineering load to the total risk. The weight needs to be increased in steep slope areas. The slope is divided into gentle slopes (slope <30°) and steep slopes (slope ≥30°), and the load factor weight in steep slope areas should be increased.
[0044] In one specific embodiment, the risk level is divided into 5 levels: extremely low risk (I<0.2), low risk (0.2≤I<0.4), medium risk (0.4≤I<0.6), high risk (0.6≤I<0.8), and extremely high risk (I≥0.8).
[0045] In a specific embodiment, the output I is validated using two metrics. First, the AUC value (area under the ROC curve) is calculated, requiring ≥0.80; second, the false positive rate of the extremely high-risk area (I≥0.80) is calculated, requiring ≤10% (false positive rate = number of false positive points / total number of points in the extremely high-risk area).
[0046] In one specific embodiment, a risk distribution map of pole and tower landslides in the target area is generated, marking the risk level, slope stability and critical instability rainfall threshold of each pole and tower location; Output an assessment report, including a list of high-risk towers, reinforcement recommendations (such as increasing burial depth and adding anti-slide piles), and site selection optimization schemes.
[0047] In one specific implementation, a quarterly update mechanism is established to automatically add or update ① historical disaster data: adding landslide locations of transmission lines and corresponding factor parameters (such as the time of landslide). , ); ② Monitoring data: Update regional rainfall thresholds Slope bearing capacity ③ Engineering data: newly added tower load parameters and reinforcement treatment feedback results; Establish a weight iterative optimization mechanism, incorporate the updated data into the model training set, recalculate the factor weights, and verify the optimization effect through the AUC value. It can be required that the AUC value after the iteration is one value higher than that before the optimization; otherwise, it should revert to the previous weight. Establish an accuracy decay early warning system and monitor the AUC value change trend in real time. If the decline is too large for two consecutive quarters, an emergency iteration should be triggered to shorten the update cycle to monthly.
[0048] In some embodiments, a risk assessment system for power poles in landslide-prone areas based on a digital elevation model is proposed, including a data acquisition module, a zoning module, a stability assessment module, a risk assessment module, and a risk classification module; The data acquisition module is used to collect digital elevation model data, geological data, meteorological data, pole data, and historical landslide location data of the target area and perform preprocessing. The partitioning module is used to divide the target area into multiple sub-regions and ensure that each sub-region includes a power pole. The stability assessment module is used to construct a slope stability evaluation model and assess the slope stability of each sub-region based on the digital elevation model data, geological data, and tower data corresponding to each sub-region. The risk assessment module is used to construct a risk assessment model for power poles and towers, and calculates the risk value of the corresponding power poles and towers in each sub-region based on slope stability data, meteorological data, pole and tower data and historical landslide location data. The risk grading module is used to set multiple different risk value ranges, and each risk value range corresponds to a risk level. The corresponding risk level is obtained based on the range of risk values of the power poles in the current sub-region.
[0049] In some embodiments, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method as described in any embodiment of the present invention.
[0050] In some embodiments, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the method as described in any embodiment of the present invention.
[0051] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, A and B simultaneously, or B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.
[0052] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0053] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0054] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0055] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.
Claims
1. A risk assessment method for power poles in landslide-prone areas based on digital elevation models, characterized in that, Includes the following steps: Collect digital elevation model data, geological data, meteorological data, pole data, and historical landslide location data of the target area and preprocess them; Divide the target area into multiple sub-areas, and ensure that each sub-area includes one power pole; A slope stability evaluation model was constructed, and the slope stability of each sub-region was evaluated based on the digital elevation model data, geological data, and tower data corresponding to each sub-region. A risk assessment model for power poles was constructed, and the risk value of the corresponding power poles in each sub-region was calculated based on slope stability data, meteorological data, pole data, and historical landslide location data. Multiple different risk value ranges are set, and each risk value range corresponds to a risk level. The corresponding risk level is obtained based on the range in which the risk value of the power pole in the current sub-region is located.
2. The method for risk assessment of power poles in landslide-prone areas based on digital elevation model according to claim 1, characterized in that, The digital elevation model data for the region includes slope, aspect, slope length, curvature, and cumulative runoff.
3. The method for risk assessment of power poles in landslide-prone areas based on digital elevation model according to claim 1, characterized in that, The digital elevation model data is scaled and normalized. The digital elevation model data is then compared with the actual GPS control points to calculate the standard deviation of the error and correct the error. Geological data, meteorological data, tower data, and historical landslide location data are standardized and outliers are removed. At the same time, missing data are supplemented using the Kriging interpolation algorithm.
4. The method for risk assessment of power poles in landslide-prone areas based on digital elevation model as described in claim 1, characterized in that, The target area is divided into multiple sub-regions using the slope unit partitioning method.
5. The method for risk assessment of power poles in landslide-prone areas based on digital elevation model according to claim 1, characterized in that, The slope stability evaluation model is specifically shown in the following formula: in: This represents the slope stability coefficient of the sub-region. Indicates the natural unit weight of the soil in the sub-region; Indicates the effective slope height of the sub-region; Indicates the slope of the sub-region; Indicates the effective internal friction angle of the sub-region; Indicates the soil cohesion in the sub-region; Indicates the length of the sliding surface in the sub-region; This indicates the total load of power poles and towers in the sub-region; This represents the load-bearing area of the power pole base in the sub-region.
6. The method for risk assessment of power poles in landslide-prone areas based on digital elevation model according to claim 5, characterized in that, A risk assessment model for power poles is constructed, as shown in the following formula: in: This indicates the risk value of power poles in the sub-region; Indicates the rainfall intensity in the sub-region; Indicates the rainfall intensity threshold; Indicates the ultimate bearing capacity of the slope in the sub-region; Indicates the weight of the stability factor; Indicates the weight of the rainfall factor; This indicates the load factor weight.
7. A risk assessment system for power poles in landslide-prone areas based on digital elevation models, characterized in that, It includes a data acquisition module, a partitioning module, a stability assessment module, a risk assessment module, and a risk classification module; The data acquisition module is used to collect digital elevation model data, geological data, meteorological data, pole data, and historical landslide location data of the target area and perform preprocessing. The partitioning module is used to divide the target area into multiple sub-regions and ensure that each sub-region includes a power pole. The stability assessment module is used to construct a slope stability evaluation model and assess the slope stability of each sub-region based on the digital elevation model data, geological data, and tower data corresponding to each sub-region. The risk assessment module is used to construct a risk assessment model for power poles and towers, and calculates the risk value of the corresponding power poles and towers in each sub-region based on slope stability data, meteorological data, pole and tower data and historical landslide location data. The risk grading module is used to set multiple different risk value ranges, and each risk value range corresponds to a risk level. The corresponding risk level is obtained based on the range of risk values of the power poles in the current sub-region.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.