Power transmission line icing high-risk section identification method and device based on typical micro-terrain model

Abstract

The invention relates to the technical field of power system disaster prevention, and particularly provides a typical microtopography model-based power transmission line icing high-risk section identification method and device, and the method comprises the steps: obtaining the characteristic indexes of a digital elevation model of a channel region corresponding to a power transmission line; determining an icing risk score of the power transmission line based on the characteristic index of the digital elevation model of the channel region corresponding to the power transmission line and the characteristic index of each typical micro-terrain model; and identifying the power transmission line with the icing risk score exceeding a preset threshold as a power transmission line icing high-risk section. According to the technical scheme provided by the invention, high-resolution remote sensing topographic data and an automatic identification technology of a typical micro-topographic model are fused, the easily-iced section in the mountainous area power grid line can be rapidly and quantitatively identified, and scientific support is provided for line design optimization, operation and maintenance inspection and icing early warning.

Description

Technical Field

[0001] This invention relates to the field of power system disaster prevention technology, specifically to a method and device for identifying high-risk icing sections of transmission lines based on a typical micro-topography model. Background Technology

[0002] The impact of power line icing disasters on power grid operation and transmission safety is becoming increasingly prominent, especially in complex mountainous areas where severe icing caused by micro-topography occurs frequently, posing a serious threat to transmission line safety. Taking an icing disaster in a certain area as an example, severe icing occurred on conductors in multiple high-altitude sections, with the maximum icing thickness exceeding 40mm, leading to problems such as line tripping and tower bending due to icing, posing significant challenges to regional power supply and winter power security.

[0003] Although some areas have mitigated the problem by installing de-icing devices or adjusting operational tension, the lack of high-resolution topographic information and quantitative identification mechanisms meant that many high-risk icing sections were not effectively avoided in the initial planning and design phases. This resulted in high maintenance costs and reliance on manual experience for icing monitoring. Traditional methods primarily rely on on-site surveys or low-resolution digital elevation models (DEMs) to determine the relationship between route alignment and icing risk, which makes it difficult to achieve regional-level batch identification and suffers from low efficiency and high subjectivity.

[0004] In addition, existing technologies for identifying icing risks on railway lines lack standardized micro-topographic models and scoring mechanisms, making it difficult to uniformly identify high-risk micro-topographic sections in different climate zones and landform types. This results in insufficient basis for engineering decisions and high costs for icing control. Summary of the Invention

[0005] To overcome the above-mentioned shortcomings, this invention proposes a method and device for identifying high-risk sections of transmission lines with icing based on a typical micro-topography model.

[0006] Firstly, a method for identifying high-risk sections of transmission lines icing based on a typical micro-topography model is provided. This method includes:

[0007] Obtain the characteristic indicators of the digital elevation model of the corridor area corresponding to the transmission line;

[0008] The icing risk score of the transmission line is determined based on the feature indicators of the digital elevation model of the corresponding corridor area and the feature indicators of each typical micro-topography model.

[0009] Transmission lines with icing risk scores exceeding a preset threshold are identified as high-risk sections for icing.

[0010] Preferably, the spatial resolution of the digital elevation model is less than or equal to 10m.

[0011] Preferably, the characteristic indicators include at least one of the following: slope aspect, slope, undulation, windward slope index, wind corridor index, valley depth index, and saddle index.

[0012] Furthermore, the characteristic indicators also include at least one of the following: wind direction, 10m wind speed, vertical temperature gradient, near-surface humidity, inversion layer distribution, surface temperature, nighttime cooling zone, water body boundary, near-surface water body distribution area, power system online monitoring data, and fusionable CFD simulation data.

[0013] Furthermore, the slope direction as follows:

[0014]

[0015] The slope θ is as follows:

[0016]

[0017] The undulation R f as follows:

[0018] R f =z max -z min

[0019] The wind slope index W in as follows:

[0020]

[0021] The wind corridor index C wind as follows:

[0022]

[0023] The valley depth index V d as follows:

[0024]

[0025] The saddle index S a as follows:

[0026]

[0027] In the above formula, q is the rate of change of elevation with respect to the east-west direction, p is the rate of change of elevation with respect to the north-south direction, and z max For the highest elevation, z min The lowest elevation, As the prevailing wind direction, d inletd is the cross-sectional width at the air duct inlet. outlet h is the cross-sectional width at the air duct outlet. inlet Because the valley at the air duct inlet is deep, h outlet The valley at the air duct outlet is deep, z ridge For the ridge elevation, z valley The elevation of the lowest point in the valley is d. ridge-valley z is the horizontal distance from the ridge to the valley floor. left The elevation of the mountain body on the left side of the saddle is z. right The elevation of the mountain body on the right side of the saddle is z. saddle This is the elevation of the lowest point of the saddle.

[0028] Preferably, the characteristic indicators of the digital elevation model of the corresponding corridor area of ​​the transmission line include:

[0029] The sliding window algorithm is used to obtain the sliding window section of the digital elevation model of the corresponding corridor area of ​​the transmission line;

[0030] Obtain the feature indicators corresponding to each sliding window segment, wherein the feature indicators corresponding to each sliding window segment are the feature indicators of the digital elevation model of the corresponding channel area.

[0031] Preferably, the typical micro-topography models include: high mountain route icing model, windward route icing model, water condensation icing model, topographic lifting icing model, and canyon wind tunnel icing model.

[0032] Furthermore, the icing model of the high-altitude line meets the following requirements: altitude greater than 2500m, undulation greater than 180m, solar temperature gradient greater than 2℃ / 100m, slope facing north or away from the sun, and the distance between the guide elevation and the snow line is ±150m.

[0033] The icing model for the windward line meets the following requirements: wind slope index greater than 0.7, local slope within the range of 10° to 35°, angle between the line and the wind direction less than 30°, wind speed enhancement ratio greater than 2, and CFD wind pressure simulation value greater than 50Pa.

[0034] The water condensation and icing model meets the following conditions: horizontal distance from the water surface less than 200m, nighttime ground temperature less than 0℃, humidity gradient greater than 90℃ and near-surface saturation, wind speed less than 1.5m / s, and latent heat flux greater than 100W / m². 2 ;

[0035] The topographic uplift and icing model meets the following criteria: uplift height greater than 120m, wind uplift index greater than 0.6, difference between traverse elevation and isotherm less than 200m, local cooling gradient greater than 0.6℃ / 100m, and cloud water content greater than 0.15g / m². 3 ;

[0036] The canyon wind tunnel icing model meets the following requirements: valley floor width-to-height ratio less than 1:3, valley depth index greater than 0.5, wind speed drop at the center of the wind tunnel greater than 1.5 m / s, terrain enclosure degree greater than 75%, and nighttime temperature gradient reversal greater than 0.

[0037] Furthermore, the icing risk score for the transmission line is as follows:

[0038]

[0039] In the above formula, R total To score the icing risk of transmission lines, w i R represents the weight of the i-th typical micro-terrain model. i The similarity between the feature indices of the digital elevation model of the transmission line corridor area and the feature indices of the i-th type of typical micro-topography model.

[0040] Furthermore, for the icing models of high-altitude lines, windward lines, topographic uplift icing models, and canyon wind tunnels, the similarity R between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is... i as follows:

[0041]

[0042] In the above formula, x is the feature index vector of the digital elevation model of the transmission line corridor area, and μ i Let T be the mean vector of feature indices of the i-th typical micro-terrain model, and let T be the transpose. Let be the empirical covariance matrix of the i-th typical micro-topography model;

[0043] For the water condensation and icing model, the similarity between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is as follows:

[0044]

[0045] In the above formula, d is the horizontal distance between the transmission line and the nearest body of water in the digital elevation model of the corresponding channel area, d0 is the preset risk threshold, and δ is the slope adjustment coefficient.

[0046] Secondly, a device for identifying high-risk sections of transmission lines icing based on a typical micro-topography model is provided, the device comprising:

[0047] The acquisition module is used to acquire the feature indicators of the digital elevation model of the corresponding corridor area of ​​the transmission line.

[0048] The determination module is used to determine the icing risk score of the transmission line based on the feature indicators of the digital elevation model of the corresponding corridor area and the feature indicators of each typical micro-topography model;

[0049] The identification module is used to identify transmission lines with icing risk scores exceeding a preset threshold as high-risk sections of transmission lines for icing.

[0050] Preferably, the spatial resolution of the digital elevation model is less than or equal to 10m.

[0051] Preferably, the characteristic indicators include at least one of the following: slope aspect, slope, undulation, windward slope index, wind corridor index, valley depth index, and saddle index.

[0052] Furthermore, the characteristic indicators also include at least one of the following: wind direction, 10m wind speed, vertical temperature gradient, near-surface humidity, inversion layer distribution, surface temperature, nighttime cooling zone, water body boundary, near-surface water body distribution area, power system online monitoring data, and fusionable CFD simulation data.

[0053] Furthermore, the slope direction as follows:

[0054]

[0055] The slope θ is as follows:

[0056]

[0057] The undulation R f as follows:

[0058] R f =z max -z min

[0059] The wind slope index W in as follows:

[0060]

[0061] The wind corridor index C wind as follows:

[0062]

[0063] The valley depth index V d as follows:

[0064]

[0065] The saddle index S a as follows:

[0066]

[0067] In the above formula, q is the rate of change of elevation with respect to the east-west direction, p is the rate of change of elevation with respect to the north-south direction, and z max For the highest elevation, z min The lowest elevation, As the prevailing wind direction, d inlet d is the cross-sectional width at the air duct inlet. outlet h is the cross-sectional width at the air duct outlet. inlet Because the valley at the air duct inlet is deep, h outlet The valley at the air duct outlet is deep, z ridge For the ridge elevation, z valley The elevation of the lowest point in the valley is d. ridge-valley z is the horizontal distance from the ridge to the valley floor. left The elevation of the mountain body on the left side of the saddle is z. right The elevation of the mountain body on the right side of the saddle is z. saddle This is the elevation of the lowest point of the saddle.

[0068] Preferably, the acquisition module is specifically used for:

[0069] The sliding window algorithm is used to obtain the sliding window section of the digital elevation model of the corresponding corridor area of ​​the transmission line;

[0070] Obtain the feature indicators corresponding to each sliding window segment, wherein the feature indicators corresponding to each sliding window segment are the feature indicators of the digital elevation model of the corresponding channel area.

[0071] Preferably, the typical micro-topography models include: high mountain route icing model, windward route icing model, water condensation icing model, topographic lifting icing model, and canyon wind tunnel icing model.

[0072] Furthermore, the icing model of the high-altitude line meets the following requirements: altitude greater than 2500m, undulation greater than 180m, solar temperature gradient greater than 2℃ / 100m, slope facing north or away from the sun, and the distance between the guide elevation and the snow line is ±150m.

[0073] The icing model for the windward line meets the following requirements: wind slope index greater than 0.7, local slope within the range of 10° to 35°, angle between the line and the wind direction less than 30°, wind speed enhancement ratio greater than 2, and CFD wind pressure simulation value greater than 50Pa.

[0074] The water condensation and icing model meets the following conditions: horizontal distance from the water surface less than 200m, nighttime ground temperature less than 0℃, humidity gradient greater than 90℃ and near-surface saturation, wind speed less than 1.5m / s, and latent heat flux greater than 100W / m². 2 ;

[0075] The topographic uplift and icing model meets the following criteria: uplift height greater than 120m, wind uplift index greater than 0.6, difference between traverse elevation and isotherm less than 200m, local cooling gradient greater than 0.6℃ / 100m, and cloud water content greater than 0.15g / m². 3 ;

[0076] The canyon wind tunnel icing model meets the following requirements: valley floor width-to-height ratio less than 1:3, valley depth index greater than 0.5, wind speed drop at the center of the wind tunnel greater than 1.5 m / s, terrain enclosure degree greater than 75%, and nighttime temperature gradient reversal greater than 0.

[0077] Furthermore, the icing risk score for the transmission line is as follows:

[0078]

[0079] In the above formula, R total To score the icing risk of transmission lines, w i R represents the weight of the i-th typical micro-terrain model. i The similarity between the feature indices of the digital elevation model of the transmission line corridor area and the feature indices of the i-th type of typical micro-topography model.

[0080] Furthermore, for the icing models of high-altitude lines, windward lines, topographic uplift icing models, and canyon wind tunnels, the similarity R between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is... i as follows:

[0081]

[0082] In the above formula, x is the feature index vector of the digital elevation model of the transmission line corridor area, and μ i Let T be the mean vector of feature indices of the i-th typical micro-terrain model, and let T be the transpose. Let be the empirical covariance matrix of the i-th typical micro-topography model;

[0083] For the water condensation and icing model, the similarity between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is as follows:

[0084]

[0085] In the above formula, d is the horizontal distance between the transmission line and the nearest body of water in the digital elevation model of the corresponding channel area, d0 is the preset risk threshold, and δ is the slope adjustment coefficient.

[0086] Thirdly, a computer device is provided, comprising: one or more processors;

[0087] The processor is used to execute one or more programs;

[0088] When the one or more programs are executed by the one or more processors, the method for identifying high-risk sections of transmission lines with icing based on typical micro-topography models is implemented.

[0089] Fourthly, a computer-readable storage device is provided, on which a computer program is stored, wherein when the computer program is executed, the method for identifying high-risk sections of transmission lines with icing based on a typical micro-topography model is implemented.

[0090] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects:

[0091] This invention provides a method and apparatus for identifying high-risk icing sections of transmission lines based on typical micro-topographic models. The method includes: acquiring feature indicators of a digital elevation model (DEM) of the corresponding corridor area of ​​the transmission line; determining the icing risk score of the transmission line based on the feature indicators of the DEM and the feature indicators of various typical micro-topographic models; and identifying transmission lines with icing risk scores exceeding a preset threshold as high-risk icing sections. The technical solution provided by this invention integrates high-resolution remote sensing topographic data with automated identification technology of typical micro-topographic models, enabling rapid and quantitative identification of icing-prone sections in mountainous power grids. This provides scientific support for line design optimization, operation and maintenance inspection, and icing early warning. Specifically:

[0092] 1) This invention proposes for the first time a method that integrates a high-precision digital elevation model with five types of typical micro-topography icing models for power grids. This method overcomes the problem that traditional methods relying on manual line inspection and low-resolution topographic maps are difficult to identify micro-scale icing sections, and realizes automated identification of high-risk areas within a 100-kilometer-level line range.

[0093] 2) This invention establishes a systematic topographic-climate index system, covering multi-dimensional parameters such as slope, aspect, windward slope index, valley depth index, topographic enclosure degree, water body distance, and uplift height. Combined with multi-source meteorological reanalysis data, it enhances the model's generalization ability and transferability to different geomorphic and climatic environments.

[0094] 3) This invention constructs a multi-model fusion and segmented risk scoring mechanism, realizing a refined quantitative assessment of icing risk at the conductor segment level, which can provide support for the optimization of new transmission channel routes, operation and maintenance deployment, and site selection of icing monitoring systems.

[0095] 4) The method of this invention has a clear process flow. The processing chain consists of high-precision digital elevation model acquisition, micro-topographic index extraction, multi-model matching, and risk superposition identification. It has good engineering integrability and automated operation capability and can be embedded into existing power grid geographic information platforms or remote sensing monitoring systems.

[0096] 5) Compared with traditional icing prediction methods that mainly rely on meteorological factors, this invention pays more attention to modeling the superimposed effects of microclimates induced by complex terrain. It is particularly suitable for identifying high-risk micro-topographic areas such as plateaus, canyons, saddles, and windward slopes. It has important reference value for the early prevention and control of icing disasters in key projects such as ultra-high voltage projects and mountain hydropower transmission lines. Attached Figure Description

[0097] Figure 1 This is a schematic diagram of the main steps of the method for identifying high-risk sections of transmission lines covered with ice based on a typical micro-topography model according to an embodiment of the present invention. Detailed Implementation

[0098] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0099] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0100] As disclosed in the background section, the impact of line icing disasters on power grid operation and transmission safety is becoming increasingly prominent, especially in complex mountainous areas where severe icing, dominated by micro-topography, occurs frequently, posing a serious threat to transmission line safety. Taking an icing disaster in a certain area as an example, severe icing occurred on conductors in multiple high-altitude sections, with the maximum icing thickness exceeding 40 mm, leading to problems such as line tripping and tower bending due to icing, posing significant challenges to regional power supply and winter power security.

[0101] Although some areas have mitigated the problem by installing de-icing devices or adjusting operating tension, the lack of high-resolution topographic information and quantitative identification mechanisms meant that many high-risk icing sections were not effectively avoided in the initial planning and design phases. This resulted in high maintenance costs and reliance on manual experience for icing monitoring. Traditional methods mainly rely on on-site surveys or low-resolution DEMs to determine the relationship between route alignment and icing risk, which makes it difficult to achieve regional-level batch identification and suffers from low efficiency and high subjectivity.

[0102] In addition, existing technologies for identifying icing risks on railway lines lack standardized micro-topographic models and scoring mechanisms, making it difficult to uniformly identify high-risk micro-topographic sections in different climate zones and landform types. This results in insufficient basis for engineering decisions and high costs for icing control.

[0103] To address the aforementioned problems, this invention relates to the field of power system disaster prevention technology, specifically providing a method and apparatus for identifying high-risk icing sections of transmission lines based on typical micro-topographic models. The method includes: acquiring feature indicators of a digital elevation model (DEM) of the corresponding corridor area of ​​the transmission line; determining the icing risk score of the transmission line based on the feature indicators of the DEM and the feature indicators of various typical micro-topographic models; and identifying transmission lines with icing risk scores exceeding a preset threshold as high-risk icing sections. The technical solution provided by this invention integrates high-resolution remote sensing terrain data with automated identification technology using typical micro-topographic models, enabling rapid and quantitative identification of icing-prone sections in mountainous power grids. This provides scientific support for line design optimization, operation and maintenance inspections, and icing early warning. Specifically:

[0104] 1) This invention proposes for the first time a method that integrates a high-precision digital elevation model with five types of typical micro-topography icing models for power grids. This method overcomes the problem that traditional methods relying on manual line inspection and low-resolution topographic maps are difficult to identify micro-scale icing sections, and realizes automated identification of high-risk areas within a 100-kilometer-level line range.

[0105] 2) This invention establishes a systematic topographic-climate index system, covering multi-dimensional parameters such as slope, aspect, windward slope index, valley depth index, topographic enclosure degree, water body distance, and uplift height. Combined with multi-source meteorological reanalysis data, it enhances the model's generalization ability and transferability to different geomorphic and climatic environments.

[0106] 3) This invention constructs a multi-model fusion and segmented risk scoring mechanism, realizing a refined quantitative assessment of icing risk at the conductor segment level, which can provide support for the optimization of new transmission channel routes, operation and maintenance deployment, and site selection of icing monitoring systems.

[0107] 4) The method of this invention has a clear process flow. The processing chain consists of high-precision digital elevation model acquisition, micro-topographic index extraction, multi-model matching, and risk superposition identification. It has good engineering integrability and automated operation capability and can be embedded into existing power grid geographic information platforms or remote sensing monitoring systems.

[0108] 5) Compared with traditional icing prediction methods that mainly rely on meteorological factors, this invention pays more attention to modeling the superimposed effects of microclimates induced by complex terrain. It is particularly suitable for identifying high-risk micro-topographic areas such as plateaus, canyons, saddles, and windward slopes. It has important reference value for the early prevention and control of icing disasters in key projects such as ultra-high voltage projects and mountain hydropower transmission lines.

[0109] The above plan will be explained in detail below.

[0110] Example 1

[0111] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a method for identifying high-risk icing sections of transmission lines based on a typical micro-topography model, according to an embodiment of the present invention. Figure 1 As shown, the method for identifying high-risk sections of transmission lines with icing based on typical micro-topography models in this embodiment of the invention mainly includes the following steps:

[0112] Step S101: Obtain the feature indicators of the digital elevation model of the corresponding corridor area of ​​the transmission line;

[0113] Step S102: Determine the icing risk score of the transmission line based on the feature indicators of the digital elevation model of the corresponding corridor area and the feature indicators of each typical micro-topography model;

[0114] Step S103: Identify transmission lines with icing risk scores exceeding a preset threshold as high-risk sections for icing.

[0115] In this embodiment, the spatial resolution of the digital elevation model is less than or equal to 10m.

[0116] In one implementation, an L-band or C-band SAR spaceborne platform (such as ALOS-2, Sentinel-1, etc.) is selected to cover the target power transmission channel area. Interferometric processing algorithms (such as SBAS or DInSAR) are used to extract a high-resolution DEM (≤10m) to cover the western mountainous and canyon areas with large elevation differences. Specifically, the topography of the target area is obtained through spaceborne InSAR data, and the following processing flow is mainly adopted: Data source: ALOS-2 PALSAR (L-band) or Sentinel-1 (C-band); Processing algorithm: SBAS-DInSAR (small baseline ensemble differential interferometry) + spatial filtering + phase unwrapping; Output data: DEM, resolution 10m, vertical accuracy <1m.

[0117] In this embodiment, the characteristic indicators include at least one of the following: slope aspect, slope, undulation, windward slope index, wind corridor index, valley depth index, and saddle index.

[0118] In one embodiment, the characteristic indicators further include at least one of the following: wind direction, 10m wind speed, vertical temperature gradient, near-surface humidity, inversion layer distribution, surface temperature, nighttime cooling zone, water body boundary, near-surface water body distribution area, power system online monitoring data (such as conductor icing thickness, wind speed, tripping records), and fusionable CFD simulation data (such as wind corridor wind speed enhancement ratio, wind pressure action coefficient, etc.).

[0119] In one embodiment, the slope direction as follows:

[0120]

[0121] The slope θ is as follows:

[0122]

[0123] The undulation R f as follows:

[0124] R f =z max -z min

[0125] The wind slope index W in as follows:

[0126]

[0127] The wind corridor index C wind as follows:

[0128]

[0129] The valley depth index V d as follows:

[0130]

[0131] The saddle index S a as follows:

[0132]

[0133] In the above formula, q is the rate of change of elevation with respect to the east-west direction, p is the rate of change of elevation with respect to the north-south direction, and z max For the highest elevation, z min The lowest elevation, As the prevailing wind direction, d inlet d is the cross-sectional width at the air duct inlet. outlet h is the cross-sectional width at the air duct outlet. inlet Because the valley at the air duct inlet is deep, h outlet The valley at the air duct outlet is deep, z ridge For the ridge elevation, z valley The elevation of the lowest point in the valley is d. ridge-valley z is the horizontal distance from the ridge to the valley floor. left The elevation of the mountain body on the left side of the saddle is z. right The elevation of the mountain body on the right side of the saddle is z. saddle This is the elevation of the lowest point of the saddle.

[0134] In this embodiment, obtaining the feature indicators of the digital elevation model of the transmission line corridor area includes:

[0135] The sliding window algorithm is used to obtain the sliding window section of the digital elevation model of the corresponding corridor area of ​​the transmission line;

[0136] Obtain the feature indicators corresponding to each sliding window segment, wherein the feature indicators corresponding to each sliding window segment are the feature indicators of the digital elevation model of the corresponding channel area.

[0137] In this embodiment, the typical micro-topography models include: a high-altitude line icing model (mechanism: high altitude, low temperature, snow accumulation on the mountaintop year-round, temperature field disturbance caused by mountain radiation differences + vertical atmospheric movement, resulting in the conductor being near the freezing point and prone to forming supercooled water droplets), a windward line icing model (mechanism: the line faces the prevailing wind direction, the wind speed is significantly enhanced (>2 times), cold and warm air masses converge; water droplets collide with the conductor and accumulate, forming dynamic pressure-dominated icing), a water condensation icing model (mechanism: water vapor above large rivers, reservoirs, etc., radiative cooling at night, evaporation and condensation form "humid cold air masses", which easily condense on the near-ground line), a topographic lifting icing model (mechanism: wind rises over the mountain slope, local condensation forms micro clouds; the conductor is at the condensation height, often accumulating ice at the ridge), and a canyon wind tunnel icing model (mechanism: canyons form quasi-two-dimensional wind tunnels, cold air converges at night and the temperature drops sharply, warm and humid air converges and condenses at the bottom of the valley, the conductor is in a stable cold and humid environment).

[0138] In one embodiment, the high-altitude route icing model meets the following requirements: altitude greater than 2500m, undulation greater than 180m, solar temperature gradient greater than 2℃ / 100m, slope facing north or away from the sun, and the distance between the guide elevation and the snow line is ±150m.

[0139] The icing model for the windward line meets the following requirements: wind slope index greater than 0.7, local slope within the range of 10° to 35°, angle between the line and the wind direction less than 30°, wind speed enhancement ratio greater than 2, and CFD wind pressure simulation value greater than 50Pa.

[0140] The water condensation and icing model meets the following conditions: horizontal distance from the water surface less than 200m, nighttime ground temperature less than 0℃, humidity gradient greater than 90℃ and near-surface saturation, wind speed less than 1.5m / s, and latent heat flux greater than 100W / m². 2 ;

[0141] The topographic uplift and icing model meets the following criteria: uplift height greater than 120m, wind uplift index greater than 0.6, difference between traverse elevation and isotherm less than 200m, local cooling gradient greater than 0.6℃ / 100m, and cloud water content greater than 0.15g / m². 3 ;

[0142] The canyon wind tunnel icing model meets the following requirements: valley floor width-to-height ratio less than 1:3, valley depth index greater than 0.5, wind speed drop at the center of the wind tunnel greater than 1.5 m / s, terrain enclosure degree greater than 75%, and nighttime temperature gradient reversal greater than 0.

[0143] In one embodiment, the icing risk score of the transmission line is as follows:

[0144]

[0145] In the above formula, R total To score the icing risk of transmission lines, w i R represents the weight of the i-th typical micro-terrain model. i The similarity between the feature indices of the digital elevation model of the transmission line corridor area and the feature indices of the i-th type of typical micro-topography model.

[0146] In one implementation, for the high-mountain line icing model, windward line icing model, topographic lifting icing model, and canyon wind tunnel icing model, the similarity R between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is... i as follows:

[0147]

[0148] In the above formula, x is the feature index vector of the digital elevation model of the transmission line corridor area, and μ i Let T be the mean vector of feature indices of the i-th typical micro-terrain model, and let T be the transpose. Let be the empirical covariance matrix of the i-th typical micro-topography model;

[0149] For the water condensation and icing model, the similarity between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is as follows:

[0150]

[0151] In the above formula, d is the horizontal distance between the transmission line and the nearest body of water in the digital elevation model of the corresponding channel area, d0 is the preset risk threshold, and δ is the slope adjustment coefficient.

[0152] In one specific implementation, taking a typical hydropower transmission line as an example, a full-process high-risk micro-topography icing section identification operation was performed on a mountain transmission line with a length of approximately 156 km. The specific steps are as follows:

[0153] Step S1: DEM Acquisition and Preprocessing

[0154] Acquire active and passive view SAR images under Sentinel-1 satellite orbit, and select image pairs with good coverage and short time baselines; apply SNAP and GMTSAR toolchains to perform SBAS-InSAR processing to generate a digital elevation model (DEM) with a spatial resolution of 10m; perform filtering, depression filling and terrain occlusion repair on the DEM to ensure the continuity of index calculation; verify the accuracy of the DEM and the statistical difference between it and the publicly available SRTM DEM, and control the absolute error within ±1m.

[0155] Step S2: Extraction of micro-topographic indicators

[0156] A sliding window of 100m x 100m with a step size of 50m was set to divide the entire route area into segments. Indicators such as slope, aspect, undulation, wind corridor index, valley depth index, windward slope index, and saddle index were calculated within each sliding window. Topographic enclosure degree and distance from major water bodies were introduced as supplementary factors for water condensation models. Simultaneously, ERA5 meteorological reanalysis data or regional automatic weather station data were loaded, and micrometeorological data such as near-surface wind speed and direction, humidity, and temperature lapse rate were imported as parameter inputs for some indicators, enhancing the physical rationality and dynamic adaptability of the indicator modeling.

[0157] Step S3: Construction and Matching of Typical Micro-Terrain Models

[0158] Using historical high-risk icing section sample data, the central index vectors and covariance matrices of five typical models were determined based on statistical analysis. Each sliding window section was matched and scored one by one using the Gaussian kernel Mahalanobis distance formula. A weighted fusion algorithm was used to calculate the total score of the section, taking into account the recognition ability of each model and the distribution of regional terrain features.

[0159] Step S4: High-risk segment identification and labeling

[0160] Set a risk identification threshold and extract high-scoring segments from the matching results; output the start and end latitude and longitude coordinates, length, highest-scoring model type, detailed micro-topographic indicators and score values ​​of each segment; generate a high-risk segment layer with color grading in the GIS platform for route alignment analysis and preliminary rerouting assessment.

[0161] Step S5: Result Verification and Analysis

[0162] The identification results were compared with historical manual inspection records and winter icing measurement data. Of the 48 high-risk sections identified, 42 matched the icing monitoring data, with a hit rate of 87.5%. Preliminary analysis showed that the high-risk sections were mainly concentrated in three typical micro-topographic areas: canyons, windward slopes, and saddles. The spatial distribution showed strong geomorphological dependence, indicating that the model has good potential for promotion and application in complex mountainous terrain.

[0163] Step S6: Output and Engineering Application

[0164] The generated result data package includes high-risk section Shapefile layers, EXCEL section risk attribute tables, and image and text recognition reports. The results can be used to guide key tasks such as route rerouting design, priority deployment of online de-icing devices, and optimization of UAV patrol paths, significantly improving the anti-icing capability and proactive prevention and control level in the engineering planning stage.

[0165] Example 2

[0166] Based on the same inventive concept, this invention also provides a device for identifying high-risk sections of transmission lines icing based on a typical micro-topography model. The device includes:

[0167] The acquisition module is used to acquire the feature indicators of the digital elevation model of the corresponding corridor area of ​​the transmission line.

[0168] The determination module is used to determine the icing risk score of the transmission line based on the feature indicators of the digital elevation model of the corresponding corridor area and the feature indicators of each typical micro-topography model;

[0169] The identification module is used to identify transmission lines with icing risk scores exceeding a preset threshold as high-risk sections of transmission lines for icing.

[0170] Preferably, the spatial resolution of the digital elevation model is less than or equal to 10m.

[0171] Preferably, the characteristic indicators include at least one of the following: slope aspect, slope, undulation, windward slope index, wind corridor index, valley depth index, and saddle index.

[0172] Furthermore, the characteristic indicators also include at least one of the following: wind direction, 10m wind speed, vertical temperature gradient, near-surface humidity, inversion layer distribution, surface temperature, nighttime cooling zone, water body boundary, near-surface water body distribution area, power system online monitoring data, and fusionable CFD simulation data.

[0173] Furthermore, the slope direction as follows:

[0174]

[0175] The slope θ is as follows:

[0176]

[0177] The undulation R f as follows:

[0178] R f=z max -z min

[0179] The wind slope index W in as follows:

[0180]

[0181] The wind corridor index C wind as follows:

[0182]

[0183] The valley depth index V d as follows:

[0184]

[0185] The saddle index S a as follows:

[0186]

[0187] In the above formula, q is the rate of change of elevation with respect to the east-west direction, p is the rate of change of elevation with respect to the north-south direction, and z max For the highest elevation, z min The lowest elevation, As the prevailing wind direction, d inlet d is the cross-sectional width at the air duct inlet. outlet h is the cross-sectional width at the air duct outlet. inlet Because the valley at the air duct inlet is deep, h outlet The valley at the air duct outlet is deep, z ridge For the ridge elevation, z valley The elevation of the lowest point in the valley is d. ridge-valley z is the horizontal distance from the ridge to the valley floor. left The elevation of the mountain body on the left side of the saddle is z. right The elevation of the mountain body on the right side of the saddle is z. saddle This is the elevation of the lowest point of the saddle.

[0188] Preferably, the acquisition module is specifically used for:

[0189] The sliding window algorithm is used to obtain the sliding window section of the digital elevation model of the corresponding corridor area of ​​the transmission line;

[0190] Obtain the feature indicators corresponding to each sliding window segment, wherein the feature indicators corresponding to each sliding window segment are the feature indicators of the digital elevation model of the corresponding channel area.

[0191] Preferably, the typical micro-topography models include: high mountain route icing model, windward route icing model, water condensation icing model, topographic lifting icing model, and canyon wind tunnel icing model.

[0192] Furthermore, the icing model of the high-altitude line meets the following requirements: altitude greater than 2500m, undulation greater than 180m, solar temperature gradient greater than 2℃ / 100m, slope facing north or away from the sun, and the distance between the guide elevation and the snow line is ±150m.

[0193] The icing model for the windward line meets the following requirements: wind slope index greater than 0.7, local slope within the range of 10° to 35°, angle between the line and the wind direction less than 30°, wind speed enhancement ratio greater than 2, and CFD wind pressure simulation value greater than 50Pa.

[0194] The water condensation and icing model meets the following conditions: horizontal distance from the water surface less than 200m, nighttime ground temperature less than 0℃, humidity gradient greater than 90℃ and near-surface saturation, wind speed less than 1.5m / s, and latent heat flux greater than 100W / m². 2 ;

[0195] The topographic uplift and icing model meets the following criteria: uplift height greater than 120m, wind uplift index greater than 0.6, difference between traverse elevation and isotherm less than 200m, local cooling gradient greater than 0.6℃ / 100m, and cloud water content greater than 0.15g / m². 3 ;

[0196] The canyon wind tunnel icing model meets the following requirements: valley floor width-to-height ratio less than 1:3, valley depth index greater than 0.5, wind speed drop at the center of the wind tunnel greater than 1.5 m / s, terrain enclosure degree greater than 75%, and nighttime temperature gradient reversal greater than 0.

[0197] Furthermore, the icing risk score for the transmission line is as follows:

[0198]

[0199] In the above formula, R total To score the icing risk of transmission lines, w i R represents the weight of the i-th typical micro-terrain model. i The similarity between the feature indices of the digital elevation model of the transmission line corridor area and the feature indices of the i-th type of typical micro-topography model.

[0200] Furthermore, for the icing models of high-altitude lines, windward lines, topographic uplift icing models, and canyon wind tunnels, the similarity R between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is... i as follows:

[0201]

[0202] In the above formula, x is the feature index vector of the digital elevation model of the transmission line corridor area, and μ i Let T be the mean vector of feature indices of the i-th typical micro-terrain model, and let T be the transpose. Let be the empirical covariance matrix of the i-th typical micro-topography model;

[0203] For the water condensation and icing model, the similarity between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is as follows:

[0204]

[0205] In the above formula, d is the horizontal distance between the transmission line and the nearest body of water in the digital elevation model of the corresponding channel area, d0 is the preset risk threshold, and δ is the slope adjustment coefficient.

[0206] Example 3

[0207] Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage device. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from the computer storage device to implement corresponding method flows or corresponding functions, thereby realizing the steps of the method for identifying high-risk icing sections of transmission lines based on a typical micro-terrain model in the above embodiments.

[0208] Example 4

[0209] Based on the same inventive concept, this invention also provides a storage device, specifically a computer-readable storage device (Memory). The computer-readable storage device is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage device here can include both the built-in storage device in the computer device and, of course, extended storage devices supported by the computer device. The computer-readable storage device provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage device here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage device to implement the steps of the method for identifying high-risk icing sections of transmission lines based on a typical micro-topography model in the above embodiments.

[0210] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage devices (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0211] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0212] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0213] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying high-risk icing sections of transmission lines based on a typical micro-topography model, characterized in that, The method includes: Obtain the characteristic indicators of the digital elevation model of the corridor area corresponding to the transmission line; The icing risk score of the transmission line is determined based on the feature indicators of the digital elevation model of the corresponding corridor area and the feature indicators of each typical micro-topography model. Transmission lines with icing risk scores exceeding a preset threshold are identified as high-risk sections for icing.

2. The method as described in claim 1, characterized in that, The spatial resolution of the digital elevation model is less than or equal to 10m.

3. The method as described in claim 1, characterized in that, The characteristic indicators include at least one of the following: aspect, slope, undulation, windward slope index, wind corridor index, valley depth index, and saddle index.

4. The method as described in claim 3, characterized in that, The characteristic indicators also include at least one of the following: wind direction, 10m wind speed, temperature vertical gradient, near-surface humidity, inversion layer distribution, surface temperature, nighttime cooling zone, water body boundary, near-surface water body distribution area, power system online monitoring data, and fusionable CFD simulation data.

5. The method as described in claim 3, characterized in that, The slope as follows: The slope θ is as follows: The undulation R f as follows: R f =z max -z min The wind slope index W in as follows: The wind corridor index C wind as follows: The valley depth index V d as follows: The saddle index S a as follows: In the above formula, q is the rate of change of elevation with respect to the east-west direction, p is the rate of change of elevation with respect to the north-south direction, and z max For the highest elevation, z min The lowest elevation, As the prevailing wind direction, d inlet d is the cross-sectional width at the air duct inlet. outlet h is the cross-sectional width at the air duct outlet. inlet Because the valley at the air duct inlet is deep, h outlet The valley at the air duct outlet is deep, z ridge For the ridge elevation, z valley The elevation of the lowest point in the valley is d. ridge-valley z is the horizontal distance from the ridge to the valley floor. left The elevation of the mountain body on the left side of the saddle is z. right The elevation of the mountain body on the right side of the saddle is z. saddle This is the elevation of the lowest point of the saddle.

6. The method as described in claim 1, characterized in that, The characteristic indicators for obtaining the digital elevation model of the corresponding corridor area of ​​the transmission line include: The sliding window algorithm is used to obtain the sliding window section of the digital elevation model of the corresponding corridor area of ​​the transmission line; Obtain the feature indicators corresponding to each sliding window segment, wherein the feature indicators corresponding to each sliding window segment are the feature indicators of the digital elevation model of the corresponding channel area.

7. The method as described in claim 1, characterized in that, The typical micro-topography models include at least one of the following: high mountain route icing model, windward route icing model, water condensation icing model, topographic lifting icing model, and canyon wind tunnel icing model.

8. The method as described in claim 7, characterized in that, The icing model for the high-altitude route meets the following requirements: altitude greater than 2500m, undulation greater than 180m, solar temperature gradient greater than 2℃ / 100m, slope facing north or away from the sun, and the distance between the guide elevation and the snow line is ±150m. The icing model for the windward line meets the following requirements: wind slope index greater than 0.7, local slope within the range of 10° to 35°, angle between the line and the wind direction less than 30°, wind speed enhancement ratio greater than 2, and CFD wind pressure simulation value greater than 50Pa. The water condensation and icing model meets the following conditions: horizontal distance from the water surface less than 200m, nighttime ground temperature less than 0℃, humidity gradient greater than 90℃ and near-surface saturation, wind speed less than 1.5m / s, and latent heat flux greater than 100W / m². 2 ; The topographic uplift and icing model meets the following criteria: uplift height greater than 120m, wind uplift index greater than 0.6, difference between traverse elevation and isotherm less than 200m, local cooling gradient greater than 0.6℃ / 100m, and cloud water content greater than 0.15g / m². 3 ; The canyon wind tunnel icing model meets the following requirements: valley floor width-to-height ratio less than 1:3, valley depth index greater than 0.5, wind speed drop at the center of the wind tunnel greater than 1.5 m / s, terrain enclosure degree greater than 75%, and nighttime temperature gradient reversal greater than 0.

9. The method as described in claim 7, characterized in that, The icing risk score for the transmission line is as follows: In the above formula, R total To score the icing risk of transmission lines, w i R represents the weight of the i-th typical micro-terrain model. i The similarity between the feature indices of the digital elevation model of the transmission line corridor area and the feature indices of the i-th type of typical micro-topography model.

10. The method as described in claim 7, characterized in that, For the icing models of high-mountain transmission lines, windward transmission lines, topographic lifting icing models, and canyon wind tunnel icing models, the similarity R between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is... i as follows: In the above formula, x is the feature index vector of the digital elevation model of the transmission line corridor area, and μ i Let T be the mean vector of feature indices of the i-th typical micro-terrain model, and let T be the transpose. Let be the empirical covariance matrix of the i-th typical micro-topography model; For the water condensation and icing model, the similarity between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is as follows: In the above formula, d is the horizontal distance between the transmission line and the nearest body of water in the digital elevation model of the corresponding channel area, d0 is the preset risk threshold, and δ is the slope adjustment coefficient.

11. A device for identifying high-risk icing sections of transmission lines based on a typical micro-topography model, characterized in that, The device includes: The acquisition module is used to acquire the feature indicators of the digital elevation model of the corresponding corridor area of ​​the transmission line. The determination module is used to determine the icing risk score of the transmission line based on the feature indicators of the digital elevation model of the corresponding corridor area and the feature indicators of each typical micro-topography model; The identification module is used to identify transmission lines with icing risk scores exceeding a preset threshold as high-risk sections of transmission lines for icing.

12. The apparatus as claimed in claim 11, characterized in that, The spatial resolution of the digital elevation model is less than or equal to 10m.

13. The apparatus as claimed in claim 11, characterized in that, The characteristic indicators include at least one of the following: aspect, slope, undulation, windward slope index, wind corridor index, valley depth index, and saddle index.

14. The apparatus as claimed in claim 13, characterized in that, The characteristic indicators also include at least one of the following: wind direction, 10m wind speed, temperature vertical gradient, near-surface humidity, inversion layer distribution, surface temperature, nighttime cooling zone, water body boundary, near-surface water body distribution area, power system online monitoring data, and fusionable CFD simulation data.

15. The apparatus as claimed in claim 13, characterized in that, The slope as follows: The slope θ is as follows: The undulation R f as follows: R f =z max -z min The wind slope index W in as follows: The wind corridor index C wind as follows: The valley depth index V d as follows: The saddle index S a as follows: In the above formula, q is the rate of change of elevation with respect to the east-west direction, p is the rate of change of elevation with respect to the north-south direction, and z max For the highest elevation, z min The lowest elevation, As the prevailing wind direction, d inlet d is the cross-sectional width at the air duct inlet. outlet h is the cross-sectional width at the air duct outlet. inlet Because the valley at the air duct inlet is deep, h outlet The valley at the air duct outlet is deep, z ridge For the ridge elevation, z valley The elevation of the lowest point in the valley is d. ridge-valley z is the horizontal distance from the ridge to the valley floor. left The elevation of the mountain body on the left side of the saddle is z. right The elevation of the mountain body on the right side of the saddle is z. saddle This is the elevation of the lowest point of the saddle.

16. The apparatus as claimed in claim 11, characterized in that, The acquisition module is specifically used for: The sliding window algorithm is used to obtain the sliding window section of the digital elevation model of the corresponding corridor area of ​​the transmission line; Obtain the feature indicators corresponding to each sliding window segment, wherein the feature indicators corresponding to each sliding window segment are the feature indicators of the digital elevation model of the corresponding channel area.

17. The apparatus as claimed in claim 11, characterized in that, The typical micro-topography models include: high mountain route icing model, windward route icing model, water condensation icing model, topographic uplift icing model, and canyon wind tunnel icing model.

18. The apparatus as claimed in claim 17, characterized in that, The icing model for the high-altitude route meets the following requirements: altitude greater than 2500m, undulation greater than 180m, solar temperature gradient greater than 2℃ / 100m, slope facing north or away from the sun, and the distance between the guide elevation and the snow line is ±150m. The icing model for the windward line meets the following requirements: wind slope index greater than 0.7, local slope within the range of 10° to 35°, angle between the line and the wind direction less than 30°, wind speed enhancement ratio greater than 2, and CFD wind pressure simulation value greater than 50Pa. The water condensation and icing model meets the following conditions: horizontal distance from the water surface less than 200m, nighttime ground temperature less than 0℃, humidity gradient greater than 90℃ and near-surface saturation, wind speed less than 1.5m / s, and latent heat flux greater than 100W / m². 2 ; The topographic uplift and icing model meets the following criteria: uplift height greater than 120m, wind uplift index greater than 0.6, difference between traverse elevation and isotherm less than 200m, local cooling gradient greater than 0.6℃ / 100m, and cloud water content greater than 0.15g / m². 3 ; The canyon wind tunnel icing model meets the following requirements: valley floor width-to-height ratio less than 1:3, valley depth index greater than 0.5, wind speed drop at the center of the wind tunnel greater than 1.5 m / s, terrain enclosure degree greater than 75%, and nighttime temperature gradient reversal greater than 0.

19. The apparatus as claimed in claim 17, characterized in that, The icing risk score for the transmission line is as follows: In the above formula, R total To score the icing risk of transmission lines, w i R represents the weight of the i-th typical micro-terrain model. i The similarity between the feature indices of the digital elevation model of the transmission line corridor area and the feature indices of the i-th type of typical micro-topography model.

20. The apparatus as claimed in claim 17, characterized in that, For the icing models of high-mountain transmission lines, windward transmission lines, topographic lifting icing models, and canyon wind tunnel icing models, the similarity R between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is... i as follows: In the above formula, x is the feature index vector of the digital elevation model of the transmission line corridor area, and μ i Let T be the mean vector of feature indices of the i-th typical micro-terrain model, and let T be the transpose. Let be the empirical covariance matrix of the i-th typical micro-topography model; For the water condensation and icing model, the similarity between the feature indices of the digital elevation model of the corresponding corridor area of ​​the transmission line and the feature indices of the i-th type of typical micro-topography model is as follows: In the above formula, d is the horizontal distance between the transmission line and the nearest body of water in the digital elevation model of the corresponding channel area, d0 is the preset risk threshold, and δ is the slope adjustment coefficient.

21. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the method for identifying high-risk icing sections of transmission lines based on a typical micro-topography model as described in any one of claims 1 to 10 is implemented.

22. A computer-readable storage device, characterized in that, It contains a computer program, which, when executed, implements the method for identifying high-risk icing sections of transmission lines based on a typical micro-topography model as described in any one of claims 1 to 10.

Images