A power grid ice area map dynamic updating method, system, device and storage medium based on long-time satellite remote sensing

Abstract

The application discloses a kind of based on long time sequence satellite remote sensing's power grid ice area map dynamic updating method, system, equipment and storage medium, it is related to intelligent power grid and satellite remote sensing application technical field, method includes: obtaining long time sequence satellite image, synthesis year by year maximum ice thickness;Based on extreme value statistics, each recurrence period ice thickness is calculated, satellite inversion ice area map is generated;With current ice area map is compared with each pixel, deviation threshold value area is automatically identified and early warning;Combined with year by year new data, trigger local revision, realize ice area map rolling dynamic updating;The application can break through the limitation of ground observation fault, accurately calculate extreme icing extreme value, automatically early warning and dynamically correct ice area grade, reduce field cost, improve power grid anti-icing safety and disaster resistance time efficiency.

Description

Technical Field

[0001] This invention relates to the field of smart grid and satellite remote sensing application technology, and in particular to a method, system, device and storage medium for dynamic updating of power grid ice zone maps based on long-term satellite remote sensing. Background Technology

[0002] In regions with complex terrain and climate, winter power grid icing disasters have always been a top priority for line operation and maintenance and prevention. At present, the main basis for guiding line anti-icing design and emergency de-icing decisions is the power grid ice distribution map, and its practical value is highly dependent on the accuracy of the data. In conventional ice map mapping practices, the industry mainly relies on manual on-site ice observation, local deployment of online monitoring devices, or spatial interpolation calculations combining early line design data and meteorological station data.

[0003] However, these traditional methods have revealed significant limitations in practical applications: manual ice measurement is easily influenced by the observer's subjective experience, and the data suffers from gaps in its timeline, making it extremely difficult to support large-scale regional grading standards; online monitoring equipment, limited by high costs and complex operation and maintenance environments, can only be deployed sporadically on key towers, and the data typically only represents the accumulation of recent years, which is insufficient to provide adequate data support for extrapolating extreme ice conditions that occur once every few decades. As for traditional meteorological element interpolation methods, they often ignore the complex nonlinear influence of micro-topography and local micro-meteorology on icing conditions. In summary, due to the extreme spatial sparsity of existing observation networks and the severe lack of long-term continuous data, existing ice maps not only have numerous observational blind spots, but also struggle to accurately calculate extreme thicknesses that occur once every 30 or 100 years in the event of sudden extreme weather events. This static and lagging mapping mechanism is unable to keep up with recent climate change trends, posing a hidden danger to the safe and stable operation of the power grid. Meanwhile, existing methods for revising ice zone maps typically rely primarily on data from ground observation points, meteorological stations, or a limited number of historical line fault records. Their spatial representation often involves site extrapolation or empirical zoning. These methods struggle to establish continuous pixel-level ice thickness fields across large transmission corridors, especially in areas with complex micro-topography such as ridges, passes, windward and leeward slopes, valleys, and high-altitude transition zones. Ice thickness often exhibits significant local abrupt changes, and traditional interpolation methods are prone to over-smoothing, leading to an underestimation of local heavy icing areas. Furthermore, existing map revisions largely depend on periodic manual verification, resulting in low update frequencies and a lack of a closed-loop mechanism for automatically identifying deviations, delineating abnormal areas, and triggering local revisions based on newly added observation data. Therefore, even if a region experiences actual icing levels exceeding those on the current ice zone map for several consecutive years, this is difficult to reflect in map management and ice prevention design in a timely manner. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention provides a method, system, device and storage medium for dynamic updating of power grid ice area maps based on long-term satellite remote sensing.

[0005] Therefore, the technical problem solved by this invention is that existing power grid ice maps mainly rely on site observations, manual inspections, online monitoring devices, and meteorological interpolation results. These problems include insufficient long-term icing samples, poor spatial continuity over a large area, easy smoothing and underestimation of local icing extremes in micro-topographic regions, insufficient collaborative utilization of multi-source remote sensing data, and map updates relying on manual periodic revisions without a data-driven closed-loop triggering mechanism. This makes it difficult to achieve pixel-level icing thickness inversion, extreme return period estimation, and dynamic updating of ice maps for power transmission corridors.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for dynamically updating power grid ice zone maps based on long-term time-series satellite remote sensing, comprising: Acquire multi-source long-time satellite remote sensing images of the study area and construct a remote sensing image time series dataset; Based on the time series dataset of remote sensing images, the maximum ice thickness at the pixel scale is synthesized according to the winter ice-covering period, and the annual maximum ice thickness raster dataset is output. Based on the annual maximum ice thickness raster dataset, extreme value statistical analysis is performed on each raster cell to calculate the extreme value of ice thickness for a specified return period and generate a satellite-retrieved ice area map. The generated satellite-inverted ice zone map is compared pixel by pixel with the current benchmark ice zone map, and continuous areas with level deviations exceeding the preset threshold are automatically identified. The warning results, including the boundary of the abnormal area and the warning assessment report, are output. Based on the early warning results and combined with the latest annual data added year by year, the partial revision and approval process is proactively triggered to dynamically update the ice zone map.

[0007] As a preferred scheme for a dynamic update method of power grid ice zone maps based on long-term satellite remote sensing, the following is provided: The acquisition of multi-source long-term satellite remote sensing images of the study area, and the construction of a remote sensing image time series dataset, includes: Acquire multi-source long-term satellite remote sensing images of the study area, the images containing at least three types of data sources: the first type is medium-resolution optical images with daily revisit capability; the second type is radar images with cloud and fog penetration capability; and the third type is high-resolution images. Radiometric calibration, atmospheric correction, cloud masking, and unified geographic coordinate registration are performed on all acquired images in sequence to form a spatially aligned remote sensing image time series dataset with a time span of no less than a preset number of years, valid for each day or scene.

[0008] As a preferred scheme for a dynamic update method of power grid ice zone maps based on long-term satellite remote sensing, the following is provided: The remote sensing image time series dataset is used to synthesize the maximum ice thickness at the pixel scale according to the winter icing period, outputting a raster dataset of the maximum ice thickness for each year, including: Based on the time series dataset of remote sensing images, for all valid remote sensing images during each winter icing period, a transmission corridor buffer zone is defined by extending a preset width to both sides of the transmission line as the center. Within the power transmission corridor buffer zone, three types of features are extracted pixel by pixel. The first type is multi-band surface reflectance obtained from optical images and optical index features used to identify snow and ice and exclude vegetation. The second type is backscattering coefficients of different polarization modes obtained from radar images, radar vegetation indices used to distinguish ground targets, and contrast and entropy features based on texture analysis. The third type is prior geographical environmental features including elevation, slope, aspect, topographic relief, and distance to water systems.

[0009] As a preferred scheme for a dynamic update method of power grid ice zone maps based on long-term satellite remote sensing, the following is provided: The method for synthesizing the maximum ice thickness at the pixel scale based on the remote sensing image time series dataset, according to the winter icing period, and outputting a raster dataset of the maximum ice thickness for each year, also includes: The features extracted pixel by pixel are input into the pre-trained ice thickness inversion model, and the spatial distribution map of ice thickness is calculated scene by scene. The maximum value of each pixel in the distribution map of ice thickness of all single scenes within the same ice-covering period is taken to synthesize the raster dataset of the annual maximum ice thickness for each year.

[0010] As a preferred scheme for a dynamic update method of power grid ice zone maps based on long-term satellite remote sensing, the following is provided: The method, based on the annual maximum ice thickness raster dataset, performs extreme value statistical analysis on each raster cell, calculates the extreme value of ice thickness for a specified return period, and generates a satellite-retrieved ice area map, including: Using each raster cell as an independent calculation unit, the annual maximum ice thickness sequence of the cell is extracted during consecutive winter ice-covering periods. An extreme value probability distribution model was used to fit the annual maximum ice thickness sequence.

[0011] As a preferred scheme for a dynamic update method of power grid ice zone maps based on long-term satellite remote sensing, the following is provided: The method of performing extreme value statistical analysis on each raster cell based on the annual maximum ice thickness raster dataset, calculating the extreme value of ice thickness for a specified return period, and generating a satellite-retrieved ice area map also includes: For a given number of return periods, the extreme values ​​of ice thickness corresponding to the return period are inferred from the extreme value distribution parameters obtained by fitting. By sequentially traversing all raster pixels within the study area, a raster map of the spatial distribution of ice thickness under each return period is generated, i.e., a satellite-inverted ice area map.

[0012] As a preferred scheme for a dynamic update method of power grid ice zone maps based on long-term satellite remote sensing, the following is provided: The process involves comparing the generated satellite-retrieved ice zone map with the existing baseline ice zone map pixel by pixel, automatically identifying continuous areas where the level deviation exceeds a preset threshold, and outputting early warning results including the boundaries of abnormal areas and an early warning assessment report. The generated satellite-inverted ice zone map is spatially resampled and aligned with the existing baseline ice zone map, and the ice thickness values ​​in the satellite-inverted ice zone map are converted into the corresponding ice zone class. The ice zone level of the inverted layer is compared with the ice zone level of the reference layer pixel by pixel, and the level difference is calculated. When the level difference of a continuously distributed cluster of pixels exceeds a preset threshold, connected component analysis is triggered to extract the outer envelope vector boundary of the abnormal region. The outer envelope vector boundary is overlaid with local micro-topographic data to generate a suggested corrected boundary. Based on the level difference and correction boundary, issue early warning and assessment reports that include level adjustment strategies.

[0013] Secondly, the present invention provides a dynamic updating system for power grid ice zone maps based on long-term time-series satellite remote sensing, comprising: The time series construction module is used to acquire multi-source long-time-series satellite remote sensing images of the study area and construct a time series dataset of remote sensing images. The year-by-year synthesis module is used to synthesize the maximum ice thickness at the pixel scale according to the winter ice-covering period based on the time series dataset of remote sensing images, and output the year-by-year maximum ice thickness raster dataset. The extreme value estimation module is used to perform extreme value statistical analysis on each raster cell based on the annual maximum ice thickness raster dataset, calculate the extreme value of ice thickness for a specified return period, and generate a satellite-retrieved ice area map. The deviation warning module is used to compare the generated satellite-inverted ice area map with the current benchmark ice area map pixel by pixel, automatically identify continuous areas where the level deviation exceeds the preset threshold, and output warning results including the boundary of the abnormal area and the warning judgment report. The rolling update module is used to proactively trigger a partial revision approval process based on early warning results and the latest annual data added year by year, and to dynamically update the ice zone map.

[0014] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing.

[0016] The beneficial effects of this invention are as follows: Based on long-term satellite remote sensing archives, this invention synthesizes multiple inversion results during the winter icing period each year into an annual maximum icing thickness raster by combining the maximum pixel value, and performs extreme value statistical analysis at the pixel scale, thereby achieving the fusion of long-term, large-scale, pixel-level icing thickness inversion and return period extreme value estimation, avoiding spatial smoothing distortion caused by traditional site interpolation methods in areas with complex micro-topography; this invention also constructs an adaptive collaborative inversion strategy for optical, radar, and high-resolution remote sensing images. When optical imagery is available, optical reflectivity and snow index are used to enhance icing identification capabilities. When optical imagery is obscured by clouds and fog, radar polarimetric scattering, texture, and terrain prior features are used to maintain continuous inversion capabilities. In key anomaly areas, high-resolution imagery is used for local verification, thereby improving inversion stability under complex weather and terrain conditions. This invention compares the satellite-inverted ice zone map with the current benchmark ice zone map pixel by pixel, and automatically extracts the boundaries of anomaly areas through connected component analysis, further triggering a local revision approval process, forming a closed-loop mechanism of "inversion—comparison—early warning—revision—re-update". Compared with the traditional static map management mode, this invention can continuously evolve with the addition of new annual remote sensing data. This invention can significantly reduce the cost of large-scale manual reconnaissance and on-site verification, enabling operation and maintenance units to prioritize key sections with large grade deviations, high icing extreme values, and high historical fault overlap, improving the pertinence of ice zone map revision and anti-icing decisions. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an overall flowchart of a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing, provided by the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing, including: S1: Acquire multi-source long-time satellite remote sensing images of the study area and construct a remote sensing image time series dataset; S2: Based on the time series dataset of remote sensing images, the maximum ice thickness at the pixel scale is synthesized according to the winter ice-covering period, and the annual maximum ice thickness raster dataset is output. S3: Based on the annual maximum ice thickness raster dataset, perform extreme value statistical analysis on each raster cell, calculate the extreme value of ice thickness for a specified return period, and generate a satellite-inverted ice area map; S4: Compare the generated satellite-inverted ice zone map with the current baseline ice zone map pixel by pixel, automatically identify continuous areas where the level deviation exceeds the preset threshold, and output early warning results including the boundary of the abnormal area and the early warning judgment report. S5: Based on the early warning results and combined with the latest annual data added year by year, proactively trigger the partial revision and approval process to dynamically update the ice zone map.

[0021] It should be noted that, through steps S1-S5, a complete closed-loop technical system was constructed, encompassing long-term time-series remote sensing data acquisition, annual ice thickness inversion, extreme recurrence period estimation, automatic deviation early warning, and rolling revision of the ice zone map. This system overcomes the fundamental limitations of traditional ice observation methods in terms of spatiotemporal coverage and dynamic updates, enabling the power grid ice zone map to evolve autonomously under data-driven conditions in response to climate change and local environmental variations. This provides a dynamic support tool with long-term statistical benchmarks and high-frequency correction capabilities for transmission line anti-icing design and emergency decision-making.

[0022] It should also be noted that in this embodiment, the dynamic update of the ice zone map does not directly replace the current reference map with a single remote sensing inversion result, but adopts a closed-loop update mechanism of "satellite inversion ice zone map - pixel-by-pixel comparison of the reference map - automatic extraction of abnormal areas - local revision triggering". First, the satellite inversion ice thickness raster map under a specified return period is converted into an inversion ice zone level map according to the ice zone level classification rules; second, the inversion ice zone level map and the current reference ice zone map are compared pixel-by-pixel under a unified coordinate system and a unified resolution, and the level difference and thickness difference of each pixel are calculated; third, connected component analysis, morphological filtering and minimum area removal are performed on continuous pixel clusters with level difference or thickness difference exceeding a preset threshold to automatically extract the boundaries of abnormal areas; finally, the abnormal areas are overlaid with information on altitude, slope aspect, wind gap, valley, distance to water system, historical fault points and route corridors to generate local revision suggestions.

[0023] When the same anomalous area exhibits the same deviation in multiple consecutive winter cycles, or when the deviation of its multi-year inversion mean from the baseline map level reaches a statistically significant level, the system automatically triggers a local revision approval process. For anomalous deviations that occur only in a single year, they are marked as areas to be observed and included in the rolling monitoring of the following year. This avoids erroneous revisions caused by a single abnormal weather event, while also identifying areas that are consistently underestimated or overestimated, realizing the transformation of ice zone maps from static map management to data-driven dynamic evolution.

[0024] Example 2, refer to Figure 1 As one embodiment of the present invention, based on the previous embodiment, a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing is provided, including: In this embodiment, the acquisition of multi-source long-term satellite remote sensing images of the study area and the construction of a remote sensing image time series dataset in step S1 above include: Archived satellite remote sensing imagery data of the study area from 1999 to the present were collected, specifically including four types of data sources: The first type is MODIS products, using MOD09GA surface reflectance products (500-meter resolution) or MOD09Q1 (250-meter resolution), utilizing their daily revisit capability to ensure the temporal integrity of capturing each icing process in winter, in order to construct a long-term macro background field. The second category is the Landsat series satellites, including the Collection 2 Level-2 surface reflectance data (30-meter resolution) of Landsat-5, Landsat-7, Landsat-8 and Landsat-9, which serve as medium-resolution core inversion data and provide the fine spatial details required for the inversion of the main ice thickness. The third category is radar satellites, which use Sentinel-1's C-band SAR GRD products (10-meter resolution) and Gaofen-3 SAR images. They mainly utilize the VH cross-polarization channel's sensitivity to ice structure to solve the problem that optical images in the southwest region are often blocked by clouds and fog in winter, and are used for cloud and fog penetration and texture supplementation. The fourth category consists of high-resolution satellites, using sub-meter level optical stereo images or high-resolution SAR images from Gaofen-7 or TerraSAR-X, to accurately extract conductor vectors and verify small-scale icing conditions, serving as a ground truth calibration benchmark for subsequent machine learning models.

[0025] All acquired images are sequentially subjected to radiometric calibration, atmospheric correction, cloud masking, and unified geographic coordinate registration, and then uniformly projected to the target coordinate system. The final output is a time series dataset of remote sensing images with a time span of no less than 25 years, valid for each day or scene, and spatially aligned.

[0026] In another possible implementation, if the high-resolution SAR image archive is incomplete, medium-resolution SAR can still be used for inversion, only the high-precision ground truth calibration is affected, which can be replaced by a small number of ground observations.

[0027] In this embodiment, step S2 above, based on the remote sensing image time series dataset, synthesizes the maximum ice thickness at the pixel scale according to the winter icing period, and outputs a raster dataset of the maximum ice thickness for each year, including: For each winter icing period in the remote sensing image time series dataset, such as all valid remote sensing images from November to March of the following year, existing remote sensing inversion models for transmission line icing, such as icing thickness inversion algorithms based on random forest and lightGBM, are used to calculate the spatial distribution map of icing thickness scene by scene.

[0028] The transmission corridor buffer zone refers to the spatial area covered by an existing transmission line, extending a certain width to both sides, such as 1 km. In this embodiment, at the model input end, three main categories of 18-dimensional features are extracted from the transmission corridor buffer zone: The first category comprises eight dimensions of optical and indexal characteristics, covering surface reflectance across six fundamental bands, as well as the normalized snow index used to highlight snow and ice properties. , represented as: in, This represents the surface reflectance in the green band of satellite imagery, with a center wavelength of approximately 0.55 μm, and a value ranging from 0 to 1. Snow has high reflectivity in the green band. This represents the surface reflectance in the shortwave infrared band 1, with a center wavelength of approximately 1.6 μm. Snow strongly absorbs this wavelength, resulting in extremely low reflectance. The closer the calculation result is to 1, the higher the probability that the pixel is snow or ice; a result close to 0 or negative indicates that it is mostly bare soil, vegetation or water.

[0029] Normalized Difference Vegetation Index Excluding Vegetation Disturbance , represented as: in, This indicates the surface reflectance in the near-infrared band, with a center wavelength of approximately 0.85 μm. Healthy vegetation reflects light strongly in this band. This indicates the surface reflectance in the red band, with a center wavelength of approximately 0.65 μm. Vegetation absorbs red light during photosynthesis, resulting in lower reflectance. The value range is [-1, 1]. High positive values ​​(>0.6) represent dense vegetation, near zero represents bare soil or rock, and negative values ​​usually represent water or snow. (Introduction) The purpose is to eliminate the interference of vegetation in the identification of ice cover.

[0030] The second type of focused radar scattering and texture features comprises five dimensions, including VV and VH polarization backscattering coefficients and radar vegetation indices used to distinguish ground targets. , represented as: in, The cross-polarization backscattering coefficient represents the echo intensity of radar waves transmitted vertically and received horizontally. It is extremely sensitive to volume scattering within vegetation canopy and ice layer, and is often used to distinguish between vegetation cover and ice-covered conditions. The backscattering coefficient is the same polarization, representing the echo intensity of radar waves transmitted and received perpendicularly. It is sensitive to surface roughness and dielectric constant.

[0031] Furthermore, VH polarization contrast and entropy based on the gray-level co-occurrence matrix are used to identify surface roughness differences.

[0032] The third category couples five-dimensional a priori geographical environmental factors, including elevation, slope and aspect, topographic relief, and distance to water systems.

[0033] To improve the continuous inversion capability in areas prone to cloud and fog during winter, this embodiment sets up a multi-source remote sensing adaptive collaborative inversion strategy.

[0034] Specifically, the effective pixel ratio of each optical image within the power transmission corridor buffer zone is first calculated. When the effective pixel ratio behind the cloud cover is not lower than a preset threshold, the system uses optical reflectivity, normalized snow index, and vegetation index as the main features, and integrates radar backscattering and terrain prior features to retrieve the ice thickness. When the effective optical pixel ratio is lower than the preset threshold, or when multiple consecutive optical images are affected by clouds, fog, or shadows and cannot form effective observations, the system automatically increases the radar feature weights and uses VV and VH polarization backscattering coefficients, VH / VV ratio, radar vegetation index, gray-level co-occurrence matrix texture features, and terrain factors as the main inputs to realize the continuous ice thickness retrieval under cloud cover conditions.

[0035] For suspected areas of heavy icing, areas of abrupt changes in icing levels, and areas with significant deviations from the current benchmark icing map, high-resolution optical or high-resolution SAR imagery is further used for local verification to correct inversion biases caused by mixed pixels of ground features near transmission corridor boundaries and towers, as well as small-scale micro-topography. This forms a hierarchical collaborative inversion mechanism of "medium-resolution long-term coverage—radar cloud and fog compensation—high-resolution local verification".

[0036] The features are input into a pre-trained remote sensing inversion model of icing on transmission lines to obtain a single-scene raster map of icing thickness. In one possible implementation, the remote sensing inversion model for icing of transmission lines is trained by extracting the icing thickness at the conductor using Gaofen-7 or TerraSAR-X images and combining it with concurrent ground-based ice observation data to construct a training sample set.

[0037] By combining the pixel-by-pixel maximum values ​​of all individual scene icing thickness maps within the same icing period, a raster map of the annual maximum icing thickness is generated. ,in Indicates the year. These are the cell coordinates.

[0038] In this embodiment, step S3 above, based on the annual maximum ice thickness raster dataset, performs extreme value statistical analysis on each raster cell to calculate the extreme value of ice thickness for a specified return period, and generates a satellite-retrieved ice area map, including: For each raster cell Extract its annual maximum icing thickness sequence over a period of more than 25 years { Frequency analysis of the sequence was performed using the extreme value type I distribution (Gumbel distribution). The cumulative distribution function of the Gumbel distribution is: in, For position parameters, The scaling parameter is obtained by fitting the historical sequence data of the pixel through maximum likelihood estimation or the method of moments. and .

[0039] For a given recurrence period The extreme values ​​of ice thickness corresponding to years, such as 15 years, 30 years, 50 years, and 100 years. The calculation formula is: The above calculations are performed sequentially on all raster pixels within the region to obtain a raster map of the spatial distribution of ice thickness at each return period, i.e., a satellite-retrieved ice area map. .

[0040] In another possible implementation, the extreme value statistical distribution can be replaced by the Pearson Type III distribution or the generalized extreme value distribution, in addition to the Gumbel distribution.

[0041] In this embodiment, step S4 above compares the generated satellite-inverted ice zone map with the current baseline ice zone map pixel by pixel, automatically identifies continuous areas where the level deviation exceeds a preset threshold, and outputs early warning results including abnormal area boundaries and early warning assessment reports, including: After completing spatial resolution resampling and coordinate alignment, the inverted thickness is converted into the corresponding ice zone level according to current power industry standards.

[0042] The system compares the level difference between the inverted layer and the current baseline map one by one. Once the deviation of a continuous pixel cluster exceeds the preset threshold, such as the absolute error of light ice area exceeding 5mm, medium ice area exceeding 10mm, and heavy ice area relative error exceeding 15%, an early warning is triggered. The system automatically intervenes in the connected domain analysis, extracts the outer envelope vector boundary of the abnormal area, and overlays local micro-topographic data to generate a suggested correction boundary. At the same time, an early warning and judgment report containing the level adjustment strategy is issued.

[0043] In this embodiment, step S5 above, which involves proactively triggering a partial revision approval process based on the early warning results and combined with the latest annual data added year by year, to dynamically update the ice zone map includes: With the end of each winter cycle, the latest annual maximum extreme value raster map will be automatically added to the base sequence library. When the deviation of a certain warning area is observed to be statistically significant for several consecutive years, such as more than 15% for three consecutive years and the confidence interval of the mean does not include zero, the local area revision and approval process will be actively triggered, thereby completely changing the original static map management mode and realizing the forward dynamic deployment of the ice zone defense line.

[0044] Example 3: The above is an illustrative scheme of a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing, according to this embodiment. It should be noted that the technical solution of a power grid ice zone map dynamic updating system based on long-term satellite remote sensing and the technical solution of the above-described method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing belong to the same concept. Details not described in detail in the technical solution of the power grid ice zone map dynamic updating system based on long-term satellite remote sensing in this embodiment can be found in the description of the technical solution of the above-described method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing.

[0045] This embodiment also provides a dynamic updating system for power grid ice zone maps based on long-term time-series satellite remote sensing, including: The time series construction module is used to acquire multi-source long-time-series satellite remote sensing images of the study area and construct a time series dataset of remote sensing images. The year-by-year synthesis module is used to synthesize the maximum ice thickness at the pixel scale according to the winter ice-covering period based on the time series dataset of remote sensing images, and output the year-by-year maximum ice thickness raster dataset. The extreme value estimation module is used to perform extreme value statistical analysis on each raster cell based on the annual maximum ice thickness raster dataset, calculate the extreme value of ice thickness for a specified return period, and generate a satellite-retrieved ice area map. The deviation warning module is used to compare the generated satellite-inverted ice area map with the current benchmark ice area map pixel by pixel, automatically identify continuous areas where the level deviation exceeds the preset threshold, and output warning results including the boundary of the abnormal area and the warning judgment report. The rolling update module is used to proactively trigger a partial revision approval process based on early warning results and the latest annual data added year by year, and to dynamically update the ice zone map.

[0046] This embodiment also provides a computer device applicable to a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing, including: The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement a dynamic update method for power grid ice zone maps based on long-term satellite remote sensing, as proposed in the above embodiments.

[0047] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as proposed in the above embodiments.

[0048] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for dynamic updating of power grid ice area maps based on long-term satellite remote sensing proposed in the above embodiment. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0049] Example 4 is an embodiment of the present invention, which provides a method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing. To verify the beneficial effects of the present invention, a simulation experiment is conducted for scientific demonstration.

[0050] This embodiment takes a typical heavy icing region as the research object to verify the specific implementation process of the present invention.

[0051] (1) Data preparation and preprocessing Remote sensing image data of the study area from 2000 to 2024 winter (December to March of the following year) were collected, and the data was cleaned strictly in accordance with the four types of satellite combinations defined by the method of this invention.

[0052] For example, in the case of the severe ice storm in southern China in 2008, if Landsat-5 optical imagery is invalid due to excessive cloud cover, Sentinel-1 and ENVISAT ASAR historical archived SAR data are automatically activated as supplementary data. MODIS / Terra daily surface reflectance product (MOD09GA), spatial resolution 500m; Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 / 9 OLI surface reflectance data, with a spatial resolution of 30 m; Sentinel-1 SAR GRD data and Gaofen-3 SAR images from some years.

[0053] All images were radiometrically calibrated, atmospherically corrected, declouded, and georegistered, and then uniformly projected to the WGS_1984_UTM_Zone_48N coordinate system.

[0054] (2) Inversion of maximum ice thickness year by year Using the previously trained and validated LightGBM-based ice thickness inversion model (whose input features include: NDSI normalized snow cover index, surface temperature, SAR VH polarization backscattering coefficient, elevation, slope, etc.), ice thickness was inverted for each valid image. The maximum pixel value was taken from all single-image inversion results for each winter to generate 27 raster maps of the annual maximum ice thickness.

[0055] (3) Calculation of return period Using a 30 m resolution grid as the computational unit, the maximum ice thickness sequence for each grid over 27 years was extracted. The Gumbel distribution parameters were fitted using the maximum likelihood method. and Calculate the 30-year return period ( The icing thickness corresponding to =30 is expressed as: Generate a spatial distribution map of the ice thickness in the study area that occurs once every 30 years (satellite inversion ice area map).

[0056] (4) Deviation analysis and early warning After entering the return period calculation phase, each pixel with a resolution of 30 meters was locked, and the Gumbel distribution parameters of that point were solved using the maximum likelihood method, thereby deriving its 30-year return period ice thickness limit. In the subsequent comparison and investigation with the existing ice zone distribution map, it was found that in the windward slope area at an altitude of 1800 to 2200 meters, the current standard only classifies it as a medium ice zone with 15mm ice, but the extreme value inversion gave a theoretical thickness as high as 23.5mm, an error exceeding 56%. This immediately triggered an automatic warning, extracted the area into a vector polygon, and provided a correction suggestion to upgrade it to a heavy ice zone.

[0057] Historical operation records also show that the area experienced power line failures due to overload and icing in both 2018 and 2022. This not only confirms that the current drawings have an underestimation of this part, but also indirectly confirms the sensitivity and practicality of the method of this invention in engineering anti-icing early warning.

[0058] (5) Verification and Rolling Updates A review of recent line fault records caused by ice damage in the area revealed that two power poles near Area A experienced line breaks due to severe icing during the winters of 2018 and 2022, confirming the current ice map's inaccurate rating. This result validates the effectiveness of the method presented in this invention. With the addition of new remote sensing data each year, the system will automatically update the historical sequence, recalculate the return period, and continuously monitor the trend of ice map deviation.

[0059] In a typical heavy icing area experiment, historical icing failure records, manual icing observation records, and local online monitoring records were selected as verification samples. The method of this invention was compared with the traditional meteorological station interpolation method. The results show that in the estimation of 30-year return period icing thickness, the average absolute error of the traditional method is 4.8 mm, while the average absolute error of the method of this invention is 2.8 mm, a reduction of 41.7%. In terms of the amount of manual verification work, the traditional method requires manual verification of 120 km of the study area. 2 The scope of field verification is limited, but this invention narrows the key verification area to 34km by automatically extracting abnormal areas. 2 The area under review decreased by 71.7%.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing, characterized in that, include: Acquire multi-source long-time satellite remote sensing images of the study area and construct a remote sensing image time series dataset; Based on the time series dataset of remote sensing images, the maximum ice thickness at the pixel scale is synthesized according to the winter ice-covering period, and the annual maximum ice thickness raster dataset is output. Based on the annual maximum ice thickness raster dataset, extreme value statistical analysis is performed on each raster cell to calculate the extreme value of ice thickness for a specified return period and generate a satellite-retrieved ice area map. The generated satellite-inverted ice zone map is compared pixel by pixel with the current benchmark ice zone map, and continuous areas with level deviations exceeding the preset threshold are automatically identified. The warning results, including the boundary of the abnormal area and the warning assessment report, are output. Based on the early warning results and combined with the latest annual data added year by year, the partial revision and approval process is proactively triggered to dynamically update the ice zone map.

2. The method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as described in claim 1, characterized in that, The acquisition of multi-source long-term satellite remote sensing images of the study area, and the construction of a remote sensing image time series dataset, includes: Acquire multi-source long-term satellite remote sensing images of the study area, the images containing at least three types of data sources: the first type is medium-resolution optical images with daily revisit capability; the second type is radar images with cloud and fog penetration capability; and the third type is high-resolution images. Radiometric calibration, atmospheric correction, cloud masking, and unified geographic coordinate registration are performed on all acquired images in sequence to form a spatially aligned remote sensing image time series dataset with a time span of no less than a preset number of years, valid for each day or scene.

3. The method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as described in claim 2, characterized in that, The remote sensing image time series dataset is used to synthesize the maximum ice thickness at the pixel scale according to the winter icing period, outputting a raster dataset of the maximum ice thickness for each year, including: Based on the time series dataset of remote sensing images, for all valid remote sensing images during each winter icing period, a transmission corridor buffer zone is defined by extending a preset width to both sides of the transmission line as the center. Within the power transmission corridor buffer zone, three types of features are extracted pixel by pixel. The first type is multi-band surface reflectance obtained from optical images and optical index features used to identify snow and ice and exclude vegetation. The second type is backscattering coefficients of different polarization modes obtained from radar images, radar vegetation indices used to distinguish ground targets, and contrast and entropy features based on texture analysis. The third type is prior geographical environmental features including elevation, slope, aspect, topographic relief, and distance to water systems.

4. The method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as described in claim 3, characterized in that, The method for synthesizing the maximum ice thickness at the pixel scale based on the remote sensing image time series dataset, according to the winter icing period, and outputting a raster dataset of the maximum ice thickness for each year, also includes: The features extracted pixel by pixel are input into the pre-trained ice thickness inversion model, and the spatial distribution map of ice thickness is calculated scene by scene. The maximum value of each pixel in the distribution map of ice thickness of all single scenes within the same ice-covering period is taken to synthesize the raster dataset of the annual maximum ice thickness for each year.

5. The method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as described in claim 4, characterized in that, The method, based on the annual maximum ice thickness raster dataset, performs extreme value statistical analysis on each raster cell, calculates the extreme value of ice thickness for a specified return period, and generates a satellite-retrieved ice area map, including: Using each raster cell as an independent calculation unit, the annual maximum ice thickness sequence of the cell is extracted during consecutive winter ice-covering periods. An extreme value probability distribution model was used to fit the annual maximum ice thickness sequence.

6. The method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as described in claim 5, characterized in that, The method of performing extreme value statistical analysis on each raster cell based on the annual maximum ice thickness raster dataset, calculating the extreme value of ice thickness for a specified return period, and generating a satellite-retrieved ice area map also includes: For a given number of return periods, the extreme values ​​of ice thickness corresponding to the return period are inferred from the extreme value distribution parameters obtained by fitting. By sequentially traversing all raster pixels within the study area, a raster map of the spatial distribution of ice thickness under each return period is generated, i.e., a satellite-inverted ice area map.

7. The method for dynamically updating power grid ice zone maps based on long-term satellite remote sensing as described in claim 6, characterized in that, The process involves comparing the generated satellite-retrieved ice zone map with the existing baseline ice zone map pixel by pixel, automatically identifying continuous areas where the level deviation exceeds a preset threshold, and outputting early warning results including the boundaries of abnormal areas and an early warning assessment report. The generated satellite-inverted ice zone map is spatially resampled and aligned with the existing baseline ice zone map, and the ice thickness values ​​in the satellite-inverted ice zone map are converted into the corresponding ice zone class. The ice zone level of the inverted layer is compared with the ice zone level of the reference layer pixel by pixel, and the level difference is calculated. When the level difference of a continuously distributed cluster of pixels exceeds a preset threshold, connected component analysis is triggered to extract the outer envelope vector boundary of the abnormal region. The outer envelope vector boundary is overlaid with local micro-topographic data to generate a suggested corrected boundary. Based on the level difference and correction boundary, issue early warning and assessment reports that include level adjustment strategies.

8. A dynamic update system for power grid ice zone maps based on long-term satellite remote sensing, employing the method described in any one of claims 1-7, characterized in that, include: The time series construction module is used to acquire multi-source long-time-series satellite remote sensing images of the study area and construct a time series dataset of remote sensing images. The year-by-year synthesis module is used to synthesize the maximum ice thickness at the pixel scale according to the winter ice-covering period based on the time series dataset of remote sensing images, and output the year-by-year maximum ice thickness raster dataset. The extreme value estimation module is used to perform extreme value statistical analysis on each raster cell based on the annual maximum ice thickness raster dataset, calculate the extreme value of ice thickness for a specified return period, and generate a satellite-retrieved ice area map. The deviation warning module is used to compare the generated satellite-inverted ice area map with the current benchmark ice area map pixel by pixel, automatically identify continuous areas where the level deviation exceeds the preset threshold, and output warning results including the boundary of the abnormal area and the warning judgment report. The rolling update module is used to proactively trigger a partial revision approval process based on early warning results and the latest annual data added year by year, and to dynamically update the ice zone map.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.

Images