Method and system for evaluating power emergency passage based on spaceborne hyperspectrum

Abstract

The application discloses a power emergency passage evaluation method and system based on a spaceborne hyperspectrum, belongs to the technical field of power grid engineering management and emergency disaster rescue management, and comprises the following steps: receiving a spaceborne hyperspectrum remote sensing image and a geographic elevation data set; extracting a surface material characteristic vector matrix and combining the elevation data set to construct a three-dimensional landform model; performing surface disaster element identification on the model to generate an emergency passage topological network containing a passage blocking node and a passable edge link; and performing emergency rescue path optimization operation based on the network to generate a power emergency passage evaluation result. The method can intelligently avoid blocking risks, and accurately and efficiently plan a post-disaster power repair route by adopting multi-band hyperspectrum data fusion modeling and combining disaster element identification to construct a topological network for optimization.

Description

Technical Field

[0001] This invention relates to the field of power grid engineering management and emergency disaster relief management technology, and in particular to a method and system for evaluating power emergency channels based on spaceborne hyperspectral imaging. Background Technology

[0002] Currently, with the frequent occurrence of extreme natural disasters such as typhoons, torrential rains, mudslides, and earthquakes, these disasters often lead to widespread power grid paralysis and severe damage to transmission and transformation equipment. During the critical post-disaster relief period, the disaster area is often accompanied by widespread flooding, landslides, or road breaks. Rapid and comprehensive surveys of the surface damage, accurate assessment of power emergency corridor blockages, and planning safe and feasible repair routes are crucial prerequisites for ensuring the smooth advance of rescue forces into the disaster area and shortening power restoration time. Existing post-disaster emergency surveys and disaster information gathering methods largely rely on conventional methods such as manual foot patrols, low-altitude helicopters, or multi-rotor drones for localized aerial photography.

[0003] Among related technologies, Chinese invention patent CN121745462A discloses a rapid response method and system for power emergency repair based on disaster early warning, including: multi-source disaster early warning data collection and power grid vulnerable area assessment, i.e., dividing vulnerable areas based on a fault probability model; emergency repair resource pre-positioning planning and plan formulation, i.e. calculating emergency repair resource requirements and selecting key pre-positioning points to formulate plans; and post-disaster dynamic response and priority repair, i.e., after a disaster occurs, using intelligent monitoring equipment such as drones to collect power grid damage data in real time, activating pre-positioned resources and carrying out priority repair.

[0004] However, the aforementioned technologies have limitations in the actual post-disaster channel assessment and surveying stages. Specifically, the real-time collection of damage data heavily relies on conventional low-altitude intelligent monitoring equipment such as drones. In the initial stages after extreme disasters, disaster areas are often accompanied by severe weather conditions such as strong gusts and continuous rainfall, as well as strict low-altitude airspace control. This results in drones being unable to take off, having weak wind and rain resistance, and severely limited range. At the same time, limited by the single-pass radius of low-altitude equipment and the image transmission interruption caused by the large-scale damage to communication base stations in the disaster area, existing methods are extremely difficult to achieve large-scale synchronous coverage surveys of the entire disaster area. Therefore, this technology cannot quickly and continuously complete the topographic mapping and multi-dimensional composite disaster indicator extraction of the entire disaster area within a few hours. This leads to fragmented underlying environmental data and even long-term perception blind spots, ultimately resulting in incomplete emergency channel topology construction, severe delays in repair optimization and dispatch response, and difficulty in meeting the urgent need for high timeliness and robustness in power emergency repairs under large-scale disasters. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method and system for evaluating power emergency channels based on spaceborne hyperspectral imaging. It employs multi-band hyperspectral feature extraction and elevation data fusion to construct a three-dimensional terrain model, and combines a multi-index comprehensive judgment model to accurately identify traffic obstruction nodes to build a channel topology network. This system can intelligently find the repair route with the optimal overall cost and generate executable incremental scheduling instructions under weak network conditions, thereby improving the efficiency and reliability of power emergency repair management.

[0006] The above objectives can be achieved through the following scheme: a power emergency channel assessment method based on spaceborne hyperspectral imaging, including receiving spaceborne hyperspectral remote sensing images and geographic elevation datasets of the target area transmitted via a satellite communication interface; extracting surface material feature vector matrices from the spaceborne hyperspectral remote sensing images, and constructing a three-dimensional geomorphic model of the target area based on the surface material feature vector matrix and the geographic elevation dataset; performing surface disaster element identification on the three-dimensional geomorphic model to generate an emergency channel topology network containing multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links; and performing emergency rescue path optimization calculations based on the emergency channel topology network to generate power emergency channel assessment results for the target area.

[0007] Optionally, the step of extracting the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image includes: reading the pixel radiance values ​​of multiple target spectral bands contained in the spaceborne hyperspectral remote sensing image; performing atmospheric correction algorithm processing on the pixel radiance values ​​of the multiple target spectral bands to generate a corresponding atmospheric corrected reflectance sequence; extracting vegetation cover parameters and water body distribution parameters from the atmospheric corrected reflectance sequence, and combining the vegetation cover parameters and the water body distribution parameters to generate the surface material feature vector matrix.

[0008] Optionally, the extraction of vegetation cover parameters and water distribution parameters from the atmospheric corrected reflectance sequence includes: separating near-infrared reflectance and red reflectance from the atmospheric corrected reflectance sequence; calculating the vegetation cover parameter based on the ratio of the difference to the sum of the near-infrared and red reflectance values; separating green and mid-infrared reflectance from the atmospheric corrected reflectance sequence; and calculating the normalized difference to obtain the water distribution parameter based on the green and mid-infrared reflectance values.

[0009] Optionally, the step of performing surface disaster element identification on the three-dimensional terrain model to generate an emergency passage topology network containing multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links includes: extracting the three-dimensional coordinate parameters of multiple candidate road nodes distributed in the three-dimensional terrain model; calculating the terrain undulation gradient values ​​between two adjacent candidate road nodes; extracting the surface water coverage area and vegetation destruction area corresponding to the candidate road nodes from the spaceborne hyperspectral remote sensing image; and combining the terrain undulation gradient values, the surface water coverage area, and the vegetation destruction area to calculate and generate the transmission line tower collapse risk index and geological disaster susceptibility index corresponding to the candidate road nodes. The system generates a node blocking probability value for each candidate road node based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value. In response to a node blocking probability value exceeding a preset probability threshold, the corresponding candidate road node is marked as a traffic blocking node, and the path connecting two traffic blocking nodes is marked as an associated disaster-stricken road segment. Paths not marked as associated disaster-stricken road segments are marked as passable edge links. The emergency passage topology network is constructed by connecting the spatial locations of all traffic blocking nodes, all associated disaster-stricken road segments, and all passable edge links.

[0010] Optionally, generating the node blocking probability value for each of the candidate road nodes based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value includes: configuring independent risk assessment weight parameters for each of the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value; performing a weighted fusion operation on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value based on the risk assessment weight parameters to construct a weighted multi-indicator comprehensive judgment model; and outputting the node blocking probability value through the weighted multi-indicator comprehensive judgment model.

[0011] Optionally, the step of performing emergency rescue path optimization calculation based on the emergency channel topology network to generate power emergency channel evaluation results for the target area includes: extracting the travel distance cost parameters and road surface resistance parameters of multiple passable edge links included in the emergency channel topology network; receiving the repair start point coordinates and power fault end point coordinates input from an external system; planning multiple candidate repair routes based on the travel distance cost parameters, the road surface resistance parameters, the repair start point coordinates, and the power fault end point coordinates, and calculating the comprehensive cost weight value of each candidate repair route; sorting the multiple candidate repair routes in ascending order of the comprehensive cost weight value, and selecting the target repair route with the highest comprehensive cost weight value as the power emergency channel evaluation result.

[0012] Optionally, calculating the comprehensive cost weight value of each of the candidate emergency repair routes includes: assigning a first preset weight ratio to the travel distance consumption parameter; assigning a second preset weight ratio to the road surface resistance parameter; and adding the product of the travel distance consumption parameter and the first preset weight ratio to the product of the road surface resistance parameter and the second preset weight ratio to generate the comprehensive cost weight value of each of the candidate emergency repair routes.

[0013] Optionally, the method further includes: after generating the power emergency channel assessment result for the target area, converting the power emergency channel assessment result into an executable dispatch instruction format; and sending the executable dispatch instruction format to an external network via a communication network to trigger the power repair terminal that receives the executable dispatch instruction format to perform route navigation actions according to the power emergency channel assessment result.

[0014] Optionally, converting the power emergency channel assessment result into an executable scheduling instruction format includes: extracting the latitude and longitude coordinate sequence of route nodes contained in the power emergency channel assessment result; obtaining network fluctuation characteristic parameters of the external network under post-disaster communication interruption conditions; triggering a lightweight emergency communication protocol based on the network fluctuation characteristic parameters to convert the latitude and longitude coordinate sequence of route nodes into a serialized data stream carrying a breakpoint resume identifier; obtaining the local cached disaster map of the power repair terminal; comparing the serialized data stream with the locally cached disaster map to generate incremental update data packets as the executable scheduling instruction format.

[0015] Based on the same inventive concept, this invention also provides a power emergency channel assessment system based on spaceborne hyperspectral imaging, the system comprising: The receiving module is used to receive onboard hyperspectral remote sensing images and geographic elevation datasets of the target area transmitted via the satellite communication interface. The extraction and construction module is used to extract the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image, and construct a three-dimensional geomorphological model of the target area based on the surface material feature vector matrix and the geographic elevation dataset. The identification module is used to identify surface disaster elements in the three-dimensional terrain model and generate an emergency passage topology network that includes multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links. The evaluation module is used to perform emergency rescue path optimization calculations based on the emergency channel topology network and generate evaluation results for the power emergency channels in the target area.

[0016] Compared with the prior art, the present invention has the following advantages: 1. Achieve high-precision geographical environment modeling and geological feature extraction in disaster areas. By fusing the surface material feature vector matrix of spaceborne hyperspectral remote sensing images with the geographic elevation dataset of the target area, vegetation cover parameters and water distribution parameters can be accurately extracted, thereby constructing a refined three-dimensional geomorphological model, providing reliable data support for power emergency assessment in complex post-disaster terrain environments.

[0017] 2. Improve the accuracy of power emergency access status assessment and road network blockage prediction. By combining topographic relief, water cover and vegetation damage, comprehensively calculate the risk of transmission line tower collapse, geological disaster susceptibility and flood depth, dynamically generate node blockage probabilities and construct an emergency access topology network, thereby enabling scientific and comprehensive identification of access blockage nodes and affected road sections in disaster areas.

[0018] 3. Improve the efficiency of post-disaster power repair route planning and the reliability of emergency dispatch management. Based on the emergency channel topology network, path optimization calculations are performed by comprehensively considering the travel distance and road surface resistance. This not only enables the rapid planning of the target repair route with the lowest overall cost, but also allows for the reliable issuance of dispatch instructions through lightweight emergency communication protocols and incremental update data packets under post-disaster communication interruption conditions, thereby enhancing the emergency management sector's ability to execute rescue and repair operations in extreme environments.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the power emergency channel evaluation method based on spaceborne hyperspectral imaging, according to an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of the power emergency channel assessment system based on spaceborne hyperspectral imaging according to an embodiment of the present invention.

[0023] Figure 3 This is a visualization diagram illustrating the multi-source data fusion and three-dimensional model construction of the power emergency channel assessment method and system based on spaceborne hyperspectral imaging, according to an embodiment of the present invention.

[0024] Figure 4 This is a thermal schematic diagram of the surface material characteristic calculation model of the power emergency channel assessment method and system based on spaceborne hyperspectral imaging, according to an embodiment of the present invention.

[0025] Figure 5 This is a radar schematic diagram of a weighted multi-index comprehensive risk assessment model for a power emergency channel assessment method and system based on spaceborne hyperspectral imaging, according to an embodiment of the present invention.

[0026] Figure 6 This is a graph-based visualization diagram of the emergency channel topology network and path cost of the power emergency channel evaluation method and system based on spaceborne hyperspectral imaging, as described in this embodiment of the invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1One embodiment of the present invention proposes a power emergency channel assessment method based on spaceborne hyperspectral imaging. It uses multi-band hyperspectral feature extraction and elevation data fusion to construct a three-dimensional terrain model, and combines a multi-index comprehensive judgment model to accurately identify traffic obstruction nodes to construct a channel topology network. It can intelligently find the repair route with the best comprehensive cost, and generate executable incremental scheduling instructions under weak network conditions, thereby improving the efficiency and reliability of power emergency repair management.

[0029] The method described in this embodiment specifically includes: The power emergency channel assessment method based on spaceborne hyperspectral imaging includes receiving spaceborne hyperspectral remote sensing images transmitted via a satellite communication interface and a geographic elevation dataset of the target area. Extract the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image, and construct a three-dimensional geomorphological model of the target area based on the surface material feature vector matrix and the geographic elevation dataset; The surface disaster element identification is performed on the three-dimensional terrain model to generate an emergency passage topology network containing multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links; Based on the emergency channel topology network, perform emergency rescue path optimization calculations to generate power emergency channel evaluation results for the target area.

[0030] Specifically, the system receives satellite-borne hyperspectral remote sensing images and geographic elevation datasets of the target area. It then extracts surface material feature vector matrices from the images and fuses them with the geographic elevation data to construct a 3D geomorphological model of the area. Surface disaster element identification is performed on the 3D geomorphological model, generating an emergency channel topology network consisting of traffic-blocking nodes, associated disaster-stricken road sections, and passable edge links. Based on this topology network, emergency rescue route optimization calculations are performed, outputting an evaluation result for the power emergency channel in the target area. This system can intelligently and accurately analyze and plan post-disaster power repair routes. By extracting surface material features from hyperspectral remote sensing images and combining them with elevation data to construct a 3D geomorphological model, the geographical environment and material damage of the disaster area can be more comprehensively and precisely reconstructed. By identifying disaster elements and transforming them into an emergency channel topology network containing specific blocking nodes and passable links, the complex disaster-stricken road conditions are successfully abstracted into a structured mathematical model. This enables rapid and automated path optimization calculations and output evaluation results, improving the timeliness and scientific rigor of power emergency rescue responses.

[0031] Optionally, the step of extracting the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image includes: Read the pixel radiance values ​​of multiple target spectral bands contained in the spaceborne hyperspectral remote sensing image; An atmospheric correction algorithm is applied to the pixel radiance values ​​of the multiple target spectral bands to generate corresponding atmospheric corrected reflectance sequences. The vegetation cover parameters and water distribution parameters are extracted from the atmospheric corrected reflectance sequence, and the vegetation cover parameters and water distribution parameters are combined to generate the surface material feature vector matrix.

[0032] Specifically, the hyperspectral remote sensing data file is accessed through a data interface, and the data structure is parsed pixel by pixel to read the radiant energy quantization values ​​corresponding to multiple preset target spectral bands; the corresponding radiance calculation formula is as follows: ; Represents the pixel radiance value. This represents the absolute calibration gain coefficient for the corresponding band, and D represents the digital quantization value of the pixel. This represents the absolute calibration offset coefficient for the corresponding band. The target spectral band is a specific electromagnetic wave frequency range encompassing the visible to near-infrared range, selected based on the power emergency channel assessment requirements. The pixel radiance value is the physical quantization of the electromagnetic wave energy reflected from the ground surface received by the sensor. The digital quantization value is the dimensionless raw grayscale data directly recorded by the remote sensing instrument. The absolute calibration gain coefficient is the slope parameter for establishing a linear relationship between the digital quantization value and radiance. The absolute calibration offset coefficient is the intercept parameter for establishing a linear relationship between the digital quantization value and radiance. Subsequently, based on the radiative transfer model, the read radiance values ​​are processed to eliminate gas absorption and aerosol scattering, inverting the radiance at the sensor's entrance pupil into the true surface reflectance sequence. The corresponding atmospheric correction calculation formula is as follows: ; This represents the value of a certain band in the atmospheric corrected reflectance sequence. Represents atmospheric path radiance. Represents solar irradiance at the top of the atmosphere. Represents the zenith angle of the sun. Represents the atmospheric transmittance from the sun to the Earth's surface. This represents the atmospheric updraft transmittance from the Earth's surface to the sensor. This represents the constant pi. The atmospheric correction algorithm is a mathematical inversion process to eliminate the attenuation of electromagnetic signals by water vapor and aerosols in the atmosphere. The atmospheric corrected reflectance sequence is a set of values ​​arranged in wavelength order of the true physical reflectance properties of the Earth's surface after atmospheric interference removal. Atmospheric path radiance is the radiant energy that enters the sensor directly from the atmosphere before reaching the surface. The solar zenith angle is the angle between the sunlight and the surface normal. Next, band data for calculating specific surface physicochemical indices are separated from the reflectance sequence and substituted into the vegetation index calculation model and water body index calculation model to obtain corresponding values. These values ​​are then concatenated into a feature matrix. The corresponding feature matrix combination formula is as follows: ; M represents the surface material feature vector matrix, V represents the vegetation cover parameter, and W represents the water body distribution parameter; among them, the vegetation cover parameter is a numerical indicator that quantitatively characterizes the growth status and distribution density of surface vegetation, the water body distribution parameter is a numerical indicator used to distinguish surface water bodies from non-water background and quantify the water body area, and the surface material feature vector matrix is ​​a multi-dimensional mathematical array structure composed of multiple feature parameters reflecting different surface cover types. For example... Figure 3 The diagram shown is a visualization of the multi-source data fusion and 3D model construction of the present invention, which intuitively demonstrates the process of bottom-level data input and 3D base establishment.

[0033] For example, in a specific evaluation calculation, the digital quantization value of the red light band is read. The absolute calibration gain coefficient is adjusted according to the factory calibration parameters of the built-in sensor. and absolute calibration offset coefficient Read the digital quantization value in the near-infrared band Absolute scaling gain coefficient Absolute calibration offset coefficient Read the digital quantization value of the green light band Absolute scaling gain coefficient Absolute calibration offset coefficient Read the digital quantization value of the mid-infrared band Absolute scaling gain coefficient Absolute calibration offset coefficient The radiance in the red light band was calculated. Near-infrared radiance Green light band radiance Mid-infrared radiance Atmospheric correction parameters for each band were obtained based on real-time meteorological data for the day, including the atmospheric path radiance in the red band. Solar irradiance at the top of the atmosphere Sun's atmospheric transmittance to the Earth's surface Atmospheric uplift transmittance from the Earth's surface to the sensor Atmospheric path radiance in the near-infrared band Solar irradiance at the top of the atmosphere Sun's atmospheric transmittance to the Earth's surface Atmospheric uplift transmittance from the Earth's surface to the sensor Atmospheric path radiance in the green light band Solar irradiance at the top of the atmosphere Sun's atmospheric transmittance to the Earth's surface Atmospheric uplift transmittance from the Earth's surface to the sensor Atmospheric path radiance in the mid-infrared band Solar irradiance at the top of the atmosphere Sun's atmospheric transmittance to the Earth's surface Atmospheric uplift transmittance from the Earth's surface to the sensor The cosine value of the solar zenith angle is fixed as follows: The constant of pi is taken as Substituting the above values ​​into the model, the atmospheric corrected reflectance in the red band is calculated. Near-infrared atmospheric corrected reflectance Atmospheric corrected reflectance in the green light band Mid-infrared atmospheric corrected reflectance The corresponding numerical values ​​are extracted and substituted into the vegetation and water index algorithm to calculate the vegetation cover parameter. The water distribution parameters were calculated. Finally, by combining the aforementioned parameters, a surface material feature vector matrix with a dimension of two rows and one column is constructed. .

[0034] Optionally, the extraction of vegetation cover parameters and water distribution parameters from the atmospheric corrected reflectance sequence includes: The near-infrared reflectance and the red light reflectance are separated from the atmospheric corrected reflectance sequence; The vegetation cover parameter is calculated based on the ratio of the difference between the near-infrared band reflectance and the red band reflectance to the sum of their values. The green light band reflectance and the mid-infrared band reflectance are separated from the atmospheric corrected reflectance sequence; The water body distribution parameters are obtained by calculating the normalized difference between the green light band reflectance and the mid-infrared band reflectance.

[0035] Specifically, near-infrared and red-band reflectance are separated from the atmospheric corrected reflectance sequence; vegetation cover parameters are calculated based on the ratio of the difference between the near-infrared and red-band reflectance to their sum; the corresponding vegetation cover parameter calculation formula is as follows: ; V represents the vegetation cover parameter. Represents near-infrared reflectivity. Reflectance represents the red light band; among which, the near-infrared band reflectance is a parameter representing the ratio of reflected energy of electromagnetic waves with wavelengths in the near-infrared spectral range by surface materials, and the red light band reflectance is a parameter representing the ratio of reflected energy of electromagnetic waves with wavelengths in the visible red light spectral range by surface materials; the green light band reflectance and mid-infrared band reflectance are separated from the atmospheric corrected reflectance sequence; the normalized difference is calculated based on the green light band reflectance and mid-infrared band reflectance to obtain the water body distribution parameter; the corresponding water body distribution parameter calculation formula is as follows: ; W represents the water distribution parameter. Represents the reflectivity in the green light band. Reflectance represents the mid-infrared band; among which, the green band reflectance is a parameter representing the ratio of reflected energy of electromagnetic waves with wavelengths in the visible green spectral range by surface materials, and the mid-infrared band reflectance is a parameter representing the ratio of reflected energy of electromagnetic waves with wavelengths in the mid-infrared spectral range by surface materials. Normalized difference is a mathematical processing method that eliminates topographic multiplicative noise and highlights specific surface target attributes by calculating the quotient of the difference and sum of the reflectance of two characteristic bands. The extraction of vegetation cover parameters and water distribution parameters from the atmospheric corrected reflectance sequence is based on the physical principle of multispectral feature mapping. Through algebraic operations of specific band combinations, the basic spectral reflectance data is transformed into macroscopic quantitative indicators characterizing the microscopic surface damage state of the disaster area. For example... Figure 4 The figure shown is a thermodynamic schematic diagram of the surface material characteristic calculation model of the present invention.

[0036] For example, the vegetation cover parameter calculation is based on the principle that healthy green vegetation has strong chlorophyll absorption characteristics in the red light band and high reflectivity of its internal cellular structure in the near-infrared band. The water body distribution parameter calculation is based on the principle that natural open water bodies have high reflectivity in the green light band and exhibit strong absorption and attenuation characteristics in the mid-infrared band. In specific evaluation calculation nodes, the near-infrared band reflectivity is separated and read from the atmospheric corrected reflectivity sequence corresponding to the disaster area location. and red light band reflectivity Then, these two characteristic values ​​were substituted into a mathematical model of the ratio of difference to sum to derive the vegetation cover parameter. Next, the green band reflectance was separated and read from the atmospheric corrected reflectance sequence. and mid-infrared reflectivity Substituting these two values ​​into the normalized difference calculation model, the water distribution parameters are derived. .

[0037] Optionally, the step of performing surface disaster element identification on the three-dimensional terrain model to generate an emergency passage topology network containing multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links includes: Extract the three-dimensional coordinate parameters of multiple candidate road nodes distributed from the three-dimensional terrain model; Calculate the terrain undulation gradient between two adjacent candidate road nodes; Extract the surface water coverage area and vegetation destruction area corresponding to the candidate road node from the spaceborne hyperspectral remote sensing image; By combining the terrain undulation gradient values, the surface water coverage area, and the vegetation destruction area, the power transmission line tower collapse risk index, geological disaster susceptibility index, and flood water depth value corresponding to the candidate road nodes are generated. Based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value, the node blockage probability value of each candidate road node is generated; In response to the node blocking probability value being greater than a preset probability threshold, the corresponding candidate road node is marked as the traffic blocking node, the path connecting two traffic blocking nodes is marked as the associated disaster-stricken road segment, and the path not marked as the associated disaster-stricken road segment is marked as the passable edge link; The emergency passage topology network is constructed by connecting the spatial locations of all the aforementioned traffic-blocking nodes, all the associated disaster-stricken road sections, and all the aforementioned passable edge links.

[0038] Specifically, the 3D coordinate parameters of multiple candidate road nodes distributed in the 3D terrain model are extracted. The extraction process is based on vertex sampling of the 3D mesh data. The numerical formula for calculating the terrain undulation gradient between two adjacent candidate road nodes is as follows: ; G represents the numerical value of the terrain undulation gradient. and as well as The three-dimensional coordinate parameters represent the first candidate road node. and as well as The three-dimensional coordinate parameters represent the second candidate road node. A candidate road node is a spatial location point in the road network representing a road intersection or key turning point. The three-dimensional coordinate parameters are spatial location markers including longitude, latitude, and altitude. The terrain undulation gradient value is a physical indicator quantifying the steepness of the terrain elevation change between two adjacent points. Subsequently, the surface water coverage and vegetation destruction areas of the corresponding candidate road nodes are extracted from satellite-borne hyperspectral remote sensing images. Combining the terrain undulation gradient values, surface water coverage, and vegetation destruction areas, calculations are performed to generate the transmission line tower collapse risk index, geological disaster susceptibility index, and flood depth value for the corresponding candidate road nodes. The corresponding formula for calculating the transmission line tower collapse risk index is as follows: ; This represents the risk index of power transmission line tower collapse. Represents the gradient influence coefficient. Represents the vegetation destruction impact coefficient. This parameter represents the area of ​​vegetation destruction. The corresponding formula for calculating the geological hazard susceptibility index is as follows: ; Represents the susceptibility index to geological disasters. Represents the influence coefficient of geological gradient. Represents the influence coefficient of water body scouring. This parameter represents the area covered by surface water bodies. The corresponding formula for calculating flood depth is as follows: ; Represents the depth of floodwater. Represents the water catchment-convergence conversion coefficient. This parameter represents the depth of the local catchment area at the node. Specifically, the surface water coverage area is the polygonal spatial boundary identified as waterlogged or flooded in the remote sensing image; the vegetation damage area is the polygonal spatial boundary identified as vegetation collapse or damage in the remote sensing image; the transmission line tower collapse risk index is a quantitative evaluation value quantifying the probability of tower foundation collapse due to terrain and vegetation damage; the geological hazard susceptibility index is a quantitative evaluation value assessing the probability of secondary disasters such as landslides or debris flows in the local area; and the floodwater depth value is a quantitative evaluation value reflecting the physical depth of the road submerged by surface water. Based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological hazard susceptibility index, and the floodwater depth value, the node blocking probability value for each candidate road node is generated. The corresponding node blocking probability value calculation formula is as follows: ; P represents the node blocking probability value. Represents gradient weights. Represents the collapse risk weight. Represents the weight of geological disasters. The system represents the weight of water depth. The node blockage probability value is a comprehensive evaluation parameter assessing the final probability that a road node will become impassable due to the combined effects of multiple disaster factors. When the node blockage probability value exceeds a preset probability threshold, the system marks the corresponding candidate road node as a blocked node, marks the path connecting two blocked nodes as an associated disaster-stricken road segment, and marks paths not marked as associated disaster-stricken road segments as passable edge links. An emergency passage topology network is constructed by connecting the spatial locations of all blocked nodes, all associated disaster-stricken road segments, and all passable edge links. The preset probability threshold is a rigid probability boundary standard for the system to determine if a road has completely lost its passability. Blocked nodes are disaster-stricken road network intersections determined to be impassable for rescue vehicles. Associated disaster-stricken road segments are damaged physical roads blocked at both ends by blocked nodes. Passable edge links are physical roads that still allow rescue vehicles to pass after the disaster. The emergency passage topology network is a graph theory structure data model composed of points and lines, reflecting the actual traffic status of the disaster area.

[0039] For example, the three-dimensional coordinate parameters of the first candidate road node are extracted as longitude of 1 kilometer, latitude of 2 kilometers, and altitude of 50 meters. The three-dimensional coordinate parameters of the second adjacent candidate road node are longitude 1 kilometer, latitude 2,100 meters, and altitude 80 meters, respectively. Substitute the values ​​into the formula to calculate the terrain relief gradient. The image of this region was extracted from spaceborne hyperspectral remote sensing images, and the area ratio of vegetation destruction within a 100-meter radius of the node was determined using a pixel statistical algorithm. At the same time, the area ratio of surface water coverage was measured. For the calculation of disaster indicators, the gradient influence coefficient is preset to... To reflect the dominant influence of steep terrain on the foundation, the vegetation destruction impact coefficient is preset to [value missing]. The collapse risk index of transmission line towers is calculated to reflect the pulling and damaging force of fallen trees on the towers. The geological gradient influence coefficient is preset to... To highlight the potential energy effect of high elevation difference in landslides, the water scour influence coefficient is preset to be \ To supplement the weight of soil erosion, calculate the geological disaster susceptibility index. The depth parameter of the local catchment depression was determined as follows: The water catchment-convergence conversion coefficient is preset to 1. The flood depth was calculated to match the soil runoff saturation characteristics of the area. In the comprehensive probability evaluation, the gradient weights are preset to... The collapse risk weight is preset to The weight of geological disasters is preset to be The water depth weight is preset to This weighting is derived from statistical regression analysis of historical causes of rescue obstruction to ensure that all disaster factors take effect evenly. The probability value of node blockage is calculated. The preset probability threshold is set at 0.50, which is determined based on historical extreme values ​​of the wading limit and off-road climbing limit of the repair vehicle chassis. A comparison shows that 0.51 is greater than 0.50, thus marking the candidate road node as a traffic-blocking node. Similarly, another connected node is also marked as a traffic-blocking node, and the path between the two points is marked as an associated disaster-affected road segment, while the path connecting to nodes that have not exceeded the threshold is marked as a passable edge link. The logical relationships between all points and lines are summarized, and an adjacency matrix with connectivity status labels is generated in memory, thereby constructing the final emergency passage topology network.

[0040] Optionally, generating the node blockage probability value for each of the candidate road nodes based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the floodwater depth value includes: Independent risk assessment weight parameters are configured for the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value, respectively; Based on the risk assessment weight parameters, a weighted fusion calculation is performed on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value to construct a weighted multi-indicator comprehensive judgment model; The node blocking probability value is output through the weighted multi-index comprehensive judgment model.

[0041] Specifically, independent risk assessment weight parameters are assigned to the topographic relief gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value. Based on the risk assessment weight parameters, a weighted fusion calculation is performed on the topographic relief gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value to construct a weighted multi-indicator comprehensive judgment model. The corresponding weighted fusion calculation formula is as follows: ; P represents the node blocking probability value, and G represents the terrain undulation gradient value. This represents the risk index of power transmission line tower collapse. Represents the susceptibility index to geological disasters. Represents the depth of floodwater. Risk assessment weight parameters representing the corresponding terrain relief gradient values. The risk assessment weighting parameter represents the risk index of the corresponding transmission line tower collapse risk index. The risk assessment weight parameters represent the corresponding geological hazard susceptibility index. The risk assessment weight parameters represent the corresponding flood depth values; the weighted multi-indicator comprehensive judgment model outputs the node blockage probability value; where, the risk assessment weight parameters are normalized numerical coefficients that measure the proportion of influence of each disaster indicator in the process of causing the road to completely lose its passability; the weighted fusion operation is a mathematical calculation method that multiplies multiple evaluation indicators of different dimensions by their respective weight coefficients and then performs linear summation to obtain the comprehensive evaluation result; the weighted multi-indicator comprehensive judgment model is a mathematical evaluation framework that integrates multi-source risk factors such as topography, geology, hydrology, and power facility damage to quantify the comprehensive disaster severity of nodes. Figure 5 The diagram shown is a radar schematic of the weighted multi-index comprehensive risk assessment model of the present invention.

[0042] For example, the terrain undulation gradient value G=0.30 obtained from the previous extraction and calculation of a candidate road node is read, and the transmission line tower collapse risk index is calculated. Geological disaster susceptibility index Floodwater depth Then, the system retrieves the risk assessment weight parameters from the built-in storage configuration table. The default risk assessment weight parameters corresponding to the terrain undulation gradient values ​​are... The weighting benchmark is based on the statistical fact that the incidence of rigid obstruction to the chassis clearance of modern emergency repair vehicles caused by simple terrain undulations is relatively low. The risk assessment weighting parameters corresponding to the transmission line tower collapse risk index are preset as follows: Its benchmark is based on the extremely high priority of blocking conditions caused by the collapse of high-voltage power line towers, which directly cut off roads and pose a fatal risk of electric shock. The risk assessment weight parameters corresponding to the geological disaster susceptibility index are preset as follows: Its benchmark is based on the severity of damage caused by landslides and debris flows that completely collapse and destroy the roadbed. The risk assessment weight parameters corresponding to the floodwater depth are preset as follows: The benchmark is based on the absolute probability of vehicle mechanical failure caused by water accumulation exceeding the engine air intake height. The sum of the four weight parameters is exactly equal to one to meet normalization requirements. The aforementioned indicator values ​​and their corresponding risk assessment weight parameters are substituted into the weighted multi-indicator comprehensive judgment model for weighted fusion calculation. The calculation process is as follows: After successive multiplication, the weighted scores for each item are 0.03, 0.117, 0.123, and 0.24. The system then performs a linear summation of the four weighted scores, deriving the final node blocking probability value P = 0.51.

[0043] Optionally, the step of performing emergency rescue path optimization calculations based on the emergency channel topology network to generate power emergency channel evaluation results for the target area includes: Extract the travel distance consumption parameters and road surface resistance parameters of multiple passable edge links included in the emergency passage topology network; Receive the coordinates of the emergency repair start point and the coordinates of the power fault end point from the external system; Based on the travel distance consumption parameter, the road surface resistance parameter, the coordinates of the emergency repair start point, and the coordinates of the power failure end point, multiple candidate emergency repair routes are planned, and the comprehensive cost weight value of each candidate emergency repair route is calculated. The candidate repair routes are sorted in ascending order of their comprehensive cost weight values, and the top-ranked target repair route is selected as the evaluation result of the power emergency channel.

[0044] Specifically, based on the constructed channel data framework, the travel distance cost parameters and road surface resistance parameters of multiple passable edge links in the emergency channel topology network are extracted; simultaneously, the coordinates of the repair start point and the power fault end point are received from the external system; then, based on the travel distance cost parameters, road surface resistance parameters, and the coordinates of the repair start point and the power fault end point, multiple candidate repair routes connecting the start and end points are planned using a graph theory shortest path algorithm, and the comprehensive cost weight value of each candidate repair route is calculated; the corresponding comprehensive cost weight value calculation formula is as follows: ; C represents the overall cost weight value, and n represents the number of accessible edge links included in the candidate repair route. This represents the travel distance cost parameter corresponding to the i-th accessible edge link. Represents the first preset weight ratio. This represents the road surface resistance parameter corresponding to the i-th passable edge link. This represents the second preset weight ratio; finally, multiple candidate repair routes are sorted in ascending order of comprehensive cost weight value, and the top-ranked target repair route is selected as the evaluation result of the power emergency channel. Among them, the travel distance cost parameter is a value representing the actual physical space length between the two ends of the edge link; the road surface resistance parameter is a physical indicator measuring the degree of speed reduction of vehicles due to mud or gravel on the affected road surface; the repair start point coordinates are the spatial location data of the rescue team or material assembly point; the power fault end point coordinates are the location data of the damaged power facility requiring emergency repair; the candidate repair route is a potential travel trajectory in the topology network connecting the start and end points and consisting entirely of passable edge links; the comprehensive cost weight value is a comprehensive quantitative indicator that evaluates the overall rescue efficiency of the route after comprehensively considering travel distance and road passability; and the target repair route is the emergency rescue execution path with the lowest comprehensive cost selected by the evaluation model and deemed optimal. Figure 6 The diagram shown is a visualization of the emergency channel topology network and path cost based on graph theory according to the present invention.

[0045] For example, the coordinates of the emergency repair starting point received from the external system via the interface are: and The coordinates of the power fault endpoint are and Two candidate repair routes were explored and planned in the emergency access road topology network using Dijkstra's graph theory search algorithm. Analysis of the first candidate repair route revealed that it consists of two sequentially connected accessible edge links. The travel distance cost parameter of the first accessible edge link was extracted. and road surface medium resistance parameters Extract the travel distance cost parameter of the second accessible edge link. and road surface medium resistance parameters Analysis of the second candidate repair route revealed that it consists of only one continuous, passable edge link. The travel distance cost parameter of this link was extracted. and road surface medium resistance parameters The built-in parameter configuration table is retrieved, and the first preset weight ratio is set to... This setting is based on the physical conversion efficiency coefficient between the standard fuel consumption per 100 kilometers and the base speed of rescue vehicles on unpaved roads, and the second preset weight ratio is set as follows: This setting is based on statistical regression analysis of post-disaster relief data, which shows that delays caused by vehicles getting stuck due to slippery or muddy roads significantly impact overall rescue efficiency compared to pure distance factors. Substituting these parameters into the comprehensive cost weighting formula, the first candidate repair route is calculated. The costs of link one and link two were calculated as 2.0 + 0.12 = 2.12 and 1.2 + 0.24 = 1.44 respectively. These costs were then summed to obtain the overall cost weight value for the first candidate repair route. The calculations for the second candidate repair route yielded the following results. The comprehensive cost weight value of the second candidate repair route was calculated. After obtaining the two calculation results, 3.56 and 4.06, a sorting operation is performed in ascending order. Since the value of 3.56 is less than 4.06, the first candidate repair route is placed first. The first candidate repair route is directly selected as the power emergency channel evaluation result output to the dispatch center.

[0046] Optionally, calculating the comprehensive cost weight value for each of the candidate repair routes includes: Assign a first preset weight ratio to the travel distance consumption parameter; Assign a second preset weight ratio to the road surface medium resistance parameter; The comprehensive cost weight value of each candidate repair route is generated by multiplying the travel distance cost parameter by the first preset weight ratio and adding the product of the road surface resistance parameter by the second preset weight ratio.

[0047] Specifically, a first preset weight ratio is assigned to the extracted travel distance cost parameter, and a second preset weight ratio is assigned to the extracted road surface resistance parameter. The product of the travel distance cost parameter and the first preset weight ratio is added to the product of the road surface resistance parameter and the second preset weight ratio to generate the comprehensive cost weight value for each candidate repair route. The corresponding comprehensive cost weight value calculation formula is as follows: ; C represents the overall cost weight value, and D represents the travel distance consumption parameter. R represents the first preset weight ratio, and R represents the road surface medium resistance parameter. The first preset weight ratio is a normalized numerical coefficient that measures the proportion of the physical space span in the overall rescue time delay. The second preset weight ratio is a normalized numerical coefficient that measures the proportion of the impact of reduced friction or bumps on the road surface on vehicle speed. The comprehensive cost weight value is a mathematical indicator used to quantitatively evaluate the overall rescue passage cost of a single route after taking into account both spatial distance and road environment factors.

[0048] For example, in a specific calculation execution node, the travel distance cost parameter D=15.0 for a specific candidate emergency repair route is extracted, and the road surface resistance parameter R=0.8 for that route is also extracted. A preset weight allocation scheme is retrieved from the database, and a first preset weight ratio is assigned to the travel distance cost parameter. This weighting scheme is determined based on historical statistical data on the percentage of time spent by conventional rescue vehicles on standard roads. Simultaneously, a second preset weighting ratio is assigned to the road surface resistance parameter. This weighting scheme is determined based on historical statistical data on the proportion of extreme delays such as speed reduction and skidding caused by complex road conditions after a disaster. Substituting the above parameters into the comprehensive cost weighting value calculation formula, numerical substitution and derivation are performed to calculate the product of the travel distance cost parameter and the first preset weighting ratio. The process of calculating the product of the road surface medium resistance parameter and the second preset weight ratio is as follows: Finally, the above two multiplications are added together to obtain the comprehensive cost weight value C = 6.0 + 0.48 = 6.48 for the candidate repair route.

[0049] Optionally, the method further includes: After generating the power emergency channel assessment results for the target area, the power emergency channel assessment results are converted into an executable dispatch instruction format; The executable scheduling instruction format is sent to an external network via a communication network to trigger the power emergency repair terminal that receives the executable scheduling instruction format to perform route navigation actions according to the power emergency channel assessment results.

[0050] Specifically, after generating the power emergency corridor assessment results for the target area, the assessment results are converted into an executable dispatch instruction format. This conversion process is based on data serialization and communication frame encapsulation algorithms, integrating the route coordinate string into a binary stream data packet readable by the underlying hardware. The corresponding instruction data packet volume calculation formula is as follows: ; H represents the total size of the instruction data packet, H represents the protocol header size, and N represents the number of route nodes included in the evaluation result. The volume represents the coordinate data of a single node, and T represents the volume of the check tail. The power emergency channel evaluation result is a data set containing the spatial topology of the optimal repair route, output by a path optimization algorithm. The executable scheduling command format is a standardized binary data structure that conforms to a specific communication protocol and can be directly parsed by hardware terminal devices to drive the navigation module. Subsequently, the system sends the executable scheduling command format to the external network via the communication network to trigger the power repair terminal receiving the executable scheduling command format to execute route navigation actions according to the power emergency channel evaluation result. The corresponding command transmission delay calculation formula is as follows: ; B represents the instruction transmission delay, and B represents the available bandwidth parameter of the communication network. The parameter represents the physical propagation delay of the signal; among them, the communication network is the wireless satellite communication or cellular base station transmission link established inside and outside the disaster area for data interaction; the external network is the front-line command system or wide area Internet node independent of the core server of the emergency management module; the power repair terminal is an intelligent computing device with satellite positioning and electronic map rendering functions carried by rescue and repair personnel or engineering vehicles; and the route navigation action is the software execution process in which the terminal device draws the guiding trajectory on the screen panel and provides real-time deviation correction.

[0051] For example, the system reads that the current power emergency channel assessment result includes a route node count N=50. The system retrieves the built-in communication encapsulation template and configures the protocol header size H=128 bits. This configuration is based on the physical overhead of source and destination address routing required for compatibility with standard underlying network communication protocols; it also configures the size of individual node coordinate data. The configuration of 32 bits is based on the minimum space required for double-precision floating-point compression encoding of longitude and latitude to balance positioning accuracy and data lightweighting; the configuration of the check tail volume T=32 bits is based on the standard physical bit width required for the cyclic redundancy check algorithm to detect transmission errors. The system substitutes the above parameters into the instruction data packet volume calculation formula and performs addition and multiplication derivation. The calculation process is as follows: The product of the intermediate node data volumes is 3200. By summing these values, the total volume of the final instruction data packet is derived. Bits. The lightweight command data packet is then pushed to the communication network's transmission queue, and the available bandwidth parameter B = 2048 bits per second in the disaster area is read in real time. This value is based on the physical bandwidth limit of communication relying solely on the satellite-borne narrowband emergency channel after widespread damage to conventional ground base stations under extreme weather conditions; the system also reads the signal physical propagation delay parameter. The value is seconds, which is based on the fixed spatial time required for electromagnetic wave signals to travel round-trip between a surface terminal and a geostationary orbit communication satellite. The system substitutes the total data packet volume and network environment parameters into the command transmission delay calculation formula, performing division and addition operations. The calculation process is as follows: The actual data stream transmission time was found to be 1.64 seconds. Adding the physical delay, the final command transmission delay was derived. Second.

[0052] Optionally, converting the power emergency channel assessment results into an executable dispatch instruction format includes: Extract the latitude and longitude coordinate sequence of the route nodes contained in the power emergency channel assessment results; Obtain network fluctuation characteristic parameters of the external network under post-disaster communication interruption conditions; A lightweight emergency communication protocol is triggered based on the network fluctuation characteristic parameters to convert the latitude and longitude coordinate sequence of the route nodes into a serialized data stream carrying a breakpoint resume identifier. Obtain the local cached disaster map of the power emergency repair terminal; By comparing the serialized data stream with the difference nodes contained in the locally cached disaster map, an incremental update data packet is generated as the executable scheduling instruction format.

[0053] Specifically, the system extracts the latitude and longitude coordinate sequences of route nodes from the power emergency channel assessment results; it also acquires network fluctuation characteristic parameters of the external network under post-disaster communication interruption conditions in real time; the corresponding calculation formulas for these network fluctuation characteristic parameters are as follows: ; F represents the network fluctuation characteristic parameter. Represents packet loss rate. Represents the currently available channel bandwidth. This represents the average jitter latency of network transmission; a lightweight emergency communication protocol is triggered based on network fluctuation characteristic parameters to convert the latitude and longitude coordinate sequence of route nodes into a serialized data stream carrying breakpoint resumption identifiers; the local cached disaster map of the power repair terminal is acquired simultaneously; the serialized data stream and the locally cached disaster map are compared to identify the different nodes, and an incremental update data packet is generated as an executable scheduling instruction format; the corresponding incremental update data packet volume calculation formula is as follows: ; The total size of the incremental update data packet represents the total size of the packet, and k represents the number of difference nodes extracted during the comparison. The coordinates and attribute data volume representing a single difference node. The additional volume represents the breakpoint resume identifier and protocol control header; among them, the route node latitude and longitude coordinate sequence is a set of locations composed of absolute geographic coordinate points on the Earth's surface arranged in the order of travel; the network fluctuation characteristic parameter is a physical assessment index that quantifies the signal loss and delay instability that occurs during data transmission in the damaged communication link after the disaster; the lightweight emergency communication protocol is a low-bandwidth and high packet loss rate extreme network environment with a simplified data packet header and optimized retransmission mechanism as the underlying communication rules; the breakpoint resume identifier is a control character field used to record the location of the interruption in data stream transmission so that the transmission can continue from the interruption point after the network is restored; the serialized data stream is a data form that converts the structured route coordinate data into a continuous one-dimensional byte sequence for network transmission; the local cached disaster map is the basic road network and terrain data of the disaster area pre-downloaded and stored in physical memory by the power repair terminal before the network outage; and the incremental update data packet is an efficient instruction carrier that only contains the latest route node information that is missing or changed locally on the terminal and does not contain globally duplicated data.

[0054] For example, at a specific command-issuing node, a sequence of latitude and longitude coordinates of route nodes containing fifty location points is extracted. The real-time physical status of the external network is obtained by sending probes, and the current packet loss rate is read. Read the currently available channel bandwidth Bits per second, reading the average jitter latency of network transmission Milliseconds. Substituting the aforementioned parameters into the network fluctuation characteristic parameter calculation formula and performing multiplication and division derivation, we obtain... The built-in lightweight protocol trigger threshold is set to 0.020, which is determined based on the historical communication interruption probability critical point of the satellite narrowband channel when the signal-to-noise ratio is lower than the standard value. If the calculated value of 0.035 is greater than the threshold of 0.020, the lightweight emergency communication protocol is automatically triggered, encoding the coordinate sequence into a serialized data stream carrying a breakpoint resumption identifier. The system queries the terminal registry to obtain the local cached disaster map version of the corresponding power repair terminal. Through a hash comparison algorithm, it finds that forty-five nodes overlap with the locally cached old version of the safety road network nodes, accurately extracting the number of differing nodes k=5. The system retrieves the protocol configuration parameters to configure the coordinates and attribute data volume of a single differing node. This configuration is based on the bit width of the simplified compressed encoding of double-precision latitude and longitude coordinates, and includes the additional size of the breakpoint resume flag and protocol control header. The bit width configuration is determined based on the minimum state machine record bit width requirement of the emergency protocol packet header. The system substitutes the parameters into the incremental update data packet volume calculation formula and performs multiplication and addition calculations as follows: The total size of the incremental update data packet is derived. Bits. Finally, this 352-bit ultra-small incremental update data packet is pushed into the transmission queue as an executable scheduling instruction format.

[0055] Reference Figure 4 Based on the same inventive concept, this invention also provides a power emergency channel assessment system based on spaceborne hyperspectral imaging, the system comprising: The receiving module is used to receive onboard hyperspectral remote sensing images and geographic elevation datasets of the target area transmitted via the satellite communication interface. The extraction and construction module is used to extract the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image, and construct a three-dimensional geomorphological model of the target area based on the surface material feature vector matrix and the geographic elevation dataset. The identification module is used to identify surface disaster elements in the three-dimensional terrain model and generate an emergency passage topology network that includes multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links. The evaluation module is used to perform emergency rescue path optimization calculations based on the emergency channel topology network and generate evaluation results for the power emergency channels in the target area.

[0056] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0057] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for evaluating power emergency channels based on spaceborne hyperspectral imaging, characterized in that, The method includes: Receives satellite-borne hyperspectral remote sensing images and geographic elevation datasets of the target area transmitted via the satellite communication interface; Extract the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image, and construct a three-dimensional geomorphological model of the target area based on the surface material feature vector matrix and the geographic elevation dataset; The surface disaster element identification is performed on the three-dimensional terrain model to generate an emergency passage topology network containing multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links; Based on the emergency channel topology network, perform emergency rescue path optimization calculations to generate power emergency channel evaluation results for the target area.

2. The power emergency channel assessment method based on spaceborne hyperspectral imaging as described in claim 1, characterized in that, The extraction of the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image includes: Read the pixel radiance values ​​of multiple target spectral bands contained in the spaceborne hyperspectral remote sensing image; An atmospheric correction algorithm is applied to the pixel radiance values ​​of the multiple target spectral bands to generate corresponding atmospheric corrected reflectance sequences. The vegetation cover parameters and water distribution parameters are extracted from the atmospheric corrected reflectance sequence, and the vegetation cover parameters and water distribution parameters are combined to generate the surface material feature vector matrix.

3. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 2, characterized in that, The extraction of vegetation cover parameters and water distribution parameters from the atmospheric corrected reflectance sequence includes: The near-infrared reflectance and the red light reflectance are separated from the atmospheric corrected reflectance sequence; The vegetation cover parameter is calculated based on the ratio of the difference between the near-infrared band reflectance and the red band reflectance to the sum of their values. The green light band reflectance and the mid-infrared band reflectance are separated from the atmospheric corrected reflectance sequence; The water body distribution parameters are obtained by calculating the normalized difference between the green light band reflectance and the mid-infrared band reflectance.

4. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 1, characterized in that, The step of performing surface disaster element identification on the three-dimensional terrain model to generate an emergency passage topology network containing multiple access blockage nodes, associated disaster-stricken road sections, and passable edge links includes: Extract the three-dimensional coordinate parameters of multiple candidate road nodes distributed from the three-dimensional terrain model; Calculate the terrain undulation gradient between two adjacent candidate road nodes; Extract the surface water coverage area and vegetation destruction area corresponding to the candidate road node from the spaceborne hyperspectral remote sensing image; By combining the terrain undulation gradient values, the surface water coverage area, and the vegetation destruction area, the power transmission line tower collapse risk index, geological disaster susceptibility index, and flood water depth value corresponding to the candidate road nodes are generated. Based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value, the node blockage probability value of each candidate road node is generated; In response to the node blocking probability value being greater than a preset probability threshold, the corresponding candidate road node is marked as the traffic blocking node, the path connecting two traffic blocking nodes is marked as the associated disaster-stricken road segment, and the path not marked as the associated disaster-stricken road segment is marked as the passable edge link; The emergency passage topology network is constructed by connecting the spatial locations of all the aforementioned traffic-blocking nodes, all the associated disaster-stricken road sections, and all the aforementioned passable edge links.

5. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 4, characterized in that, The generation of node blockage probability values ​​for each candidate road node based on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the floodwater depth value includes: Independent risk assessment weight parameters are configured for the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value, respectively; Based on the risk assessment weight parameters, a weighted fusion calculation is performed on the terrain undulation gradient value, the transmission line tower collapse risk index, the geological disaster susceptibility index, and the flood water depth value to construct a weighted multi-indicator comprehensive judgment model; The node blocking probability value is output through the weighted multi-index comprehensive judgment model.

6. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 1, characterized in that, The step of performing emergency rescue path optimization calculations based on the emergency channel topology network to generate power emergency channel evaluation results for the target area includes: Extract the travel distance consumption parameters and road surface resistance parameters of the multiple passable edge links contained in the emergency passage topology network; Receive the coordinates of the emergency repair start point and the coordinates of the power fault end point from the external system; Based on the travel distance consumption parameter, the road surface resistance parameter, the coordinates of the emergency repair start point, and the coordinates of the power failure end point, multiple candidate emergency repair routes are planned, and the comprehensive cost weight value of each candidate emergency repair route is calculated. The candidate repair routes are sorted in ascending order of their comprehensive cost weight values, and the top-ranked target repair route is selected as the evaluation result of the power emergency channel.

7. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 6, characterized in that, The calculation of the comprehensive cost weight value for each of the candidate repair routes includes: Assign a first preset weight ratio to the travel distance consumption parameter; Assign a second preset weight ratio to the road surface medium resistance parameter; The comprehensive cost weight value of each candidate repair route is generated by multiplying the travel distance cost parameter by the first preset weight ratio and adding the product of the road surface resistance parameter by the second preset weight ratio.

8. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 1, characterized in that, The method further includes: After generating the power emergency channel assessment results for the target area, the power emergency channel assessment results are converted into an executable dispatch instruction format; The executable scheduling instruction format is sent to an external network via a communication network to trigger the power emergency repair terminal that receives the executable scheduling instruction format to perform route navigation actions according to the power emergency channel assessment results.

9. The power emergency channel assessment method based on spaceborne hyperspectral imaging according to claim 8, characterized in that, The process of converting the power emergency channel assessment results into an executable dispatch instruction format includes: Extract the latitude and longitude coordinate sequence of the route nodes contained in the power emergency channel assessment results; Obtain network fluctuation characteristic parameters of the external network under post-disaster communication interruption conditions; A lightweight emergency communication protocol is triggered based on the network fluctuation characteristic parameters to convert the latitude and longitude coordinate sequence of the route nodes into a serialized data stream carrying a breakpoint resume identifier. Obtain the local cached disaster map of the power emergency repair terminal; By comparing the serialized data stream with the difference nodes contained in the locally cached disaster map, an incremental update data packet is generated as the executable scheduling instruction format.

10. A power emergency channel assessment system based on spaceborne hyperspectral imaging, applied to the power emergency channel assessment method based on spaceborne hyperspectral imaging as described in any one of claims 1-9, characterized in that, The system includes: The receiving module is used to receive onboard hyperspectral remote sensing images and geographic elevation datasets of the target area transmitted via the satellite communication interface. The extraction and construction module is used to extract the surface material feature vector matrix from the spaceborne hyperspectral remote sensing image, and construct a three-dimensional geomorphological model of the target area based on the surface material feature vector matrix and the geographic elevation dataset. The identification module is used to identify surface disaster elements in the three-dimensional terrain model and generate an emergency passage topology network that includes multiple traffic blockage nodes, associated disaster-stricken road sections, and passable edge links. The evaluation module is used to perform emergency rescue path optimization calculations based on the emergency channel topology network and generate evaluation results for the power emergency channels in the target area.

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