Micro-terrain and micro-meteorological power transmission line icing reanalysis database construction method and system

Abstract

The invention provides a micro-topography and micro-meteorological power transmission line icing reanalysis database construction method and system, and relates to the technical field of power system disaster prevention and reduction and power grid meteorological application. The method comprises the following steps: carrying out space-time alignment on original DEM data and original reanalysis meteorological data of a complex mountain region to obtain a basic data set; carrying out dynamic downscaling simulation by taking re-analyzed meteorological data in the basic data set as a large-scale background field and boundary conditions and taking DEM data of the basic data set as a terrain forced field; performing vertical gradient correction for the actual height of the power transmission line based on a boundary layer theory; extracting a microtopographic factor of the power transmission line corridor area; carrying out local micro-scale terrain effect correction by utilizing the micro-terrain factors; and constructing a power transmission line icing reanalysis database based on the microtopographic factors and the high temporal-spatial resolution reanalysis meteorological data. The problem that the icing prediction accuracy and precision of an existing complex mountain region are low is solved.

Description

Technical Field

[0001] This invention relates to the field of power system disaster prevention and mitigation and power grid meteorological application technology, specifically to a method and system for constructing a database for micro-topography and micro-meteorology transmission line icing reanalysis. Background Technology

[0002] Transmission line icing is one of the most typical and destructive meteorological disasters in the mountainous areas of southern my country and regions frequently affected by freezing rain and snow. Icing significantly increases the weight of conductors and fittings, as well as wind loads, triggering a series of cascading faults such as conductor galloping, strand breakage, wire breakage, fitting damage, tower collapse, and insulator flashover. This can lead to widespread power outages or even grid disconnection, seriously threatening the safe and stable operation of the power grid and national economic activities. As power grids develop towards higher voltage, larger capacity, longer distances, and deeper mountainous areas, the proportion of transmission lines traversing complex mountainous terrain and valleys is constantly increasing, thus increasing the risk of exposure to icing disasters.

[0003] Currently, power grid icing monitoring methods still mainly rely on a limited number of meteorological observation stations, icing monitoring devices, and manual line inspections. The spatial coverage is limited, and the accuracy and precision of icing prediction in complex mountainous areas are low, making it difficult to meet the requirements for safe operation of the power grid. Summary of the Invention

[0004] To overcome the problem of low accuracy and precision in icing prediction in complex mountainous areas, this invention provides a method and system for constructing a micro-topography and micro-meteorology transmission line icing reanalysis database.

[0005] On the one hand, this invention provides a method for constructing a database for reanalysis of icing on power transmission lines in micro-topography and micro-meteorology, comprising: Spatiotemporal alignment was performed on the original digital elevation model (DEM) data and the original reanalysis meteorological data of the acquired complex mountainous area to obtain the basic dataset; Using the reanalysis meteorological data in the aforementioned basic dataset as the large-scale background field and boundary conditions, and the DEM data in the aforementioned basic dataset as the topographic forcing field, dynamic downscaling simulation is performed to obtain the mesoscale meteorological field of the target icy mountain area. Based on boundary layer theory, the vertical gradient of the mesoscale meteorological field is corrected for the actual height of the transmission line to obtain the mesoscale distribution field of each meteorological element in the conductor height layer. Micro-topographic factors of the transmission line corridor area are extracted from the basic dataset; the micro-topographic factors are used to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, and high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Based on the micro-topographic factors of the target icy mountain area and the high spatiotemporal resolution reanalysis meteorological data, a reanalysis database of icing on transmission lines is constructed.

[0006] Optionally, the micro-topographic factors include at least slope, aspect, topographic relief, topographic shielding, and wind corridor index; The terrain shielding degree is used to characterize the windward / leeward characteristics of different spatial locations of the target icy mountain area in different directions; The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area.

[0007] Optionally, the process of extracting the terrain shielding degree includes: For a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset, the terrain shielding degree of the spatial location is calculated by performing terrain shielding scans along multiple azimuth angles of the spatial location. The extraction process of the wind corridor index includes: For a spatial location within the transmission line corridor area of ​​a target icy mountainous region in the basic dataset, the wind corridor index of the spatial location is calculated based on the prevailing wind direction and topographic profile features extracted from the basic dataset.

[0008] Optionally, the terrain shielding degree of the spatial location is calculated by performing terrain shielding scans along multiple azimuth angles, including: Centered on the spatial location, the terrain is scanned upstream along multiple azimuth angles of the spatial location to determine the height of the scanning point that blocks the incoming wind and the horizontal distance between the scanning point and the spatial location. Based on the height of the scanning point and the horizontal distance between the scanning point and the spatial location, the maximum shielding angle in the azimuth direction corresponding to the scanning point is determined; the maximum shielding angles in multiple azimuth directions are normalized to obtain the terrain shielding degree of the spatial location.

[0009] Optionally, based on the prevailing wind direction and topographic profile features of the spatial location extracted from the base dataset, the wind corridor index of the spatial location is calculated, including: Based on historical statistical analysis of DEM data and reanalysis meteorological data in the aforementioned basic dataset, the prevailing wind direction in the transmission line corridor area is determined. The topographic profile along the transmission line is extracted along the prevailing wind direction in the transmission line corridor area to obtain the elevation difference characteristics, slope characteristics along the prevailing wind direction, and effective channel characteristics along the prevailing wind direction of the spatial location. Based on the weighted fusion of the elevation characteristics, windward slope characteristics, and effective windward channel characteristics of the spatial location, the wind corridor index of the spatial location is obtained. The effective channel feature along the prevailing wind direction is the aspect ratio of an effective channel with valley characteristics in the prevailing wind direction.

[0010] Optionally, the original DEM data and original reanalysis meteorological data of the acquired complex mountainous areas are spatiotemporally aligned to obtain a basic dataset, including: The latitude and longitude grid of the original DEM data in the complex mountainous area is projected and resampled to the target plane coordinate system to obtain the preprocessed elevation field; By performing projection mapping and spatial interpolation on the near-surface meteorological elements in the original reanalysis meteorological data of complex mountainous areas for the target plane coordinate system, a fine-grid meteorological field is obtained. The time step of the fine-grid meteorological field is converted to the target time step using linear time interpolation to obtain the preprocessed meteorological field. The basic dataset is obtained based on the preprocessed elevation field and the preprocessed meteorological field.

[0011] Optionally, using the reanalysis meteorological data in the base dataset as the large-scale background field and boundary conditions, and the DEM data in the base dataset as the topographic forcing field, dynamic downscaling simulation is performed to obtain the mesoscale meteorological field of the target glacier-covered mountain area, including: Configure a multi-nested grid for the preset mesoscale numerical weather prediction model, and map the DEM data in the basic dataset to each layer of the mesoscale numerical weather prediction model through spatial interpolation to generate the model topographic height field. Vertical interpolation and variable transformation are performed on the reanalysis meteorological data in the basic dataset to generate the three-dimensional spatiotemporal dependent initial field and boundary conditions required by the mesoscale numerical weather prediction model. Using the terrain height field of the model as the underlying surface forcing, and the three-dimensional spatiotemporally dependent initial field and boundary conditions as the driving force and constraints, the mesoscale numerical weather prediction model is run, and the mesoscale meteorological field of the target icy mountain area is screened out on the inner grid of the mesoscale numerical weather prediction model.

[0012] Optionally, the mesoscale meteorological field includes a mesoscale temperature field, a mesoscale wind speed field, a mesoscale wind direction field, a mesoscale humidity field, and a mesoscale precipitation variable field; based on boundary layer theory, the mesoscale meteorological field is corrected for the vertical gradient of the transmission line at its actual height to obtain the mesoscale distribution field of each meteorological element at the conductor height layer, including: For each grid cell in the transmission line corridor region of the mesoscale temperature field, the vertical temperature gradient of the grid cell is calculated using boundary layer theory based on the temperature and height of the two adjacent model layers above and below the conductor height. Based on the vertical temperature gradient and the height difference between the conductor height and the lower model layer, the temperature of the lower model layer is linearly extrapolated to obtain the corrected temperature at the conductor height of the grid cell. For each grid cell in the transmission line corridor region of the mesoscale wind speed field, based on the boundary layer theory, the wind speed at any near-ground height is extrapolated to the conductor height using the power law exponent of the wind speed profile to obtain the corrected wind speed at the conductor height of the grid cell. The near-surface wind direction of the mesoscale wind field is taken as the wind direction of the conductor height layer; For the mesoscale humidity field and the mesoscale precipitation variable field, vertical direction diagnosis or extrapolation is performed for the conductor height to obtain the relative humidity and precipitation-related variables at the conductor height layer. Based on the corrected air temperature and corrected wind speed of each grid cell in the transmission line corridor area, as well as wind direction, relative humidity and precipitation-related variables at the conductor height layer, a mesoscale distribution field of meteorological elements at the conductor height layer is generated. The power-law exponent of the wind speed profile is determined based on the surface roughness of the underlying surface of the mesoscale wind speed field.

[0013] Optionally, the micro-topographic factors are used to correct the mesoscale distribution field of each meteorological element in the guide height layer for local micro-scale topographic effects, generating high spatiotemporal resolution reanalysis meteorological data for the target icy mountain area, including: Based on the topographic relief, topographic shielding and wind corridor index in the micro-topographic factors, the temperature distribution field of the conductor height layer is microscale corrected to obtain the microscale corrected temperature distribution field of the conductor height layer. Based on the wind corridor index, terrain shielding degree and slope in the micro-topographic factors, the wind speed field at the conductor height layer is corrected for acceleration effect to obtain the micro-scale corrected wind speed distribution field at the conductor height layer. Based on the microscale corrected temperature distribution field and wind speed distribution field at the conductor height, the distribution fields of other meteorological elements at the conductor height are corrected by utilizing the correlation between different meteorological elements. Based on the correction results of the microscale corrected temperature distribution field, the microscale corrected wind speed distribution field, and the distribution fields of other meteorological elements at the conductor height, high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Other meteorological elements include at least relative humidity and precipitation-related variables.

[0014] Optionally, after extracting the micro-topographical factors of the transmission line corridor area from the base dataset, the method further includes: The power transmission line is divided into several line segments; For each line segment, a buffer is constructed for the line segment, and the statistics for each micro-topographic factor within the buffer are used as the micro-topographic factors of the line segment. Accordingly, before using the micro-topographic factors to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, the method further includes: By spatial interpolation, the mesoscale distribution fields of meteorological elements in the conductor height layer are mapped to each line segment, thus obtaining the mesoscale distribution fields of meteorological elements in each line segment at the conductor height layer.

[0015] Optionally, the mesoscale distribution field of each meteorological element in the conductor height layer also includes the mesoscale liquid water content distribution field of the conductor height layer; The liquid water content distribution field of the conductor height layer is determined based on the air temperature distribution field, the relative humidity distribution field, and the precipitation-related variable distribution field of the conductor height layer. Accordingly, the high spatiotemporal resolution reanalysis meteorological data also includes the correction results of the liquid water content distribution field in the conductor height layer; The correction results for the liquid water content distribution field of the conductor height layer are determined based on the correction results of the air temperature distribution field of the conductor height layer after microscale correction, the correction results of the relative humidity distribution field of the conductor height layer, and the correction results of the precipitation-related variable distribution field.

[0016] Optionally, the diagnostic model for the liquid water content distribution field of the conductor height layer is as follows:

[0017] in, Diagnostic value of liquid water content in conductor height layer at time t for line segment k; The proportionality coefficient for diagnosing liquid water content; For line segment k, the precipitation-related variables at the conductor height layer at time t; The relative humidity of the conductor height layer at time t for line segment k; The conductor height and air temperature at time t are given for line segment k. This is a relative humidity weighting function used to characterize the degree of influence of different humidity levels on liquid water content; This is a temperature weighting function used to characterize the degree of influence of different temperatures on supercooled liquid water.

[0018] Optionally, before performing the dynamic downscaling simulation, the method further includes: Based on the centralized reanalysis of meteorological data in the aforementioned basic dataset, the average meteorological field for each time period is calculated, and the reanalysis meteorological data at any moment in the aforementioned basic dataset is decomposed into the sum of the average meteorological field and the abnormal meteorological quantity for the corresponding time period. The abnormal meteorological data are subjected to spatiotemporal smoothing. Based on the smoothed abnormal meteorological data and the average meteorological field of each time period, smoothed meteorological data are obtained for further analysis.

[0019] Optionally, based on the micro-topographical factors of the target icing-covered mountain area and high spatiotemporal resolution reanalysis meteorological data, a transmission line icing reanalysis database is constructed, including: Using a pre-defined first-format data model, the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data are stored hierarchically using spatial grid cells and time steps as indexes.

[0020] Optionally, based on the micro-topographical factors of the target icing-covered mountain area and high spatiotemporal resolution reanalysis meteorological data, a transmission line icing reanalysis database is constructed, including: Using a pre-defined second-format data model, the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data are stored hierarchically, indexed by line segments and time steps.

[0021] Optionally, after constructing the transmission line icing reanalysis database, the following steps are also included: If an update to the original reanalysis meteorological data is detected, dynamic downscaling simulation, vertical gradient correction, and local microscale topographic effect correction are performed on the updated reanalysis meteorological data to obtain high spatiotemporal resolution meteorological update data. The transmission line icing reanalysis database is updated based on the high spatiotemporal resolution meteorological update data.

[0022] On the other hand, the present invention also provides a system for constructing a database for reanalysis of icing on power transmission lines in micro-topography and micro-meteorology, comprising: The preprocessing module is used to perform spatiotemporal alignment on the acquired raw digital elevation model (DEM) data and raw reanalysis meteorological data of complex mountainous areas to obtain the basic dataset. The downscaling module is used to perform dynamic downscaling simulation using reanalysis meteorological data in the base dataset as a large-scale background field and boundary conditions, and DEM data in the base dataset as a topographic forcing field, to obtain the mesoscale meteorological field of the target icy mountain area. The vertical correction module is used to perform vertical gradient correction of the mesoscale meteorological field based on the boundary layer theory for the actual height of the transmission line, so as to obtain the mesoscale distribution field of each meteorological element in the conductor height layer. The microscale correction module is used to extract micro-topographic factors of the transmission line corridor area from the basic dataset; and to use the micro-topographic factors to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, thereby generating high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area. A construction module is used to build a reanalysis database for icing of transmission lines based on the micro-topographic factors and high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area.

[0023] Optionally, the micro-topographic factors include at least slope, aspect, topographic relief, topographic shielding, and wind corridor index; The terrain shielding degree is used to characterize the windward / leeward characteristics of different spatial locations of the target icy mountain area in different directions; The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area.

[0024] Optionally, the microscale correction module includes a terrain factor extraction submodule, which includes: The shielding calculation subunit is used to calculate the terrain shielding of a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset by performing terrain shielding scans along multiple azimuth angles of the spatial location. The wind corridor calculation subunit is used to calculate the wind corridor index of a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset, based on the prevailing wind direction and topographic profile features of the spatial location extracted from the basic dataset.

[0025] Optionally, the shielding calculation subunit is specifically used for: Centered on the spatial location, the terrain is scanned upstream along multiple azimuth angles of the spatial location to determine the height of the scanning point that blocks the incoming wind and the horizontal distance between the scanning point and the spatial location. Based on the height of the scanning point and the horizontal distance between the scanning point and the spatial location, the maximum shielding angle in the azimuth direction corresponding to the scanning point is determined; the maximum shielding angles in multiple azimuth directions are normalized to obtain the terrain shielding degree of the spatial location.

[0026] Optionally, the wind corridor calculation subunit is specifically used for: Based on historical statistical analysis of DEM data and reanalysis meteorological data in the aforementioned basic dataset, the prevailing wind direction in the transmission line corridor area is determined. The topographic profile along the transmission line is extracted along the prevailing wind direction in the transmission line corridor area to obtain the elevation difference characteristics, slope characteristics along the prevailing wind direction, and effective channel characteristics along the prevailing wind direction of the spatial location. Based on the weighted fusion of the elevation characteristics, windward slope characteristics, and effective windward channel characteristics of the spatial location, the wind corridor index of the spatial location is obtained. The effective channel feature along the prevailing wind direction is the aspect ratio of an effective channel with valley characteristics in the prevailing wind direction.

[0027] Optionally, the preprocessing module includes: The elevation preprocessing submodule is used to project and resample the latitude and longitude grid of the original DEM data of complex mountainous areas to the target plane coordinate system, so as to obtain the preprocessed elevation field. The meteorological preprocessing submodule is used to perform projection mapping and spatial interpolation on near-ground meteorological elements in the raw reanalysis meteorological data of complex mountainous areas to the target plane coordinate system, so as to obtain a fine-grid meteorological field; and to use linear time interpolation to transform the time step of the fine-grid meteorological field to the target time step, so as to obtain the preprocessed meteorological field. The dataset formation submodule is used to obtain the basic dataset based on the preprocessed elevation field and the preprocessed meteorological field.

[0028] Optionally, the downscaling module is specifically used for: Configure a multi-nested grid for the preset mesoscale numerical weather prediction model, and map the DEM data in the basic dataset to each layer of the mesoscale numerical weather prediction model through spatial interpolation to generate the model topographic height field. Vertical interpolation and variable transformation are performed on the reanalysis meteorological data in the basic dataset to generate the three-dimensional spatiotemporal dependent initial field and boundary conditions required by the mesoscale numerical weather prediction model. Using the terrain height field of the model as the underlying surface forcing, and the three-dimensional spatiotemporally dependent initial field and boundary conditions as the driving force and constraints, the mesoscale numerical weather prediction model is run, and the mesoscale meteorological field of the target icy mountain area is screened out on the inner grid of the mesoscale numerical weather prediction model.

[0029] Optionally, the mesoscale meteorological field includes a mesoscale temperature field, a mesoscale wind speed field, a mesoscale wind direction field, a mesoscale humidity field, and a mesoscale precipitation variable field; the vertical correction module includes: The temperature correction submodule is used to calculate the vertical temperature gradient of each grid cell in the transmission line corridor region of the mesoscale temperature field using boundary layer theory, based on the temperature and height of the two adjacent model layers above and below the conductor height; and to linearly extrapolate the temperature of the lower model layer based on the vertical temperature gradient and the height difference between the conductor height and the lower model layer to obtain the corrected temperature at the conductor height of the grid cell. The wind speed correction submodule is used to extrapolate the wind speed at any near-ground height to the conductor height for each grid cell in the transmission line corridor area of ​​the mesoscale wind speed field, based on boundary layer theory and using the power law exponent of the wind speed profile, to obtain the corrected wind speed at the conductor height of the grid cell. The diagnostic extrapolation submodule is used to take the near-surface wind direction of the mesoscale wind direction field as the wind direction of the conductor height layer; for the mesoscale humidity field and the mesoscale precipitation variable field, vertical direction diagnosis or extrapolation is performed for the conductor height to obtain the relative humidity and precipitation-related variables of the conductor height layer. The distribution field generation submodule is used to generate the mesoscale distribution field of various meteorological elements at the conductor height layer based on the corrected air temperature and corrected wind speed of each grid cell in the transmission line corridor area, as well as wind direction, relative humidity and precipitation-related variables at the conductor height layer. The power-law exponent of the wind speed profile is determined based on the surface roughness of the underlying surface of the mesoscale wind speed field.

[0030] Optionally, the microscale correction module includes a scale correction submodule, which is used for: Based on the topographic relief, topographic shielding and wind corridor index in the micro-topographic factors, the temperature distribution field of the conductor height layer is microscale corrected to obtain the microscale corrected temperature distribution field of the conductor height layer. Based on the wind corridor index, terrain shielding degree and slope in the micro-topographic factors, the wind speed field at the conductor height layer is corrected for acceleration effect to obtain the micro-scale corrected wind speed distribution field at the conductor height layer. Based on the microscale corrected temperature distribution field and wind speed distribution field at the conductor height, the distribution fields of other meteorological elements at the conductor height are corrected by utilizing the correlation between different meteorological elements. Based on the correction results of the microscale corrected temperature distribution field, the microscale corrected wind speed distribution field, and the distribution fields of other meteorological elements at the conductor height, high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Other meteorological elements include at least relative humidity and precipitation-related variables.

[0031] Optionally, the microscale correction module further includes: The segmented statistics submodule is used to divide the transmission line into several line segments; for each line segment, a buffer is constructed for the line segment, and the statistics for each micro-topographic factor in the buffer are used as the micro-topographic factors of the line segment. The segmented mapping submodule is used to map the mesoscale distribution field of each meteorological element in the conductor height layer to each line segment through spatial interpolation, so as to obtain the mesoscale distribution field of each meteorological element in each line segment in the conductor height layer.

[0032] Optionally, the mesoscale distribution field of each meteorological element in the conductor height layer also includes the mesoscale liquid water content distribution field of the conductor height layer; The liquid water content distribution field of the conductor height layer is determined based on the air temperature distribution field, the relative humidity distribution field, and the precipitation-related variable distribution field of the conductor height layer. Accordingly, the high spatiotemporal resolution reanalysis meteorological data also includes the correction results of the liquid water content distribution field in the conductor height layer; The correction results for the liquid water content distribution field of the conductor height layer are determined based on the correction results of the air temperature distribution field of the conductor height layer after microscale correction, the correction results of the relative humidity distribution field of the conductor height layer, and the correction results of the precipitation-related variable distribution field.

[0033] Optionally, the diagnostic model for the liquid water content distribution field of the conductor height layer is as follows:

[0034] in, Diagnostic value of liquid water content in conductor height layer at time t for line segment k; The proportionality coefficient for diagnosing liquid water content; For line segment k, the precipitation-related variables at the conductor height layer at time t; The relative humidity of the conductor height layer at time t for line segment k; The conductor height and air temperature at time t are given for line segment k. This is a relative humidity weighting function used to characterize the degree of influence of different humidity levels on liquid water content; This is a temperature weighting function used to characterize the degree of influence of different temperatures on supercooled liquid water.

[0035] Optionally, it further includes: a data smoothing module, the data smoothing module being used for: Based on the centralized reanalysis of meteorological data in the aforementioned basic dataset, the average meteorological field for each time period is calculated, and the reanalysis meteorological data at any moment in the aforementioned basic dataset is decomposed into the sum of the average meteorological field and the abnormal meteorological quantity for the corresponding time period. The abnormal meteorological data are subjected to spatiotemporal smoothing. Based on the smoothed abnormal meteorological data and the average meteorological field of each time period, smoothed meteorological data are obtained for further analysis.

[0036] Optionally, the building module includes: The first construction submodule is used to store the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data in a hierarchical manner using a preset first format data model, with spatial grid cells and time steps as indexes.

[0037] Optionally, the building module includes: The second construction submodule is used to store the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data in a hierarchical manner using a preset second-format data model, indexed by line segments and time steps.

[0038] Optionally, it also includes: The data update module is used to perform dynamic downscaling simulation, vertical gradient correction, and local microscale topographic effect correction on the updated reanalysis meteorological data if an update to the original reanalysis meteorological data is detected, so as to obtain high spatiotemporal resolution meteorological update data; and to update the transmission line icing reanalysis database based on the high spatiotemporal resolution meteorological update data.

[0039] On the other hand, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method described in any of the foregoing is implemented.

[0040] On the other hand, the present invention also provides a readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the method described in any one of the above.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method and system for constructing a micro-topography and micro-meteorological transmission line icing reanalysis database. By spatiotemporally aligning the original DEM data and reanalysis meteorological data, a unified basic dataset is formed, solving the problem of spatiotemporal inconsistency of multi-source data. Using the reanalysis meteorological data in the basic dataset as a large-scale background field and boundary conditions, and the DEM data in the basic dataset as a topographic forcing field, dynamic downscaling simulation is performed to generate a mesoscale meteorological field of the target icy mountain area. This effectively refines the meteorological elements of the icy mountain area to the mountain scale, significantly improving the spatial resolution of the meteorological field in complex mountainous areas.

[0042] This invention uses boundary layer theory to obtain a mesoscale meteorological field at the conductor height level through vertical gradient correction, shifting meteorological elements to the actual transmission line height for a more accurate characterization of the icing microenvironment. Furthermore, by extracting micro-topographic factors from the transmission line corridor, it precisely quantifies local topographic features and uses these factors to perform local micro-scale corrections on meteorological elements at the conductor height, correcting the influence of local special topography such as ridges, valleys, and wind corridors on meteorological elements. This fully integrates topographic and vertical meteorological details to achieve a refined depiction of icing meteorological characteristics, generating high spatiotemporal resolution reanalysis meteorological data for the icing process. This significantly improves the accuracy and precision of meteorological element simulation for icing in complex mountainous areas, thereby enhancing the accuracy and precision of icing risk assessment and prediction based on this data.

[0043] The icing reanalysis database for transmission lines in complex mountainous areas constructed by this invention can provide a unified data foundation for long-term, high-precision icing analysis, improve the accuracy and precision of transmission line icing risk assessment and prediction, and meet the requirements for safe operation of the power grid. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a method for constructing a database for reanalysis of icing on power transmission lines in micro-topography and micro-meteorology, according to the present invention. Figure 2 This is a schematic diagram illustrating the data processing procedure for constructing a transmission line icing reanalysis database, as an example of the present invention. Figure 3 This is a schematic diagram illustrating the process of constructing an ice-covered micro-region sample library as an example of the present invention; Figure 4 This is a flowchart illustrating different stages of the construction of an icing reanalysis database, as an example of the present invention. Figure 5 This is a block diagram of an electronic device according to the present invention. Detailed Implementation

[0045] The formation and development of icing are influenced by a variety of meteorological factors and underlying surface conditions. The wind, temperature, and humidity fields within complex mountainous areas often exhibit strong spatial heterogeneity and small-scale abrupt changes. The icing condition of the same transmission line can vary significantly within a few tens of meters or even smaller scales. Currently, power grid icing monitoring methods still mainly rely on a limited number of meteorological observation stations, icing monitoring devices, and manual line inspections. These methods have limited spatial coverage and are difficult to reflect changes in the micro-scale meteorological environment along the line in real time.

[0046] Currently, common practices in power grid icing operations include empirical early warning based on station observations, calculation of icing mechanism models based on reanalysis data, and short-term forecasting based on numerical weather prediction models. However, due to the lack of high-precision reanalysis data that fully considers the coupling effect of micro-topography and micro-meteorology, these methods still have significant uncertainties in identifying icing in complex mountainous areas, estimating icing thickness, and determining critical moments (such as reaching warning thickness or the time of extreme value occurrence). These methods often fail to meet the requirements of "high precision, strong robustness, and traceability" for safe power grid operation.

[0047] Based on the above analysis, this invention provides a method and system that, under limited basic data conditions such as DEM and reanalysis data, explicitly incorporates micro-topographic information, fully considers the near-surface structure of the conductor height, and uses multi-scale modeling and correction methods to reconstruct key meteorological elements of icing such as temperature, humidity, wind speed and direction, liquid water content, and precipitation phase with high precision. Furthermore, it constructs a high-precision reanalysis database of icing that can be updated on a rolling basis using a unified data model, providing a solid data foundation for transmission line icing risk assessment, real-time early warning, operation and maintenance decision-making, and large-scale model training in the field of icing.

[0048] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0049] Example 1 This invention provides a method for constructing a database for reanalysis of icing on power transmission lines in micro-topography and micro-meteorology, as illustrated in the diagram below. Figure 1 As shown, the method includes: Step S110: Spatiotemporal alignment is performed on the original digital elevation model (DEM) data and the original reanalysis meteorological data of the acquired complex mountainous area to obtain the basic dataset; Step S120: Using the reanalysis meteorological data in the basic dataset as the large-scale background field and boundary conditions, and using the DEM data in the basic dataset as the topographic forcing field, perform dynamic downscaling simulation to obtain the mesoscale meteorological field of the target icy mountain area. Step S130: Based on boundary layer theory, the vertical gradient of the mesoscale meteorological field is corrected for the actual height of the transmission line to obtain the mesoscale distribution field of each meteorological element in the conductor height layer. Step S140: Extract micro-topographic factors of the transmission line corridor area from the basic dataset; use the micro-topographic factors to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, and generate high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area. Step S150: Based on the micro-topographic factors of the target icy mountain area and the high spatiotemporal resolution reanalysis meteorological data, construct a reanalysis database for icing of transmission lines.

[0050] In this example implementation, the raw DEM (Digital Elevation Model) data can be obtained from an existing DEM library or generated based on actual measurements. The raw reanalysis meteorological data can be obtained from existing publicly available reanalysis datasets, such as ERA5. The process begins with the integration and alignment of the two fundamental data sources. The raw DEM data provides accurate topographic relief information for complex mountainous areas, fundamentally characterizing micro-topographic features; while the raw reanalysis meteorological data, such as ERA5, provides a large-scale, long-series meteorological background field. Spatiotemporal alignment is the foundation for all subsequent analyses, unifying data from different sources, resolutions, and coordinate systems into a single spatiotemporal framework, forming a "baseline" dataset. This ensures a strict spatial and temporal correspondence between topographic and meteorological information, eliminating systematic errors in data fusion and providing reliable input for subsequent refined modeling. After obtaining the unified basic dataset, this method introduces dynamic downscaling simulation technology. This aims to overcome the limitations of large-scale reanalysis data, which has coarse spatial resolution and cannot reflect the dynamic and thermal forcing effects of topography on atmospheric circulation. Specifically, the reanalysis meteorological field in the basic dataset is used as a large-scale background field and boundary condition, providing the initial regional field and persistent boundary constraints for the simulation. Simultaneously, high-resolution DEM data is used as input for the underlying topographic forcing field. By running a mesoscale numerical weather prediction model, such as the WRF model, its physical equations are integrated on a nested high-resolution grid. The model can finely characterize the effects of complex topography on airflow (e.g., bypass, ascent, acceleration), temperature (e.g., inversion, cold lake effect), and humidity distribution, thus "downscaling" large-scale meteorological information to the target glacier area, generating a meteorological field with higher spatial resolution that reflects the mesoscale topographic forcing effect—a mesoscale meteorological field—that can physically couple the regional climate background with local topographic effects. However, the obtained mesoscale meteorological field is usually output at the model's standard height level, which differs from the actual suspension height of power transmission line conductors. Conductor height is typically between 20 and 40 meters, while the model's lowest level may be higher, or the standard output may be 2 meters for air temperature and 10 meters for wind speed. Directly using these data introduces significant errors. Therefore, vertical gradient correction of the mesoscale meteorological field is further performed based on boundary layer theory. The mesoscale meteorological field includes at least the mesoscale temperature field, mesoscale wind speed field, mesoscale wind direction field, mesoscale humidity field, and mesoscale precipitation variable field. The mesoscale meteorological field may also include the liquid water content field directly related to icing.Accordingly, each meteorological element includes at least temperature, wind speed, wind direction, relative humidity, and precipitation-related variables. Precipitation-related variables refer to meteorological variables used to characterize precipitation conditions, such as precipitation intensity or liquid water flux representative variables, precipitation phase, etc. Meteorological elements may also include liquid water content. In the absence of direct liquid water content observations, liquid water content can be diagnosed based on precipitation-related variables from ERA5 and near-conductor temperature and humidity elements. By calculating the vertical temperature gradient and performing linear extrapolation, and by using the power-law wind speed profile to extrapolate wind speed to the conductor height, the mesoscale meteorological field is accurately mapped to the near-conductor atmosphere where the transmission line is located, resulting in corrected conductor height temperature field, wind speed field, etc., making the meteorological data more consistent with the actual exposure environment of the transmission line. Subsequently, in order to further improve the accuracy of the data at the microscale, especially to capture abrupt changes in meteorological elements caused by minor topographic undulations within a scale of tens of meters, a local correction process driven by micro-topographic factors was introduced. First, a series of micro-topographic factors, such as slope, aspect, and topographic relief, are extracted from the unified high-resolution base DEM data to quantitatively describe the impact of terrain on wind, temperature, and humidity in the transmission line corridor area. These micro-topographic factors are mathematical representations of the micro-physical characteristics of the terrain. Then, using these micro-topographic factors as correction terms, further targeted adjustments are made to the mesoscale distribution field, which has already been corrected to conductor height. This correction achieves a refined characterization of the unique meteorological environment within micro-topographic units such as ridges, valleys, and slopes, thereby generating high spatiotemporal resolution reanalysis meteorological data for the target icy mountain area. Finally, based on the micro-topographic factors of the target icy mountain area and the high spatiotemporal resolution reanalysis meteorological data, a transmission line icing reanalysis database is constructed. This database organizes and stores the finely characterized topographic background and high-precision meteorological element time-series data at the conductor height level according to a unified data model, forming an integrated structured dataset of "line-topography-meteorology". The establishment of this database provides high-quality, highly consistent, and long-term time-series foundational data support for subsequent research on icing mechanisms, calculation of icing thickness models, dynamic assessment of icing risks, setting of early warning thresholds, and training of data-driven artificial intelligence models. The entire construction process, from data preprocessing, dynamic downscaling, vertical correction to micro-topography correction, forms a high-precision meteorological data reconstruction system covering both macro-climate background and micro-line environment. This effectively compensates for the insufficient spatial resolution and unrepresentative standard height elements inherent in existing operational methods that directly use coarse-resolution reanalysis products. Its operation and core lie in constructing a complete and systematic data processing and analysis workflow, aiming to generate a high spatiotemporal resolution reanalysis database specifically for assessing icing risks of transmission lines in complex mountainous areas.

[0051] In some example implementations, the raw DEM data and raw reanalysis meteorological data of the acquired complex mountainous areas are spatiotemporally aligned to obtain a base dataset, including: The latitude and longitude grid of the original DEM data in the complex mountainous area is projected and resampled to the target plane coordinate system to obtain the preprocessed elevation field; By performing projection mapping and spatial interpolation on the near-surface meteorological elements in the original reanalysis meteorological data of complex mountainous areas for the target plane coordinate system, a fine-grid meteorological field is obtained. The time step of the fine-grid meteorological field is converted to the target time step using linear time interpolation to obtain the preprocessed meteorological field. The basic dataset is obtained based on the preprocessed elevation field and the preprocessed meteorological field.

[0052] In this example implementation, the original DEM data and the original reanalysis meteorological data typically employ different data formats, spatial reference systems, and spatiotemporal resolutions. It is necessary to unify the original DEM raster and the ERA5 reanalysis meteorological field spatially and temporally within the same framework to lay the data foundation for subsequent coupling of micro-topographical factors and micro-meteorological factors. The first step is to perform projection mapping on the original DEM data and the original reanalysis meteorological data, transforming the original DEM data from the geographic coordinate system to a preset target plane coordinate system. The selection of the target plane coordinate system must consider the extent and shape of the study area; typically, an equal-area projection or an isometric projection is used to ensure that the distortion of distance, direction, and area within the analysis area is within an acceptable range. For example, the spatial projection and raster unification of the original DEM data and the original reanalysis meteorological data are first performed: First, the latitude and longitude grids of the original DEM and the original reanalysis meteorological data (such as ERA5) are uniformly projected onto the preset target plane coordinate system. Let the geographic longitude of any raster point be λ and the latitude be φ, then the projection transformation operator... The coordinates (x, y) mapped to the target plane coordinate system are:

[0053] Where: λ is the geographic longitude of the grid point; φ is the geographic latitude of the grid point; x is the east-west plane coordinate in the target plane coordinate system, i.e., the projected coordinate system; y is the north-south plane coordinate in the target plane coordinate system, i.e., the projected coordinate system. This is the projection transformation operator in the preset projection coordinate system.

[0054] In the target plane coordinate system / projected coordinate system, let the DEM elevation field be z(x, y) and any meteorological element field in ERA5 be q(x, y, t), where: z(x, y) is the ground elevation value at the projected coordinate (x, y); q(x, y, t) is the meteorological variable at the projected coordinate (x, y) and time t (e.g., 2 m air temperature, 2 m relative humidity, 10 m wind speed, etc.).

[0055] Then, a uniform regular grid is constructed in the (x, y) plane. Let the grid column index be i, the row index be j, and the corresponding grid center coordinates be... The grid spacing in the east-west and north-south directions is Δx (east-west) and Δy (north-south), respectively.

[0056] Next, spatial interpolation and resampling are performed. The original DEM elevation z(x, y) is resampled onto a uniform regular raster, denoted as the uniform elevation field. , can be represented as:

[0057] in: To unify the elevation value at the raster index (i, j) of the rule; For spatial resampling operators of DEM data, methods such as bilinear interpolation, bicubic interpolation, or neighborhood averaging can be used.

[0058] For the meteorological element field q(x, y, t), at each time t, through spatial interpolation weights... Mapping it onto a uniform rule grid yields a uniform weather field. :

[0059] in: To unify the rule of raster index (i, j) and meteorological variable interpolation at time t; The original reanalysis meteorological data (ERA5) raster index (m, n) and meteorological variable values ​​at time t; The weights of the original raster (m, n) in the spatial interpolation of the uniform raster (i, j) can be determined based on bilinear interpolation or inverse distance weighting; m is the column index of the original ERA5 raster; n is the row index of the original ERA5 raster.

[0060] Finally, time step unification and time interpolation are performed. Let the target time step required for icing analysis be Δt (which can be preset), and the corresponding target time series be { },in For time step indexing. For any uniform grid point When the target time Falling into two adjacent primitive times and In between, that is ≤ ≤ The following is obtained using linear time interpolation:

[0061] in: For grid (i, j) at the target time Interpolation of meteorological variables; For grid (i, j) at the original time Meteorological variable values; For grid (i, j) at the original time Meteorological variable values; For adjacent And earlier than The original time; For adjacent And later than The original time; This is the time interpolation weight, with a value range of 0 to 1.

[0062] Through the above unified spatial and temporal processing, a unified basic dataset of DEM–ERA5 with the same projection coordinate system, the same spatial resolution, and the same time step is obtained, providing a unified data base for the subsequent construction of micro-topography and micro-meteorological features.

[0063] For example, the micro-topographic factors include at least slope, aspect, topographic relief, topographic shielding, and wind corridor index; The terrain shielding degree is used to characterize the windward / leeward characteristics of different spatial locations of the target icy mountain area in different directions; The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area.

[0064] In this example implementation, micro-topographic factors are a systematic description system designed for key meteorological conditions for icing formation, particularly wind, temperature, and liquid water transport. Slope reflects the steepness of the land surface inclination and is a fundamental parameter for calculating the amount of solar radiation received by the surface, influencing near-surface airflow, and precipitation retention. Aspect indicates the direction the slope faces, directly affecting sunshine duration and intensity, thus causing temperature differences between sunny and shady slopes. Slope aspect also interacts with the prevailing wind direction, determining whether a location is a windward or leeward slope. Topographic relief quantifies the complexity and degree of elevation variation within a local area; areas with greater topographic relief experience more intense airflow disturbances and more complex local circulation. Topographic shielding characterizes the windward / leeward characteristics of different spatial locations within the target icing mountain area. It is a dynamic, multi-directional evaluation indicator designed to accurately quantify the degree to which a point is shielded by the surrounding terrain in three-dimensional space, directly related to the wind speed and liquid water capture efficiency required for icing. The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area. It is specifically designed to depict the dynamic modulation effect of topography on the near-surface wind field (such as the "funneling effect"). By constructing a micro-topographic factor system from basic geometric features to advanced meteorological dynamic features, static topographic information can be dynamically transformed into scientific estimates of wind field acceleration / deceleration and temperature increases / decreases, thereby achieving deep coupling between micro-topography and micro-meteorology at the data level.

[0065] For example, the calculation process for slope and aspect is as follows: in the unified DEM elevation field Above, calculate the east-west and north-south elevation gradients for each grid cell (i, j):

[0066] in: Let (i, j) be the east-west slope component of the terrain at grid (i, j). The north-south slope component of the terrain at grid (i, j); Let x be the partial derivative of elevation with respect to x; Let be the partial derivative of elevation with respect to y.

[0067] The slope s(i, j) at grid (i, j) can be written as:

[0068] Where s(i, j) is the slope at grid (i, j), reflecting the steepness of the terrain.

[0069] The slope aspect is obtained through the arctangent relationship of the slope components, such as:

[0070] in: Let be the slope angle at grid (i, j); The arctangent function with quadrant determination; symbol " "Used to ensure that the slope direction points in the direction of decreasing elevation."

[0071] The calculation process of terrain relief: To characterize the complexity and intensity of terrain undulation, a terrain relief index centered on a certain grid is introduced. Let the grid be... The neighborhood window centered on is There are N grid points in the window, and the average elevation within the window is... Then the grid undulation at the location It can be defined as the standard deviation of elevation:

[0072] in: For grid The terrain relief at the location; N is the neighborhood window. The total number of grid cells contained within; For window The average value of the internal elevation; The larger the value, the more complex and mountainous the terrain.

[0073] In some example implementations, the process of extracting the terrain shielding includes: For a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset, the terrain shielding degree of the spatial location is calculated by performing terrain shielding scans along multiple azimuth angles of the spatial location. The extraction process of the wind corridor index includes: For a spatial location within the transmission line corridor area of ​​a target icy mountainous region in the basic dataset, the wind corridor index of the spatial location is calculated based on the prevailing wind direction and topographic profile features extracted from the basic dataset.

[0074] In this example implementation, the calculation of terrain shielding is a systematic azimuth scanning and geometric analysis process. Its core idea is to assess the degree to which the target point's line of sight is obstructed by surrounding terrain within a 360-degree horizontal range. This process directly simulates the obstructive effect of terrain on horizontal airflows and weather systems from different directions. The calculation of the wind corridor index focuses more on analyzing the profile of the terrain in the prevailing wind direction to quantify its guiding and accelerating effects on airflow. This is crucial for understanding the key variable that increases wind speed during icing, affecting the collision efficiency of supercooled water droplets.

[0075] For example, the terrain shielding degree of the spatial location is calculated by performing terrain shielding scans along multiple azimuth angles, including: Centered on the spatial location, the terrain is scanned upstream along multiple azimuth angles of the spatial location to determine the height of the scanning point that blocks the incoming wind and the horizontal distance between the scanning point and the spatial location. Based on the height of the scanning point and the horizontal distance between the scanning point and the spatial location, the maximum shielding angle in the azimuth direction corresponding to the scanning point is determined; the maximum shielding angles in multiple azimuth directions are normalized to obtain the terrain shielding degree of the spatial location.

[0076] In this example implementation, terrain shielding is used to measure the "windward / leeward" exposure of a location in different orientations. A terrain scan can be performed on a preset set of azimuth angles (e.g., every 10° in each direction) starting from any grid. Let azimuth angle θ belong to set D, and the terrain scan can be performed on a grid... Starting from the azimuth angle θ, a series of discrete sampling points are taken sequentially upstream. The corresponding coordinates are Its horizontal distance from the starting point is:

[0077] in: For scan points Horizontal distance from the center of the starting grid; , For scan points Plane coordinates; , Starting grid The plane coordinates.

[0078] The corresponding elevation difference is:

[0079] in: For scan points The elevation difference from the starting point, For scan points Elevation value, Starting grid The elevation value.

[0080] Then, for all scanning points at the azimuth angle θ... Calculate the shading angle:

[0081] in: For scan points The shielding angle generated by the starting point in the azimuth direction θ; It is the arctangent function.

[0082] The maximum shielding angle at this azimuth angle θ is defined as:

[0083] in: For grid The maximum shading angle at azimuth angle θ.

[0084] To construct a terrain shielding index in the range of 0 to 1, the global maximum value of the absolute value of the maximum shielding angle over all azimuth angle sets D is used. As a reference value for normalization, we have:

[0085] in: For grid The terrain shielding index value at the location; Let D be the number of azimuth angles in the azimuth set. The absolute value of the maximum shielding angle obtained statistically across the entire study area (the transmission line corridor area of ​​the target icy mountainous region) and all azimuth angles is used as a normalization constant. The smaller the value, the more exposed and windward the location; the larger the value, the more it is blocked by the surrounding terrain and located in a leeward or recessed area.

[0086] For example, based on the prevailing wind direction and topographic profile features of the spatial location extracted from the base dataset, the wind corridor index of the spatial location is calculated, including: Based on historical statistical analysis of DEM data and reanalysis meteorological data in the aforementioned basic dataset, the prevailing wind direction in the transmission line corridor area is determined. The topographic profile along the transmission line is extracted along the prevailing wind direction in the transmission line corridor area to obtain the elevation difference characteristics, slope characteristics along the prevailing wind direction, and effective channel characteristics along the prevailing wind direction of the spatial location. Based on the weighted fusion of the elevation characteristics, windward slope characteristics, and effective windward channel characteristics of the spatial location, the wind corridor index of the spatial location is obtained. The effective channel feature along the prevailing wind direction is the aspect ratio of an effective channel with valley characteristics in the prevailing wind direction.

[0087] In this example implementation, a wind corridor index is introduced to characterize the guiding and constricting effects of valleys and ridges on incoming winds. First, based on the prevailing wind direction obtained from historical data using DEM and ERA5, a topographic profile is extracted along the prevailing wind direction and within a specified angle to its left and right. For raster data... Based on a prevailing wind profile centered on the center, calculate the local valley depth, width, and slope along the wind direction, and construct a dimensionless index:

[0088] in: For grid Wind corridor index at the location; , , These are the empirical weighting coefficients for elevation difference, slope, and wind corridor, respectively. This represents the elevation of a local ridgeline along the prevailing wind direction. This represents the elevation of the local valley floor along the prevailing wind direction; The reference height difference used for normalization; For grid Slope component along the prevailing wind direction; The effective channel length that exhibits distinct valley characteristics in the prevailing wind direction; This represents the average width of the corresponding channel. The larger the value, the more likely the location is to form a "wind corridor" effect, where wind speed contraction and acceleration occur.

[0089] In some example implementations, prior to performing the dynamic downscaling simulation, the following steps are also included: Based on the centralized reanalysis of meteorological data in the aforementioned basic dataset, the average meteorological field for each time period is calculated, and the reanalysis meteorological data at any moment in the aforementioned basic dataset is decomposed into the sum of the average meteorological field and the abnormal meteorological quantity for the corresponding time period. The abnormal meteorological data are subjected to spatiotemporal smoothing. Based on the smoothed abnormal meteorological data and the average meteorological field of each time period, smoothed meteorological data are obtained for further analysis.

[0090] In this example implementation, based on the original DEM-ERA5 spatial and temporal unification, climatological decomposition and anomaly smoothing are performed on the ERA5 dataset in the base dataset to construct a spatiotemporally continuous and physically consistent large-scale background field, which serves as the initial and boundary fields for subsequent dynamic downscaling of the WRF (Weather Research and Forecasting Model). For example, firstly, the ERA5 climatological mean field and anomaly field are decomposed: for the unified ERA5 meteorological variables... For example, temperature, humidity, wind speed, and wind direction are statistically averaged monthly based on years of ERA5 historical data. Let's assume... The month in question is m( If the climatic mean field is denoted as:

[0091] in: Here, m represents the climate average for grid (i, j) in month m; m is the month index; m( (time) The corresponding month index.

[0092] At any time The ERA5 unified field decomposition consists of "climatology + anomalies":

[0093] in: For grid (i, j), time Abnormalities (deviations from the climatic average).

[0094] Next, spatiotemporal smoothing of the anomalous field and construction of the background field are performed. To reduce occasional anomalous noise points in ERA5, the anomalous field is... Applying a joint spatiotemporal smoothing operator A smooth anomaly field is obtained. :

[0095] in: A spatiotemporal smoothing operator for performing weighted averaging within a certain spatiotemporal neighborhood; This represents the smoothed outliers. Then, a large-scale background field for ERA5 is constructed. :

[0096] in: For grid (i, j), time The large-scale background field of ERA5 at the location.

[0097] In the absence of other reanalysis data (such as CRA40, FNL, etc.), This serves as the sole large-scale constraint field and also as the initial and boundary field source for subsequent dynamic downscaling of WRF models. This example provides a more stable and physically consistent large-scale background field and boundary conditions for subsequent dynamic downscaling simulations. While the original reanalysis data, such as ERA5, is of high quality, it may still contain some small- to medium-scale random fluctuations or analytical noise at individual time intervals. These fluctuations, when used as initial and boundary conditions for mesoscale models, may excite unnecessary high-frequency perturbations within the model, even leading to integral instability and affecting the robustness of the downscaling results. This example addresses this issue. Furthermore, separating climatological states and anomalies helps in better understanding and controlling the characteristics of the input field.

[0098] In some example implementations, reanalysis meteorological data from the base dataset is used as a large-scale background field and boundary conditions, and DEM data from the base dataset is used as a topographic forcing field to perform dynamic downscaling simulations to obtain the mesoscale meteorological field of the target glacier-covered mountain area, including: Configure a multi-nested grid for the preset mesoscale numerical weather prediction model, and map the DEM data in the basic dataset to each layer of the mesoscale numerical weather prediction model through spatial interpolation to generate the model topographic height field. Vertical interpolation and variable transformation are performed on the reanalysis meteorological data in the basic dataset to generate the three-dimensional spatiotemporal dependent initial field and boundary conditions required by the mesoscale numerical weather prediction model. Using the terrain height field of the model as the underlying surface forcing, and the three-dimensional spatiotemporally dependent initial field and boundary conditions as the driving force and constraints, the mesoscale numerical weather prediction model is run, and the mesoscale meteorological field of the target icy mountain area is screened out on the inner grid of the mesoscale numerical weather prediction model.

[0099] In this example implementation, a mesoscale numerical weather prediction model (WRF) is used, with DEM topography and ERA5 large-scale background field as the basis. Using this as input, the model achieves dynamic downscaling from a coarse-resolution ERA5 to a regionally high-resolution 3D meteorological field, characterizing the dynamic-thermal effects of complex mountainous terrain on temperature, humidity, and wind fields. The first step is model preprocessing and configuration. The computational domain and grid structure need to be configured for the selected mesoscale model. Considering computational efficiency and resolution requirements, a multi-nested grid technique is typically used. An outer, large-scale grid (parent domain) with relatively coarse resolution is set to accommodate the large-scale background field; one or more higher-resolution sub-grids are nested within, with the sub-grids focusing on the target icy mountainous area. For example, in a unified projected coordinate system, a multi-nested grid can be configured, such as an outer d1 (horizontal resolution approximately 9 km), a middle d2 (approximately 3 km), and an inner d3 (approximately 1-3 km). The second step is preparing the model's initial and boundary conditions. Mesoscale models require 3D atmospheric state variables (such as temperature, humidity, wind vectors, and air pressure) as the initial field, and during the simulation integration process, meteorological fields at the lateral and upper boundaries need to be continuously provided from the outside to constrain the evolution of large-scale circulation, ensuring it does not deviate from actual weather processes. These data originate from the reanalysis meteorological field in the base dataset. However, the variables and vertical layers provided by the reanalysis data are not entirely consistent with those required by the model. Therefore, data preprocessing is necessary, including vertical interpolation and variable transformation. Vertical interpolation interpolates the reanalysis data from its own pressure or height layers to a series of model height layers defined by the model, constructing a three-dimensional spatial distribution. Variable transformation calculates or converts variables in the reanalysis data (such as potential temperature, specific humidity, wind components, etc.) into input variables directly usable by the model, according to model requirements. For example, for each grid layer, the WRF model internally uses a unified elevation field based on the DEM. Generating mode terrain height field :

[0100] in: This represents the terrain height value used in the corresponding grid for the WRF mode; This is the spatial interpolation operator from the DEM to the model mesh. The initial field and boundary conditions required by the WRF model are derived from the large-scale background field of ERA5. Obtained through vertical interpolation and variable transformation, and collectively denoted as:

[0101] in: It is a vector field containing multiple variables such as temperature, humidity, and wind vector; ℓ represents the geometric height or equivalent height of the ℓth vertical layer of the model; ℓ is the vertical layer index. This nesting method allows large-scale information to be transferred from the parent domain to the child domain, while the child domain can characterize terrain details at high resolution. The topographic field is the underlying surface forcing source in the model's dynamic framework, determining the spatial distribution of pressure field, wind field disturbances, and thermal effects in the model's bottom layer. High-precision input topography is crucial for simulating accurate local circulations such as valley winds and slope winds. The third step is to run the model and extract the results. Using the previously generated topographic height field as the static underlying surface forcing, and the processed three-dimensional spatiotemporally dependent initial field and boundary conditions as the dynamic drivers and constraints, the mesoscale numerical model is started for time integration. The model's governing equations (including momentum equations, thermodynamic equations, continuity equations, water vapor equations, etc.) are numerically solved under a given physical parameterization scheme (such as the boundary layer scheme, microphysics scheme, radiation scheme, and land surface process scheme), thereby simulating the weather evolution process from large-scale to mesoscale. During integration, complex terrain significantly influences the fine structure of wind, temperature, and humidity fields within the simulated area through dynamic uplift / sinking, friction effects, and thermal inhomogeneities. After model integration, the model outputs three-dimensional meteorological variable fields at each time step across all grid layers (including the inner high-resolution grid). From these outputs, the meteorological field covering the target glacier area on the innermost high-resolution grid (i.e., a 1 km or higher resolution grid) is selected as the obtained mesoscale meteorological field. These meteorological variable fields include, but are not limited to, three-dimensional wind speed components, air temperature, relative humidity, specific humidity, air pressure, and cloud and precipitation-related variables. Compared to the input coarse-resolution reanalysis data, these mesoscale meteorological fields are spatially more refined and can preliminarily reflect the differentiation of meteorological elements caused by topographic units such as ridges and valleys, such as increased wind speed on ridges and nighttime temperature inversions in valleys.

[0102] For example, on the innermost high-resolution mesh, the state variable vector of the WRF mode is denoted as:

[0103] in: This is a vector of atmospheric state variables at horizontal coordinates (x, y), height h, and time t, including three-dimensional wind speed, temperature, specific humidity, etc.

[0104] The control equations for the WRF mode are written as follows:

[0105] in: For dynamic process operators, represents the contribution of the compressible fluid dynamics equations to the evolution of state variables; For physical parameterization operators, it represents the contribution of physical processes such as planetary boundary layer, microphysics, surface processes, and radiation to state quantities; This is an external forcing term, mainly derived from the constraints of the large-scale background field of ERA5 on the boundary and the terrain forcing.

[0106] Integrating the above equation over a given integration step size yields the three-dimensional mesoscale field on the innermost high-resolution grid:

[0107] in: For high-resolution grid points ( ), height layer ,time The state variables of the mesoscale reanalysis are on the scale; the subscript M indicates the mesoscale result.

[0108] from The required meteorological variables, such as mesoscale temperature fields, can be extracted from it. Wind speed field This provides a basis for subsequent correction of conductor height.

[0109] In some example implementations, the mesoscale meteorological field includes a mesoscale temperature field, a mesoscale wind speed field, a mesoscale wind direction field, a mesoscale humidity field, and a mesoscale precipitation variable field; based on boundary layer theory, the mesoscale meteorological field is corrected for the vertical gradient of the transmission line at its actual height to obtain the mesoscale distribution field of each meteorological element at the conductor height layer, including: For each grid cell in the transmission line corridor region of the mesoscale temperature field, the vertical temperature gradient of the grid cell is calculated using boundary layer theory based on the temperature and height of the two adjacent model layers above and below the conductor height. Based on the vertical temperature gradient and the height difference between the conductor height and the lower model layer, the temperature of the lower model layer is linearly extrapolated to obtain the corrected temperature at the conductor height of the grid cell. For each grid cell in the transmission line corridor region of the mesoscale wind speed field, based on the boundary layer theory, the wind speed at any near-ground height is extrapolated to the conductor height using the power law exponent of the wind speed profile to obtain the corrected wind speed at the conductor height of the grid cell. The near-surface wind direction of the mesoscale wind field is taken as the wind direction of the conductor height layer; For the mesoscale humidity field and the mesoscale precipitation variable field, vertical direction diagnosis or extrapolation is performed for the conductor height to obtain the relative humidity and precipitation-related variables at the conductor height layer. Based on the corrected air temperature and corrected wind speed of each grid cell in the transmission line corridor area, as well as wind direction, relative humidity and precipitation-related variables at the conductor height layer, a mesoscale distribution field of meteorological elements at the conductor height layer is generated. The power-law exponent of the wind speed profile is determined based on the surface roughness of the underlying surface of the mesoscale wind speed field.

[0110] In this example implementation, since the conductor height (typically 20-50 meters) is located in the lower part of the atmospheric boundary layer, the meteorological conditions here differ significantly from the model's lowest layer (which may be tens of meters or even higher above the ground) or the standard output layer (such as 2 meters or 10 meters). Direct use of this information would lead to input errors in the icing model. This correction process is based on the fundamental principles of boundary layer meteorology and employs targeted processing methods for different meteorological elements.

[0111] For example, the physical basis for vertical temperature correction is that near-surface temperature typically varies approximately linearly with altitude, especially under near-neutral stratification conditions. The vertical gradient information output by the WRF can be directly used to dynamically correct the conductor height. This correction is applied to each horizontal grid cell in the transmission line corridor area, i.e., at the horizontal position of the conductor. Nearby, select the two mode height layers immediately above and below the conductor height. and The corresponding mesoscale temperatures are:

[0112] Then in time ,Location vertical temperature gradient It can be written as:

[0113] in: The vertical temperature gradient calculated based on WRF results; and The height of the conductor is the height of the two mode layers above and below it, satisfying... .

[0114] Then the height of the conductor mesoscale temperature at It can be written as:

[0115] in: For in position ,time conductor height The mesoscale temperature at that location.

[0116] Wind speed correction can be performed using a power-law profile, utilizing the near-surface wind speed and power-law exponent from the WRF to adjust the wind speed at a specific near-surface height. mesoscale wind speed Extrapolating to the height of the conductor, we get:

[0117] in: For in position ,time conductor height Mesoscale wind speed at the location; This refers to a near-surface reference height output by WRF. It is the power-law exponent of the wind speed profile, which is closely related to the roughness length of the underlying surface. The greater the roughness (e.g., in forests and cities), the more the surface roughness increases. The higher the value, the faster the wind speed increases with altitude; the lower the roughness (such as water surface, snow surface). The smaller the value. The roughness length field can be diagnosed based on the boundary layer parameterization scheme of the mesoscale model itself, or a representative value can be obtained by statistical analysis of meteorological tower observation data in the region.

[0118] Regarding wind direction, in the near-surface layer, the change in wind direction with altitude is usually small. Therefore, the wind direction of the mesoscale wind field at a certain near-surface height (e.g., in the model layer, it is close to the conductor height) can be directly taken as the wind direction of the conductor height layer.

[0119] For relative humidity and other factors, variables such as temperature and specific humidity output by the WRF can be directly diagnosed or approximately extrapolated at the conductor height. For example, for relative humidity and precipitation-related variables, their vertical distribution is significantly affected by microphysical processes such as phase change, evaporation, and condensation. Therefore, vertical diagnosis or simple extrapolation can be performed. For example, for relative humidity, the specific humidity and air temperature at the level near the conductor height can be used from the mesoscale model output, combined with the saturated vapor pressure formula, to re-diagnose the relative humidity at the conductor height. Alternatively, if the model's vertical layers are sufficiently dense, a linear interpolation method similar to that used for air temperature can be employed. For precipitation-related variables (such as precipitation intensity and liquid water flux), assuming that precipitation-related variables in the lower troposphere change gradually with height, the values ​​output by the model at the near-surface layer or a representative layer can be approximated as the values ​​at the conductor height layer after applying a possible height adjustment factor.

[0120] After completing the grid-by-grid vertical correction of the above meteorological elements, a mesoscale distribution field of each meteorological element at conductor height is obtained, covering the entire transmission line corridor area. This dataset is closer to the actual operating environment of the transmission line than the original mesoscale output. The entire vertical correction process relies closely on boundary layer theory, ensuring the physical rationality of the correction results and the accurate characterization of key meteorological elements for icing (low temperature, appropriate wind speed, high humidity, and liquid water). This example obtains the near-surface meteorological field at conductor height based on the WRF mesoscale results.

[0121] In some example implementations, the micro-topographic factors are used to correct the mesoscale distribution field of each meteorological element in the conductor height layer for local micro-scale topographic effects, generating high spatiotemporal resolution reanalysis meteorological data for the target icy mountain area, including: Based on the topographic relief, topographic shielding and wind corridor index in the micro-topographic factors, the temperature distribution field of the conductor height layer is microscale corrected to obtain the microscale corrected temperature distribution field of the conductor height layer. Based on the wind corridor index, terrain shielding degree and slope in the micro-topographic factors, the wind speed field at the conductor height layer is corrected for acceleration effect to obtain the micro-scale corrected wind speed distribution field at the conductor height layer. Based on the microscale corrected temperature distribution field and wind speed distribution field at the conductor height, the distribution fields of other meteorological elements at the conductor height are corrected by utilizing the correlation between different meteorological elements. Based on the correction results of the microscale corrected temperature distribution field, the microscale corrected wind speed distribution field, and the distribution fields of other meteorological elements at the conductor height, high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Other meteorological elements include at least relative humidity and precipitation-related variables.

[0122] In this example implementation, topographic-based microscale correction is key to achieving "micro-topography-micro-meteorology" coupling and is also the core element in dynamizing and functionalizing static topographic information to capture meteorological differences at scales of tens to hundreds of meters. While mesoscale models can reflect topographic forcing, their resolution (e.g., 1 km) still cannot resolve smaller micro-topographic features such as ridges, valleys, and slope aspect changes, which are precisely the important reasons for differences in icing on adjacent towers along the same route. Specifically, the first step is to perform microscale correction on the temperature distribution field. Local temperature variations are influenced by both topographic thermal effects and shading effects. The correction process utilizes three key micro-topographic factors to construct correction terms. Topographic relief reflects the degree of topographic fragmentation; areas with greater undulation have more complex local circulation and cold air runoff, potentially leading to colder valleys and relatively warmer slopes at night. Topographic shielding comprehensively characterizes the exposure level of a location; areas with high shielding (such as leeward slopes and deep valleys) may receive less solar radiation during the day, and cold air tends to accumulate at night, potentially resulting in lower temperatures; conversely, exposed ridges may have temperatures closer to the free atmosphere. The wind corridor index is related to dynamic processes; increased wind speed may lead to local dynamic cooling (adiabatic expansion) or hinder the formation of temperature inversions. By combining these three factors in a linear or nonlinear form using empirical coefficients, a microscale temperature correction is formed. This microscale temperature correction is then superimposed on the temperature distribution field at the conductor height level to obtain a microscale corrected temperature field. The corrected temperature field shows lower temperatures in valleys with high shielding and exhibits a possible cooling effect in wind corridor regions, thus better reflecting the actual thermal environment of mountain micro-topography.

[0123] Secondly, the microscale correction of the wind speed distribution field focuses on characterizing the acceleration effect caused by topography. Local wind speed enhancement is mainly related to the guiding and contracting effects of topography. The correction process primarily relies on three factors: the wind corridor index, topographic shielding, and slope. The wind corridor index is a core indicator directly measuring the acceleration potential of topography; the higher its value, the positive the correction (increased wind speed). The complement of topographic shielding (1 - topographic shielding) represents the degree of exposure; the higher the exposure, the more likely the wind speed is to approach or exceed the background value, and the correction may also be positive. Slope can affect the lifting or sinking of airflow, thus modulating wind speed. Similarly, a wind speed correction term can be constructed based on the wind corridor index, topographic shielding, and slope. This correction term is added to the wind speed distribution field at the conductor height level to obtain the microscale-corrected wind speed distribution field at the conductor height level. After correction, wind speed will be reasonably enhanced in terrains such as mountain passes, narrow valleys, and open ridges, more realistically reflecting the micro-topographic dynamic effects. The determination of the coefficients requires calibration and verification based on limited wind measurement towers or icing observation data along the route.

[0124] After correcting for the two core dynamic and thermodynamic elements, temperature and wind speed, it is necessary to perform consistency corrections on other elements based on the physical relationships between meteorological elements. For example, changes in relative humidity are closely related to temperature. When temperature is corrected (e.g., the temperature in a valley is lowered), if the water vapor content (specific humidity) in the air does not change significantly at the microscale (or can be obtained from the model background field), then according to the characteristic that saturated vapor pressure decreases with decreasing temperature, the relative humidity at that point will automatically increase. Therefore, the corrected temperature and the specific humidity provided by the model (or a simply adjusted specific humidity) can be used to re-diagnose the relative humidity at the conductor height. For precipitation-related variables, their microscale changes are more complex and may be related to orographic uplift (slope, aspect) and local circulation. For example, based on factors such as topographic shielding and aspect, the mesoscale precipitation field can be corrected with topographic enhancement factors, such as increasing precipitation intensity on the windward slope (low shielding, facing the prevailing wind direction) and decreasing it on the leeward slope. Finally, all the microscale-corrected meteorological element fields are integrated to generate the final high spatiotemporal resolution reanalysis meteorological data for the target glacial mountain area. This dataset spatially distinguishes variations caused by micro-topography, maintains long-term sequence continuity temporally, corresponds to the conductor layer of transmission lines in terms of altitude, and has undergone physical consistency adjustments among its various elements. It represents the most refined meteorological product closely resembling the actual microenvironment of transmission lines, obtained from a large-scale climate background after dynamic downscaling, vertical correction, and micro-topography correction. This provides a highly valuable data foundation for subsequent icing simulation, risk assessment, and early warning. Micro-topographic factors are used to perform micro-scale corrections on the mesoscale results, making the final results more closely reflect the complex icing microenvironment of mountainous terrain.

[0125] like Figure 2 As shown, this invention utilizes DEM + ERA5 as the sole external data source. Through large-scale background field construction, mesoscale dynamic downscaling, vertical correction of the conductor height, and micro-scale correction driven by micro-topography, a high-precision reanalysis meteorological data model of the icing scene closely approximating the conductor height is formed, achieving a systematic mapping from "large-scale ERA5 background field" to "conductor height – micro-meteorological elements". The steps include: 1) ERA5 large-scale background field construction; 2) ERA5-driven mesoscale dynamic downscaling; 3) vertical gradient correction of the conductor height and micro-scale correction driven by micro-topography; 4) high-precision reanalysis meteorological element output and feature update of the icing scene. This process, based on the constructed "line segmentation – micro-topography – ERA5 micro-meteorology" feature system, addresses the operational needs of icing by using DEM + ERA5 as the sole external data source. Through dynamic downscaling, vertical gradient correction, and micro-topography-driven micro-scale correction, it constructs a high-precision reanalysis meteorological data model for icing scenarios that closely approximates the conductor height and adapts to complex mountainous terrain. This achieves a systematic mapping from "large-scale ERA5 background field" to "conductor height – micro-meteorological elements".

[0126] In some example implementations, after extracting the micro-topographical factors of the transmission line corridor area from the base dataset, the method further includes: The power transmission line is divided into several line segments; For each line segment, a buffer is constructed for the line segment, and the statistics for each micro-topographic factor within the buffer are used as the micro-topographic factors of the line segment. Accordingly, before using the micro-topographic factors to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, the method further includes: By spatial interpolation, the mesoscale distribution fields of meteorological elements in the conductor height layer are mapped to each line segment, thus obtaining the mesoscale distribution fields of meteorological elements in each line segment at the conductor height layer.

[0127] In this example implementation, the transmission line is divided into several segments according to a preset length, using the centerline as a reference. The segment index is denoted as k. For each segment k, a buffer zone with a width of B centered on the line is constructed. Within this buffer zone, the aforementioned micro-topographic factors are statistically analyzed, such as: the average and extreme values ​​of slope, the dominant distribution of slope aspect, the average value of topographic relief, the average value of topographic shielding, and the average or maximum value of the wind corridor index. These statistical values ​​of micro-topographic factors are used as the micro-topographic factors for the corresponding line segment, thus forming the micro-topographic feature vector corresponding to line segment k.

[0128] in: The micro-topographic feature vector for segment k of the route; This represents the average gradient within the buffer zone k of the line segment; The dominant direction or average value of the slope aspect within the buffer zone k of the line segment; The average terrain undulation within the k-th buffer zone of the line segment; This represents the average terrain shielding within the k-th buffer zone of the line segment; The average or maximum value of the wind corridor index within the k-segment buffer zone of the line.

[0129] Correspondingly, in meteorological data processing, it is also necessary to associate gridded data with the line segments. Before correcting for local microscale topographic effects, the gridded field of the distribution field of each meteorological element at the conductor height level can be mapped to the line segments. For example, the mesoscale field of the conductor height can be mapped... , etc., through spatial interpolation weights Mapping to the center of line segment k, we obtain the mesoscale conductor height meteorological field for segment k:

[0130] in: For the line segment k in time Mesoscale temperature at the conductor height; For the line segment k in time Mesoscale wind speed at the height of the conductor; The spatial interpolation weights of the grid (i, j) for line segment k.

[0131] Therefore, the micro-scale correction process driven by micro-topography based on the line segmentation dimension is as follows: Construct linear or nonlinear microscale correction terms. Taking temperature and wind speed as an example, define the temperature microscale correction term. With wind speed microscale correction for:

[0132] in: For the line segment k in time Microscale temperature correction at conductor height; For the line segment k in time Microscale correction for wind speed at conductor height; This represents the average terrain shielding within the k-th buffer zone of the line segment; The average or maximum value of the wind corridor index within the k-segment buffer zone of the line; This represents the average gradient within the buffer zone k of the line segment; Empirical coefficients related to temperature correction; These are empirical coefficients related to wind speed correction. After obtaining the microscale correction, the mesoscale conductor height, temperature, and wind speed can be updated to the microscale corrected results:

[0133] in: For the microscale corrected line segment k in time High-precision reanalysis temperature at conductor height; For the microscale corrected line segment k in time High-precision reanalysis of wind speed at conductor height.

[0134] Relative humidity and precipitation-related variables (represented by precipitation intensity or precipitation flux) can be re-diagnosed or corrected based on microscale-corrected variables such as temperature and wind speed to maintain consistency among elements.

[0135] In some example implementations, the mesoscale distribution field of each meteorological element in the conductor height layer also includes the mesoscale liquid water content distribution field of the conductor height layer; The liquid water content distribution field of the conductor height layer is determined based on the air temperature distribution field, the relative humidity distribution field, and the precipitation-related variable distribution field of the conductor height layer. Accordingly, the high spatiotemporal resolution reanalysis meteorological data also includes the correction results of the liquid water content distribution field in the conductor height layer; The correction results for the liquid water content distribution field of the conductor height layer are determined based on the correction results of the air temperature distribution field of the conductor height layer after microscale correction, the correction results of the relative humidity distribution field of the conductor height layer, and the correction results of the precipitation-related variable distribution field.

[0136] In this example implementation, liquid water content is incorporated into the high-precision reanalysis data system. Liquid water content is one of the most direct and critical meteorological parameters for icing formation, representing the amount of supercooled liquid water in the air that can be captured and frozen into ice by the conductor. However, neither large-scale reanalysis data nor mesoscale model outputs typically provide this variable directly, especially the liquid water content at the near-surface conductor height. Therefore, diagnostic estimation based on other available meteorological elements is necessary. First, the mesoscale distribution fields of various meteorological elements at the conductor height, obtained after the vertical gradient correction stage, include not only air temperature, wind speed, and relative humidity, but also a mesoscale liquid water content distribution field at the conductor height. This mesoscale liquid water content distribution field at the conductor height is not directly derived from the model output but is diagnosed based on other element fields already obtained at the conductor height. The physical basis for this diagnosis is that liquid water content is closely related to the presence of liquid precipitation (or supercooled cloud droplets), ambient humidity, and temperature conditions. A specific diagnostic model can be constructed using the air temperature distribution field, relative humidity distribution field, and precipitation-related variable distribution field at the conductor height. Precipitation-related variables can include near-surface precipitation intensity, rainwater mixing ratio, or cloud liquid water path from model outputs. The diagnostic model aims to capture the following pattern: in areas and time periods where temperatures are near or slightly below 0°C (supercooled water conditions), relative humidity is high (near saturation, conducive to cloud or fog droplet existence), and precipitation occurs or cloud water conditions are abundant, the liquid water content (LWC) at the conductor height is higher. By linking temperature, humidity, and precipitation-related variables through an appropriate functional relationship, the spatial distribution of LWC at the conductor height under a mesoscale background can be preliminarily estimated. Secondly, after completing the micro-topographic-driven correction of core meteorological elements (temperature, wind speed, etc.), the final high spatiotemporal resolution reanalysis meteorological data also includes the corrected results of the liquid water content distribution field at the conductor height. That is, the diagnostic value of LWC also needs to be updated according to the micro-topographic effect to maintain consistency with the corrected environmental field. The logic of the correction is that micro-topography not only affects temperature and wind speed but also indirectly affects the microscale distribution of humidity and precipitation, thereby affecting LWC. Therefore, LWC correction should not be performed independently, but should be based on a re-diagnosis of other element fields after microscale correction. That is, the corrected results of the microscale-corrected temperature distribution field, relative humidity distribution field, and precipitation-related variable distribution field of the conductor height layer are used as inputs and substituted into the same LWC diagnostic model as described above. Recalculating the liquid water content of the conductor height layer yields the corrected result of the liquid water content distribution field of the conductor height layer.

[0137] For example, the diagnostic model of the liquid water content distribution field in the conductor height layer is as follows:

[0138] in, Diagnostic value of liquid water content in conductor height layer at time t for line segment k; The proportionality coefficient for diagnosing liquid water content; For line segment k, the precipitation-related variables at the conductor height layer at time t; The relative humidity of the conductor height layer at time t for line segment k; The conductor height and air temperature at time t are given for line segment k. It is a relative humidity weighting function used to characterize the degree of influence of different humidity on liquid water content. It can monotonically increase within a certain humidity range, reflecting that high humidity environment is more conducive to the existence of liquid water. This is a temperature weighting function used to characterize the influence of different temperatures on supercooled liquid water. Higher weights can be assigned to supercooled liquid water near 0°C, while lower weights can be assigned to temperatures significantly below or above 0°C. This can be achieved through appropriate selection... and The function form can ensure The values ​​are larger under conditions of high humidity, near-0°C temperature, and precipitation, which better meet the conditions required for icing formation.

[0139] In some example implementations, such as Figure 3 As shown, after completing the construction of "line segmentation - micro-topographic factors", it is necessary to map the ERA5 near-surface meteorological field to the line segment location and extrapolate it to the conductor height, while diagnosing the icing-sensitive liquid water content (LWC), thereby forming the time series characteristics of "line segmentation - micro-meteorological factors".

[0140] First, the spatial mapping between line segments and the ERA5 grid: After unification, the ERA5 weather field Above, based on the center coordinates of line segment k Spatial interpolation is used to map ERA5 raster values ​​to the segment location. Let a meteorological variable be q (e.g., 2 m air temperature or 10 m wind speed), then the value of this variable at time t at segment k can be written as:

[0141] in: The meteorological variable values ​​for line segment k at time t; The meteorological variable values ​​at time t in the unified ERA5 raster (i, j); The weight of line segment k in spatial interpolation for ERA5 grid (i, j) can be determined based on distance or area weights.

[0142] Secondly, the vertical correction for conductor height temperature and wind speed: Let the height of the conductor be 2 m height 10 m height is Under near-neutral stratification conditions, the conductor height temperature can be linearly extrapolated from the temperature at 2 m as follows:

[0143] in: For line segment k, at time t and conductor height Temperature at the location; For line segment k at time t and height 2 m Temperature at the location; This represents the vertical temperature lapse rate.

[0144] The wind speed at the conductor height can be extrapolated from the 10 m wind speed based on the power-law wind speed profile:

[0145] in: For line segment k, at time t and conductor height Wind speed at the location; Line segment k at time t, height 10 m Wind speed at the location; The height is 10 m; This represents the power-law exponent related to the surface roughness. Factors such as relative humidity and precipitation intensity can be directly obtained from the ERA5 interpolation results. Obtain, or perform simple height correction. Based on ERA5 precipitation-related variables, near-traverse temperature and humidity elements, and a liquid water content diagnostic model, assess liquid water content. To conduct a diagnosis.

[0146] Finally, the construction of the icing micro-topography-micro-meteorological feature vector is as follows: In each line segment k and at each time step t, the micro-terrain feature vector is... By splicing the micro-meteorological features near the conductor, an icing microenvironment feature vector is constructed:

[0147] in: The joint feature vector of icing micro-topography and micro-meteorology for line segment k at time t; This is the micro-topographic feature vector for this segment; These are the air temperature, relative humidity, wind speed at the conductor height, representative values ​​of precipitation intensity, and diagnostic values ​​of liquid water content at the conductor height.

[0148] This example demonstrates a systematic feature construction from raw DEM and ERA5 data to "line segmentation – micro-topographic factors – near-conductor micro-meteorological factors," providing a structured and directly accessible input feature system for building high-precision reanalysis meteorological data models for icing scenarios, as well as for constructing databases and service platforms. A corresponding database can also be constructed based on this icing microenvironment feature vector. The meteorological elements in this database are data specific to transmission line segments and have not undergone mesoscale meteorological field transformation or microscale correction, serving as a foundation for analysis data in conventional areas (flat terrain).

[0149] In some example implementations, a transmission line icing reanalysis database is constructed based on the micro-topographical factors of the target icing-covered mountain area and high spatiotemporal resolution reanalysis meteorological data, including: Using a pre-defined first-format data model, the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data are stored hierarchically using spatial grid cells and time steps as indexes.

[0150] In this example implementation, the first format is a storage format based on spatial grid cells. This format preserves the original spatial structure of the data and is suitable for scenarios requiring continuous spatial analysis, visualization, or as input for other spatial analysis tools. It treats the entire processing area as a two-dimensional plane composed of regular grid cells, each grid cell being associated with a set of attribute data at each time step. Running this storage model first requires establishing a unified spatial reference frame, namely the target plane projection coordinate system, and a unified regular grid defined within that coordinate system. This grid remains consistent throughout the entire database construction process, assuming the unified grid shares a common east-west direction. The column has a total of [number] columns in the north-south direction. Line, in the time dimension, there are a total of At time steps, the global conductor height reanalysis field can be represented as a set of four-dimensional arrays:

[0151] in: A high-precision reanalysis database for gridded icing scenarios; For grid points ,time A high-precision reanalysis of meteorological element sets for icing scenarios. To unify the total number of grid cells in the x-direction; To unify the total number of grid cells in the y-direction; is the total number of time steps within the historical period; i is the column index of the raster (east-west direction); j is the row index of the raster (north-south direction). For time step indexing.

[0152] To facilitate linear storage in the database, a raster linear index is defined. for:

[0153] in: This is the linear index number corresponding to grid (i, j), with a value ranging from 1 to... .

[0154] Four-dimensional data can then be logically mapped to a three-dimensional array:

[0155] in: For linear indexes ,time The high-precision reanalysis of the icing scene still contains various physical quantities, such as temperature, wind speed, relative humidity, and liquid water content. Layered storage can employ partitioning techniques, such as partitioning time-series data by year or month, storing data from different periods in different physical files or storage units. This facilitates data management and rapid access to data for specific time periods. The gridded storage model in this example constructs a database that fully preserves the spatial details of high-resolution data, supports flexible spatial queries and spatial analysis (such as contour plotting, spatial statistics, and buffer analysis), and has good compatibility with GIS (Geographic Information System) platforms, facilitating visualization and spatial overlay analysis. This storage method provides power grid planners and researchers with a powerful tool for examining the icing meteorological environment from macro to micro perspectives, and is one of the fundamental forms of databases as a "data foundation."

[0156] In some example implementations, a transmission line icing reanalysis database is constructed based on the micro-topographical factors of the target icing-covered mountain area and high spatiotemporal resolution reanalysis meteorological data, including: Using a pre-defined second-format data model, the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data are stored hierarchically, indexed by line segments and time steps.

[0157] In this example implementation, the second format data model is a data storage model oriented towards transmission line business applications, organized around line segments. This model directly aligns with the power system's operational model, which uses "lines" as the object of asset management and risk assessment, processing data into a form most convenient for line engineers and operation and maintenance systems to use. Similarly, high-precision feature vectors for line segments are generated. Define the line index function :

[0158] in: This is the index number of line segment k.

[0159] The historical route segmentation data sub-database can be considered as having a primary key. The collection of records:

[0160] in: This refers to the actual set of line segment records stored in the database. The total number of segments in the line. For time step indexing. (Through...) and By establishing composite indexes and auxiliary indexes along the variable dimensions, the database can support efficient time-space-variable joint retrieval. Hierarchical storage can refer to storing data from different lines separately (e.g., dividing databases or tables by voltage level or region), or separating high-frequency data (e.g., hourly data) from low-frequency aggregated data (e.g., daily maximum wind speed, daily minimum temperature, and cumulative precipitation) to meet different analytical needs. It can also involve hierarchical design of the original high-precision sequences and the sample feature library used for machine learning models.

[0161] In some example implementations, after constructing the transmission line icing reanalysis database, the method further includes: If an update to the original reanalysis meteorological data is detected, dynamic downscaling simulation, vertical gradient correction, and local microscale topographic effect correction are performed on the updated reanalysis meteorological data to obtain high spatiotemporal resolution meteorological update data. The transmission line icing reanalysis database is updated based on the high spatiotemporal resolution meteorological update data.

[0162] In this example implementation, it is necessary to continuously update and incrementally store high-precision reanalysis results for icing scenarios, following the release of new ERA5 reanalysis data. The first step is to identify the new time step. Let the latest available time step index in ERA5 be... The current maximum time step index in the database is Then a new set of time steps for computation needs to be added. for:

[0163] in: The latest time step index provided for ERA5; Index the largest time step already stored in the database; This is the set of time-step indexes for which incremental reanalysis and data insertion are required.

[0164] Then, incremental reanalysis and data writing are performed. For each new time step, DEM-ERA5 unified base data is constructed from the original field of the new ERA5 time step; and data is obtained based on the large-scale background field of ERA5 and the mesoscale dynamic downscaling of WRF. and Then, these new results are appended to the database record set:

[0165] in: For the actual set of grid records stored in the database, the symbol This means appending and updating the right set to the left set; This indicates a set union operation.

[0166] Through the aforementioned incremental update mechanism, the database can continuously expand the time dimension without repeatedly calculating historical periods.

[0167] In some implementations, considering multi-dimensional queries, this example designs a unified service interface. Based on the database data model, a standardized query request description and service interface are built to achieve efficient retrieval and service based on multi-dimensional combinations of conditions such as time, space, variables, and line segments.

[0168] First, the query request vector is defined as follows: Abstract a single query request into a query vector. Its form is:

[0169] in: The start time of the query; This is the end time of the query; The spatial query area can be a point, rectangle, arbitrary polygon, or a set of line segments. The selected set of variables, such as {temperature, wind speed, liquid water content, etc.}; This is the query mode type, used to distinguish between grid field queries and line segment queries.

[0170] Under time constraints, the corresponding set of time step indices It can be represented as:

[0171] in: This is the set of time step indices that fall within the requested time range.

[0172] Secondly, the gridded data query result set: When query mode When specifying "grid mode", it is necessary to consider the spatial region. Determine the set of grid cells to be selected :

[0173] in: For spatial regions A set of linear grid indices within the grid.

[0174] The gridded query result set It can be written as:

[0175] in: This is the set of gridded data results returned by this query; For index ,time Only the set of variables is retained at this location. A high-precision reanalysis subset of elements.

[0176] Finally, the result set of the line segment data query: When query mode When specified as "line mode", the spatial area This can be represented as a set of line segment indexes. :

[0177] Then the set of results for line segment queries for:

[0178] in: This is the collection of line segmentation data results returned by this query; Segmenting the line ,time Only the set of variables is retained at this location. A high-precision subset of feature vectors. By providing... and The platform provides a unified interface description, enabling it to offer services to external business systems and users in the form of APIs, file exports, or visualization layers.

[0179] Based on the constructed icing micro-topography-micro-meteorological feature system and the high-precision reanalysis meteorological data model of icing scenarios, the high-precision reanalysis field of gridded traverse height is then applied. and high-precision feature vectors of line segments Standardized storage, rolling updates, and service-oriented encapsulation are implemented to construct a "High-Precision Reanalysis Database for Icing Micro-Topography and Micro-Meteorology" and an "Online Update Service Platform." This example, based on a unified projected coordinate system and a unified time step, logically layers and designs a data model for high-precision reanalysis meteorological data of icing scenarios, constructing a gridded reanalysis data sub-database and a line segment feature data sub-database, respectively.

[0180] In areas with complex terrain, directly using reanalysis products to calculate and assess line icing generally presents the following problems: (1) Insufficient spatial resolution leads to loss of terrain information: coarse grid reanalysis data cannot resolve micro-topographic features such as ridges, canyons, windward and leeward slopes, and it is difficult to reflect the wind speed increase / decrease effect and precipitation differences at different terrain locations; (2) Meteorological elements at standard height are difficult to represent the environment at the height of the conductor: There is a significant vertical difference between the air temperature at 2 m and the air temperature near the ground at the height of the conductor, and the wind speed at 10 m is also difficult to reflect the real wind field structure at the height of the conductor. Direct use will introduce a large systematic error. (3) The precipitation phase and liquid water content are not well described: The reanalysis product is relatively rough in its representation of precipitation phases such as rain, snow and freezing rain, and it is difficult to accurately reflect the key conditions required for ice formation, such as liquid water content and supercooled water droplets, which leads to a large deviation in the estimation of ice amount. (4) Lack of supporting long-term icing data: Due to terrain conditions and operation and maintenance costs, automatic stations and icing monitoring stations in mountainous areas are sparse, lacking long-term, continuous, and high spatiotemporal resolution icing-micrometeorological joint data covering the line corridor, making it difficult to support the study of icing mechanism and the training of data-driven models.

[0181] This invention proposes a method and system for constructing a high-precision reanalysis database for transmission line icing based on micro-topography-micro-meteorological coupling. Relying solely on digital elevation models (DEMs) and reanalysis data as input, it achieves high-precision reconstruction and long-term database accumulation of key meteorological elements related to conductor height, providing a unified data foundation for transmission line icing risk assessment, refined early warning, and large-scale model training. It overcomes the problems of existing reanalysis meteorological products, such as low spatial resolution, difficulty in depicting the true meteorological environment of micro-topography and conductor height in complex mountainous transmission lines, and lack of high-precision reanalysis basic data for icing operations.

[0182] This invention provides a method for constructing a high-precision reanalysis database for icing transmission lines based on micro-topography-micro-meteorology coupling. The method generally follows a three-layer architecture: "multi-source data → high-precision reanalysis modeling → database and service platform." It revolves around the construction of micro-scale meteorological characteristics and the high-precision reanalysis requirements of iced transmission lines, forming the following closed-loop technical route: acquisition of multi-source micro-topography / micro-meteorology data → construction and mechanism analysis of icing micro-region characteristic indicators → reanalysis modeling of "large-scale constraints – meso-scale simulation – micro-scale correction" → inversion of small-scale wind field and precipitation phases → evaluation and iterative optimization of high-precision reanalysis products → construction and service-based release of the micro-topography-micro-meteorology coupled database. Within this framework, as... Figure 4 As shown, the invention can be divided into three interconnected technical modules: (A) construction of an icing micro-topography-micro-meteorological feature system; (B) construction of a high-precision reanalysis meteorological data model for icing scenarios; and (C) construction of a high-precision reanalysis database and update service platform for icing micro-topography-micro-meteorology. Data is interconnected among these modules through unified spatial grids, time steps, and metadata specifications, realizing an integrated R&D approach from grid simulation → conductor height correction → line icing micro-area characterization → database support for business operations. This invention can be used for icing risk assessment of complex mountain transmission lines, refined icing early warning, and training and application of icing-related data-driven models.

[0183] Example 2 Based on the same inventive concept, this invention also provides a system for constructing a database for reanalysis of icing on power transmission lines in micro-topography and micro-meteorology, comprising: The preprocessing module is used to perform spatiotemporal alignment on the acquired raw digital elevation model (DEM) data and raw reanalysis meteorological data of complex mountainous areas to obtain the basic dataset. The downscaling module is used to perform dynamic downscaling simulation using reanalysis meteorological data in the base dataset as a large-scale background field and boundary conditions, and DEM data in the base dataset as a topographic forcing field, to obtain the mesoscale meteorological field of the target icy mountain area. The vertical correction module is used to perform vertical gradient correction of the mesoscale meteorological field based on the boundary layer theory for the actual height of the transmission line, so as to obtain the mesoscale distribution field of each meteorological element in the conductor height layer. The microscale correction module is used to extract micro-topographic factors of the transmission line corridor area from the basic dataset; and to use the micro-topographic factors to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, thereby generating high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area. A construction module is used to build a reanalysis database for icing of transmission lines based on the micro-topographic factors and high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area.

[0184] In one possible implementation, the micro-topographic factors include at least slope, aspect, topographic relief, topographic shielding, and wind corridor index. The terrain shielding degree is used to characterize the windward / leeward characteristics of different spatial locations of the target icy mountain area in different directions; The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area.

[0185] In one possible implementation, the microscale correction module includes a terrain factor extraction submodule, which comprises: The shielding calculation subunit is used to calculate the terrain shielding of a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset by performing terrain shielding scans along multiple azimuth angles of the spatial location. The wind corridor calculation subunit is used to calculate the wind corridor index of a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset, based on the prevailing wind direction and topographic profile features of the spatial location extracted from the basic dataset.

[0186] In one possible implementation, the shielding calculation subunit is specifically used for: Centered on the spatial location, the terrain is scanned upstream along multiple azimuth angles of the spatial location to determine the height of the scanning point that blocks the incoming wind and the horizontal distance between the scanning point and the spatial location. Based on the height of the scanning point and the horizontal distance between the scanning point and the spatial location, the maximum shielding angle in the azimuth direction corresponding to the scanning point is determined; the maximum shielding angles in multiple azimuth directions are normalized to obtain the terrain shielding degree of the spatial location.

[0187] In one possible implementation, the wind corridor calculation subunit is specifically used for: Based on historical statistical analysis of DEM data and reanalysis meteorological data in the aforementioned basic dataset, the prevailing wind direction in the transmission line corridor area is determined. The topographic profile along the transmission line is extracted along the prevailing wind direction in the transmission line corridor area to obtain the elevation difference characteristics, slope characteristics along the prevailing wind direction, and effective channel characteristics along the prevailing wind direction of the spatial location. Based on the weighted fusion of the elevation characteristics, windward slope characteristics, and effective windward channel characteristics of the spatial location, the wind corridor index of the spatial location is obtained. The effective channel feature along the prevailing wind direction is the aspect ratio of an effective channel with valley characteristics in the prevailing wind direction.

[0188] In one possible implementation, the preprocessing module includes: The elevation preprocessing submodule is used to project and resample the latitude and longitude grid of the original DEM data of complex mountainous areas to the target plane coordinate system, so as to obtain the preprocessed elevation field. The meteorological preprocessing submodule is used to perform projection mapping and spatial interpolation on near-ground meteorological elements in the raw reanalysis meteorological data of complex mountainous areas to the target plane coordinate system, so as to obtain a fine-grid meteorological field; and to use linear time interpolation to transform the time step of the fine-grid meteorological field to the target time step, so as to obtain the preprocessed meteorological field. The dataset formation submodule is used to obtain the basic dataset based on the preprocessed elevation field and the preprocessed meteorological field.

[0189] In one possible implementation, the downscaling module is specifically used for: Configure a multi-nested grid for the preset mesoscale numerical weather prediction model, and map the DEM data in the basic dataset to each layer of the mesoscale numerical weather prediction model through spatial interpolation to generate the model topographic height field. Vertical interpolation and variable transformation are performed on the reanalysis meteorological data in the basic dataset to generate the three-dimensional spatiotemporal dependent initial field and boundary conditions required by the mesoscale numerical weather prediction model. Using the terrain height field of the model as the underlying surface forcing, and the three-dimensional spatiotemporally dependent initial field and boundary conditions as the driving force and constraints, the mesoscale numerical weather prediction model is run, and the mesoscale meteorological field of the target icy mountain area is screened out on the inner grid of the mesoscale numerical weather prediction model.

[0190] In one possible implementation, the mesoscale meteorological field includes a mesoscale temperature field, a mesoscale wind speed field, a mesoscale wind direction field, a mesoscale humidity field, and a mesoscale precipitation variable field; the vertical correction module includes: The temperature correction submodule is used to calculate the vertical temperature gradient of each grid cell in the transmission line corridor region of the mesoscale temperature field using boundary layer theory, based on the temperature and height of the two adjacent model layers above and below the conductor height; and to linearly extrapolate the temperature of the lower model layer based on the vertical temperature gradient and the height difference between the conductor height and the lower model layer to obtain the corrected temperature at the conductor height of the grid cell. The wind speed correction submodule is used to extrapolate the wind speed at any near-ground height to the conductor height for each grid cell in the transmission line corridor area of ​​the mesoscale wind speed field, based on boundary layer theory and using the power law exponent of the wind speed profile, to obtain the corrected wind speed at the conductor height of the grid cell. The diagnostic extrapolation submodule is used to take the near-surface wind direction of the mesoscale wind direction field as the wind direction of the conductor height layer; for the mesoscale humidity field and the mesoscale precipitation variable field, vertical direction diagnosis or extrapolation is performed for the conductor height to obtain the relative humidity and precipitation-related variables of the conductor height layer. The distribution field generation submodule is used to generate the mesoscale distribution field of various meteorological elements at the conductor height layer based on the corrected air temperature and corrected wind speed of each grid cell in the transmission line corridor area, as well as wind direction, relative humidity and precipitation-related variables at the conductor height layer. The power-law exponent of the wind speed profile is determined based on the surface roughness of the underlying surface of the mesoscale wind speed field.

[0191] In one possible implementation, the microscale correction module includes a scale correction submodule, the scale correction submodule being used for: Based on the topographic relief, topographic shielding and wind corridor index in the micro-topographic factors, the temperature distribution field of the conductor height layer is microscale corrected to obtain the microscale corrected temperature distribution field of the conductor height layer. Based on the wind corridor index, terrain shielding degree and slope in the micro-topographic factors, the wind speed field at the conductor height layer is corrected for acceleration effect to obtain the micro-scale corrected wind speed distribution field at the conductor height layer. Based on the microscale corrected temperature distribution field and wind speed distribution field at the conductor height, the distribution fields of other meteorological elements at the conductor height are corrected by utilizing the correlation between different meteorological elements. Based on the correction results of the microscale corrected temperature distribution field, the microscale corrected wind speed distribution field, and the distribution fields of other meteorological elements at the conductor height, high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Other meteorological elements include at least relative humidity and precipitation-related variables.

[0192] In one possible implementation, the microscale correction module further includes: The segmented statistics submodule is used to divide the transmission line into several line segments; for each line segment, a buffer is constructed for the line segment, and the statistics for each micro-topographic factor in the buffer are used as the micro-topographic factors of the line segment. The segmented mapping submodule is used to map the mesoscale distribution field of each meteorological element in the conductor height layer to each line segment through spatial interpolation, so as to obtain the mesoscale distribution field of each meteorological element in each line segment in the conductor height layer.

[0193] In one possible implementation, the mesoscale distribution field of each meteorological element in the conductor height layer also includes the mesoscale liquid water content distribution field of the conductor height layer. The liquid water content distribution field of the conductor height layer is determined based on the air temperature distribution field, the relative humidity distribution field, and the precipitation-related variable distribution field of the conductor height layer. Accordingly, the high spatiotemporal resolution reanalysis meteorological data also includes the correction results of the liquid water content distribution field in the conductor height layer; The correction results for the liquid water content distribution field of the conductor height layer are determined based on the correction results of the air temperature distribution field of the conductor height layer after microscale correction, the correction results of the relative humidity distribution field of the conductor height layer, and the correction results of the precipitation-related variable distribution field.

[0194] In one possible implementation, the diagnostic model for the liquid water content distribution field of the conductor height layer is as follows:

[0195] in, Diagnostic value of liquid water content in conductor height layer at time t for line segment k; The proportionality coefficient for diagnosing liquid water content; For line segment k, the precipitation-related variables at the conductor height layer at time t; The relative humidity of the conductor height layer at time t for line segment k; The conductor height and air temperature at time t are given for line segment k. This is a relative humidity weighting function used to characterize the degree of influence of different humidity levels on liquid water content; This is a temperature weighting function used to characterize the degree of influence of different temperatures on supercooled liquid water.

[0196] In one possible implementation, it further includes: a data smoothing module, the data smoothing module being used for: Based on the centralized reanalysis of meteorological data in the aforementioned basic dataset, the average meteorological field for each time period is calculated, and the reanalysis meteorological data at any moment in the aforementioned basic dataset is decomposed into the sum of the average meteorological field and the abnormal meteorological quantity for the corresponding time period. The abnormal meteorological data are subjected to spatiotemporal smoothing. Based on the smoothed abnormal meteorological data and the average meteorological field of each time period, smoothed meteorological data are obtained for further analysis.

[0197] In one possible implementation, the building module includes: The first construction submodule is used to store the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data in a hierarchical manner using a preset first format data model, with spatial grid cells and time steps as indexes.

[0198] In one possible implementation, the building module includes: The second construction submodule is used to store the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data in a hierarchical manner using a preset second-format data model, indexed by line segments and time steps.

[0199] In one possible implementation, it also includes: The data update module is used to perform dynamic downscaling simulation, vertical gradient correction, and local microscale topographic effect correction on the updated reanalysis meteorological data if an update to the original reanalysis meteorological data is detected, so as to obtain high spatiotemporal resolution meteorological update data; and to update the transmission line icing reanalysis database based on the high spatiotemporal resolution meteorological update data.

[0200] Example 3 like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0201] 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, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the micro-topography and micro-meteorology transmission line icing reanalysis database construction method in the above embodiments.

[0202] Example 4 Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). An electronic device readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both the built-in storage medium of the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which 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 executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the method for constructing a micro-topography and micro-meteorology transmission line icing reanalysis database in the above embodiments.

[0203] 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 media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0204] 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.

[0205] 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 1The function specified in one or more boxes.

[0206] 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.

[0207] 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 its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A method for constructing a database for reanalysis of icing on power transmission lines in micro-topography and micro-meteorology, characterized in that, include: Spatiotemporal alignment was performed on the original digital elevation model (DEM) data and the original reanalysis meteorological data of the acquired complex mountainous area to obtain the basic dataset; Using the reanalysis meteorological data in the aforementioned basic dataset as the large-scale background field and boundary conditions, and the DEM data in the aforementioned basic dataset as the topographic forcing field, dynamic downscaling simulation is performed to obtain the mesoscale meteorological field of the target icy mountain area. Based on boundary layer theory, the vertical gradient of the mesoscale meteorological field is corrected for the actual height of the transmission line to obtain the mesoscale distribution field of each meteorological element in the conductor height layer. Extract micro-topographic factors for the transmission line corridor region from the aforementioned base dataset; The micro-topographic factors are used to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, generating high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area. Based on the micro-topographic factors of the target icy mountain area and the high spatiotemporal resolution reanalysis meteorological data, a reanalysis database of icing on transmission lines is constructed.

2. The method according to claim 1, characterized in that, The micro-topographic factors include at least slope, aspect, topographic relief, topographic shielding, and wind corridor index; The terrain shielding degree is used to characterize the windward / leeward characteristics of different spatial locations of the target icy mountain area in different directions; The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area.

3. The method according to claim 2, characterized in that, The process of extracting the terrain shielding degree includes: For a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset, the terrain shielding degree of the spatial location is calculated by performing terrain shielding scans along multiple azimuth angles of the spatial location. The extraction process of the wind corridor index includes: For a spatial location within the transmission line corridor area of ​​a target icy mountainous region in the basic dataset, the wind corridor index of the spatial location is calculated based on the prevailing wind direction and topographic profile features extracted from the basic dataset.

4. The method according to claim 3, characterized in that, The terrain shielding degree of the spatial location is calculated by performing terrain shielding scans along multiple azimuth angles, including: Centered on the spatial location, the terrain is scanned upstream along multiple azimuth angles of the spatial location to determine the height of the scanning point that blocks the incoming wind and the horizontal distance between the scanning point and the spatial location. Based on the height of the scanning point and the horizontal distance between the scanning point and the spatial location, the maximum shielding angle in the azimuth direction corresponding to the scanning point is determined; the maximum shielding angles in multiple azimuth directions are normalized to obtain the terrain shielding degree of the spatial location.

5. The method according to claim 3, characterized in that, Based on the prevailing wind direction and topographic profile features of the spatial location extracted from the base dataset, the wind corridor index of the spatial location is calculated, including: Based on historical statistical analysis of DEM data and reanalysis meteorological data in the aforementioned basic dataset, the prevailing wind direction in the transmission line corridor area is determined. The topographic profile along the transmission line is extracted along the prevailing wind direction in the transmission line corridor area to obtain the elevation difference characteristics, slope characteristics along the prevailing wind direction, and effective channel characteristics along the prevailing wind direction of the spatial location. Based on the weighted fusion of the elevation characteristics, windward slope characteristics, and effective windward channel characteristics of the spatial location, the wind corridor index of the spatial location is obtained. The effective channel feature along the prevailing wind direction is the aspect ratio of an effective channel with valley characteristics in the prevailing wind direction.

6. The method according to claim 1, characterized in that, Spatiotemporal alignment was performed on the raw DEM data and raw reanalysis meteorological data of the acquired complex mountainous area to obtain the basic dataset, including: The latitude and longitude grid of the original DEM data in the complex mountainous area is projected and resampled to the target plane coordinate system to obtain the preprocessed elevation field; By performing projection mapping and spatial interpolation on the near-surface meteorological elements in the original reanalysis meteorological data of complex mountainous areas for the target plane coordinate system, a fine-grid meteorological field is obtained. The time step of the fine-grid meteorological field is converted to the target time step using linear time interpolation to obtain the preprocessed meteorological field. The basic dataset is obtained based on the preprocessed elevation field and the preprocessed meteorological field.

7. The method according to claim 2, characterized in that, Using the reanalysis meteorological data in the aforementioned basic dataset as the large-scale background field and boundary conditions, and the DEM data in the aforementioned basic dataset as the topographic forcing field, dynamic downscaling simulation is performed to obtain the mesoscale meteorological field of the target glaciated mountain area, including: Configure a multi-nested grid for the preset mesoscale numerical weather prediction model, and map the DEM data in the basic dataset to each layer of the mesoscale numerical weather prediction model through spatial interpolation to generate the model topographic height field. Vertical interpolation and variable transformation are performed on the reanalysis meteorological data in the basic dataset to generate the three-dimensional spatiotemporal dependent initial field and boundary conditions required by the mesoscale numerical weather prediction model. Using the terrain height field of the model as the underlying surface forcing, and the three-dimensional spatiotemporally dependent initial field and boundary conditions as the driving force and constraints, the mesoscale numerical weather prediction model is run, and the mesoscale meteorological field of the target icy mountain area is screened out on the inner grid of the mesoscale numerical weather prediction model.

8. The method according to claim 7, characterized in that, The mesoscale meteorological field includes a mesoscale temperature field, a mesoscale wind speed field, a mesoscale wind direction field, a mesoscale humidity field, and a mesoscale precipitation variable field. Based on boundary layer theory, the mesoscale meteorological field is corrected for the vertical gradient at the actual height of the transmission line to obtain the mesoscale distribution field of each meteorological element at the conductor height layer, including: For each grid cell in the transmission line corridor region of the mesoscale temperature field, the vertical temperature gradient of the grid cell is calculated using boundary layer theory based on the temperature and height of the two adjacent model layers above and below the conductor height. Based on the vertical temperature gradient and the height difference between the conductor height and the lower model layer, the temperature of the lower model layer is linearly extrapolated to obtain the corrected temperature at the conductor height of the grid cell. For each grid cell in the transmission line corridor region of the mesoscale wind speed field, based on the boundary layer theory, the wind speed at any near-ground height is extrapolated to the conductor height using the power law exponent of the wind speed profile to obtain the corrected wind speed at the conductor height of the grid cell. The near-surface wind direction of the mesoscale wind field is taken as the wind direction of the conductor height layer; For the mesoscale humidity field and the mesoscale precipitation variable field, vertical direction diagnosis or extrapolation is performed for the conductor height to obtain the relative humidity and precipitation-related variables at the conductor height layer. Based on the corrected air temperature and corrected wind speed of each grid cell in the transmission line corridor area, as well as wind direction, relative humidity and precipitation-related variables at the conductor height layer, a mesoscale distribution field of meteorological elements at the conductor height layer is generated. The power-law exponent of the wind speed profile is determined based on the surface roughness of the underlying surface of the mesoscale wind speed field.

9. The method according to claim 8, characterized in that, Using the aforementioned micro-topographic factors, the mesoscale distribution field of each meteorological element in the guide height layer is corrected for local micro-scale topographic effects, generating high spatiotemporal resolution reanalysis meteorological data for the target icy mountain area, including: Based on the topographic relief, topographic shielding and wind corridor index in the micro-topographic factors, the temperature distribution field of the conductor height layer is microscale corrected to obtain the microscale corrected temperature distribution field of the conductor height layer. Based on the wind corridor index, terrain shielding degree and slope in the micro-topographic factors, the wind speed field at the conductor height layer is corrected for acceleration effect to obtain the micro-scale corrected wind speed distribution field at the conductor height layer. Based on the microscale corrected temperature distribution field and wind speed distribution field at the conductor height, the distribution fields of other meteorological elements at the conductor height are corrected by utilizing the correlation between different meteorological elements. Based on the correction results of the microscale corrected temperature distribution field, the microscale corrected wind speed distribution field, and the distribution fields of other meteorological elements at the conductor height, high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Other meteorological elements include at least relative humidity and precipitation-related variables.

10. The method according to claim 9, characterized in that, After extracting the micro-topographic factors of the transmission line corridor area from the base dataset, the process also includes: The power transmission line is divided into several line segments; For each line segment, a buffer is constructed for the line segment, and the statistics for each micro-topographic factor within the buffer are used as the micro-topographic factors of the line segment. Accordingly, before using the micro-topographic factors to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, the method further includes: By spatial interpolation, the mesoscale distribution fields of meteorological elements in the conductor height layer are mapped to each line segment, thus obtaining the mesoscale distribution fields of meteorological elements in each line segment at the conductor height layer.

11. The method according to claim 10, characterized in that, The mesoscale distribution field of each meteorological element in the conductor height layer also includes the mesoscale liquid water content distribution field in the conductor height layer; The liquid water content distribution field of the conductor height layer is determined based on the air temperature distribution field, the relative humidity distribution field, and the precipitation-related variable distribution field of the conductor height layer. Accordingly, the high spatiotemporal resolution reanalysis meteorological data also includes the correction results of the liquid water content distribution field in the conductor height layer; The correction results for the liquid water content distribution field of the conductor height layer are determined based on the correction results of the air temperature distribution field of the conductor height layer after microscale correction, the correction results of the relative humidity distribution field of the conductor height layer, and the correction results of the precipitation-related variable distribution field.

12. The method according to claim 11, characterized in that, The diagnostic model for the liquid water content distribution field in the conductor height layer is as follows: in, Diagnostic value of liquid water content in conductor height layer at time t for line segment k; The proportionality coefficient for diagnosing liquid water content; For line segment k, the precipitation-related variables at the conductor height layer at time t; The relative humidity of the conductor height layer at time t for line segment k; The conductor height and air temperature at time t are given for line segment k. This is a relative humidity weighting function used to characterize the degree of influence of different humidity levels on liquid water content; This is a temperature weighting function used to characterize the degree of influence of different temperatures on supercooled liquid water.

13. The method according to claim 1 or 7, characterized in that, Prior to performing the dynamic downscaling simulation, the following is also included: Based on the centralized reanalysis of meteorological data in the aforementioned basic dataset, the average meteorological field for each time period is calculated, and the reanalysis meteorological data at any moment in the aforementioned basic dataset is decomposed into the sum of the average meteorological field and the abnormal meteorological quantity for the corresponding time period. The abnormal meteorological data are subjected to spatiotemporal smoothing. Based on the smoothed abnormal meteorological data and the average meteorological field of each time period, smoothed meteorological data are obtained for further analysis.

14. The method according to claim 1, characterized in that, Based on the micro-topographical factors and high spatiotemporal resolution reanalysis meteorological data of the target icing-covered mountain area, a transmission line icing reanalysis database is constructed, including: Using a pre-defined first-format data model, the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data are stored hierarchically using spatial grid cells and time steps as indexes.

15. The method according to claim 10, characterized in that, Based on the micro-topographical factors and high spatiotemporal resolution reanalysis meteorological data of the target icing-covered mountain area, a transmission line icing reanalysis database is constructed, including: Using a pre-defined second-format data model, the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data are stored hierarchically, indexed by line segments and time steps.

16. The method according to claim 1, characterized in that, After constructing the transmission line icing reanalysis database, the following is also included: If an update to the original reanalysis meteorological data is detected, dynamic downscaling simulation, vertical gradient correction, and local microscale topographic effect correction are performed on the updated reanalysis meteorological data to obtain high spatiotemporal resolution meteorological update data. The transmission line icing reanalysis database is updated based on the high spatiotemporal resolution meteorological update data.

17. A system for constructing a database for reanalysis of icing on power transmission lines in a micro-topographical and micro-meteorological context, characterized in that, include: The preprocessing module is used to perform spatiotemporal alignment on the acquired raw digital elevation model (DEM) data and raw reanalysis meteorological data of complex mountainous areas to obtain the basic dataset. The downscaling module is used to perform dynamic downscaling simulation using reanalysis meteorological data in the base dataset as a large-scale background field and boundary conditions, and DEM data in the base dataset as a topographic forcing field, to obtain the mesoscale meteorological field of the target icy mountain area. The vertical correction module is used to perform vertical gradient correction of the mesoscale meteorological field based on the boundary layer theory for the actual height of the transmission line, so as to obtain the mesoscale distribution field of each meteorological element in the conductor height layer. The micro-scale correction module is used to extract micro-topographic factors of the transmission line corridor area from the base dataset; The micro-topographic factors are used to correct the local micro-scale topographic effect of the mesoscale distribution field of each meteorological element in the conductor height layer, generating high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area. A construction module is used to build a reanalysis database for icing of transmission lines based on the micro-topographic factors and high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area.

18. The system according to claim 17, characterized in that, The micro-topographic factors include at least slope, aspect, topographic relief, topographic shielding, and wind corridor index; The terrain shielding degree is used to characterize the windward / leeward characteristics of different spatial locations of the target icy mountain area in different directions; The wind corridor index is used to characterize the guiding and contracting effects of topography on the wind field at various spatial locations in the target icy mountain area.

19. The system according to claim 18, characterized in that, The microscale correction module includes a terrain factor extraction submodule, which includes: The shielding calculation subunit is used to calculate the terrain shielding of a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset by performing terrain shielding scans along multiple azimuth angles of the spatial location. The wind corridor calculation subunit is used to calculate the wind corridor index of a spatial location within the transmission line corridor area of ​​the target icy mountainous region in the basic dataset, based on the prevailing wind direction and topographic profile features of the spatial location extracted from the basic dataset.

20. The system according to claim 19, characterized in that, The shielding calculation subunit is specifically used for: Centered on the spatial location, the terrain is scanned upstream along multiple azimuth angles of the spatial location to determine the height of the scanning point that blocks the incoming wind and the horizontal distance between the scanning point and the spatial location. Based on the height of the scanning point and the horizontal distance between the scanning point and the spatial location, the maximum shielding angle in the azimuth direction corresponding to the scanning point is determined; the maximum shielding angles in multiple azimuth directions are normalized to obtain the terrain shielding degree of the spatial location.

21. The system according to claim 19, characterized in that, The wind corridor calculation subunit is specifically used for: Based on historical statistical analysis of DEM data and reanalysis meteorological data in the aforementioned basic dataset, the prevailing wind direction in the transmission line corridor area is determined. The topographic profile along the transmission line is extracted along the prevailing wind direction in the transmission line corridor area to obtain the elevation difference characteristics, slope characteristics along the prevailing wind direction, and effective channel characteristics along the prevailing wind direction of the spatial location. Based on the weighted fusion of the elevation characteristics, windward slope characteristics, and effective windward channel characteristics of the spatial location, the wind corridor index of the spatial location is obtained. The effective channel feature along the prevailing wind direction is the aspect ratio of an effective channel with valley characteristics in the prevailing wind direction.

22. The system according to claim 17, characterized in that, The preprocessing module includes: The elevation preprocessing submodule is used to project and resample the latitude and longitude grid of the original DEM data of complex mountainous areas to the target plane coordinate system, so as to obtain the preprocessed elevation field. The meteorological preprocessing submodule is used to perform projection mapping and spatial interpolation on near-ground meteorological elements in the raw reanalysis meteorological data of complex mountainous areas to the target plane coordinate system, so as to obtain a fine-grid meteorological field; and to use linear time interpolation to transform the time step of the fine-grid meteorological field to the target time step, so as to obtain the preprocessed meteorological field. The dataset formation submodule is used to obtain the basic dataset based on the preprocessed elevation field and the preprocessed meteorological field.

23. The system according to claim 18, characterized in that, The downscaling module is specifically used for: Configure a multi-nested grid for the preset mesoscale numerical weather prediction model, and map the DEM data in the basic dataset to each layer of the mesoscale numerical weather prediction model through spatial interpolation to generate the model topographic height field. Vertical interpolation and variable transformation are performed on the reanalysis meteorological data in the basic dataset to generate the three-dimensional spatiotemporal dependent initial field and boundary conditions required by the mesoscale numerical weather prediction model. Using the terrain height field of the model as the underlying surface forcing, and the three-dimensional spatiotemporally dependent initial field and boundary conditions as the driving force and constraints, the mesoscale numerical weather prediction model is run, and the mesoscale meteorological field of the target icy mountain area is screened out on the inner grid of the mesoscale numerical weather prediction model.

24. The system according to claim 23, characterized in that, The mesoscale meteorological field includes a mesoscale temperature field, a mesoscale wind speed field, a mesoscale wind direction field, a mesoscale humidity field, and a mesoscale precipitation variable field; the vertical correction module includes: The temperature correction submodule is used to calculate the vertical temperature gradient of each grid cell in the transmission line corridor region of the mesoscale temperature field using boundary layer theory, based on the temperature and height of the two adjacent model layers above and below the conductor height; and to linearly extrapolate the temperature of the lower model layer based on the vertical temperature gradient and the height difference between the conductor height and the lower model layer to obtain the corrected temperature at the conductor height of the grid cell. The wind speed correction submodule is used to extrapolate the wind speed at any near-ground height to the conductor height for each grid cell in the transmission line corridor area of ​​the mesoscale wind speed field, based on boundary layer theory and using the power law exponent of the wind speed profile, to obtain the corrected wind speed at the conductor height of the grid cell. The diagnostic extrapolation submodule is used to take the near-surface wind direction of the mesoscale wind direction field as the wind direction of the conductor height layer; for the mesoscale humidity field and the mesoscale precipitation variable field, vertical direction diagnosis or extrapolation is performed for the conductor height to obtain the relative humidity and precipitation-related variables of the conductor height layer. The distribution field generation submodule is used to generate the mesoscale distribution field of various meteorological elements at the conductor height layer based on the corrected air temperature and corrected wind speed of each grid cell in the transmission line corridor area, as well as wind direction, relative humidity and precipitation-related variables at the conductor height layer. The power-law exponent of the wind speed profile is determined based on the surface roughness of the underlying surface of the mesoscale wind speed field.

25. The system according to claim 24, characterized in that, The microscale correction module includes a scale correction submodule, which is used for: Based on the topographic relief, topographic shielding and wind corridor index in the micro-topographic factors, the temperature distribution field of the conductor height layer is microscale corrected to obtain the microscale corrected temperature distribution field of the conductor height layer. Based on the wind corridor index, terrain shielding degree and slope in the micro-topographic factors, the wind speed field at the conductor height layer is corrected for acceleration effect to obtain the micro-scale corrected wind speed distribution field at the conductor height layer. Based on the microscale corrected temperature distribution field and wind speed distribution field at the conductor height, the distribution fields of other meteorological elements at the conductor height are corrected by utilizing the correlation between different meteorological elements. Based on the correction results of the microscale corrected temperature distribution field, the microscale corrected wind speed distribution field, and the distribution fields of other meteorological elements at the conductor height, high spatiotemporal resolution reanalysis meteorological data of the target icy mountain area are generated. Other meteorological elements include at least relative humidity and precipitation-related variables.

26. The system according to claim 25, characterized in that, The microscale correction module also includes: The segmented statistics submodule is used to divide the transmission line into several line segments; for each line segment, a buffer is constructed for the line segment, and the statistics for each micro-topographic factor in the buffer are used as the micro-topographic factors of the line segment. The segmented mapping submodule is used to map the mesoscale distribution field of each meteorological element in the conductor height layer to each line segment through spatial interpolation, so as to obtain the mesoscale distribution field of each meteorological element in each line segment in the conductor height layer.

27. The system according to claim 26, characterized in that, The mesoscale distribution field of each meteorological element in the conductor height layer also includes the mesoscale liquid water content distribution field in the conductor height layer; The liquid water content distribution field of the conductor height layer is determined based on the air temperature distribution field, the relative humidity distribution field, and the precipitation-related variable distribution field of the conductor height layer. Accordingly, the high spatiotemporal resolution reanalysis meteorological data also includes the correction results of the liquid water content distribution field in the conductor height layer; The correction results for the liquid water content distribution field of the conductor height layer are determined based on the correction results of the air temperature distribution field of the conductor height layer after microscale correction, the correction results of the relative humidity distribution field of the conductor height layer, and the correction results of the precipitation-related variable distribution field.

28. The system according to claim 27, characterized in that, The diagnostic model for the liquid water content distribution field in the conductor height layer is as follows: in, Diagnostic value of liquid water content in conductor height layer at time t for line segment k; The proportionality coefficient for diagnosing liquid water content; For line segment k, the precipitation-related variables at the conductor height layer at time t; The relative humidity of the conductor height layer at time t for line segment k; The conductor height and air temperature at time t are given for line segment k. This is a relative humidity weighting function used to characterize the degree of influence of different humidity levels on liquid water content; This is a temperature weighting function used to characterize the degree of influence of different temperatures on supercooled liquid water.

29. The system according to claim 17 or 23, characterized in that, Also includes: The data smoothing module is used for: Based on the centralized reanalysis of meteorological data in the aforementioned basic dataset, the average meteorological field for each time period is calculated, and the reanalysis meteorological data at any moment in the aforementioned basic dataset is decomposed into the sum of the average meteorological field and the abnormal meteorological quantity for the corresponding time period. The abnormal meteorological data are subjected to spatiotemporal smoothing. Based on the smoothed abnormal meteorological data and the average meteorological field of each time period, smoothed meteorological data are obtained for further analysis.

30. The system according to claim 17, characterized in that, The building module includes: The first construction submodule is used to store the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data in a hierarchical manner using a preset first format data model, with spatial grid cells and time steps as indexes.

31. The system according to claim 26, characterized in that, The building module includes: The second construction submodule is used to store the micro-topographic factors and the high spatiotemporal resolution reanalysis meteorological data in a hierarchical manner using a preset second-format data model, indexed by line segments and time steps.

32. The system according to claim 17, characterized in that, Also includes: The data update module is used to perform dynamic downscaling simulation, vertical gradient correction, and local microscale topographic effect correction on the updated reanalysis meteorological data if an update to the original reanalysis meteorological data is detected, so as to obtain high spatiotemporal resolution meteorological update data; and to update the transmission line icing reanalysis database based on the high spatiotemporal resolution meteorological update data.

33. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method as described in any one of claims 1 to 16 is implemented.

34. A readable storage medium, characterized in that, It contains an executable program, which, when executed, implements the method as described in any one of claims 1 to 16.

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