Line safety margin assessment method and system under influence of mountain fire

Abstract

The invention discloses a line safety margin assessment method and system under the influence of a forest fire, and relates to the field of power systems and automation thereof. The method comprises the following steps: S1, acquiring spatial information of a power transmission line based on a power grid geographic information system, generating a path evaluation point set, and acquiring multi-source real-time monitoring data of an area where the path evaluation point set is located by using a space-air-ground integrated monitoring network; s2, performing synchronization and space registration processing on the multi-source real-time monitoring data, performing reconstruction to obtain a dynamic fire scene parameter set, and calculating a line heat stress index; s3, performing Monte Carlo scene simulation by adopting a probability risk assessment method based on the dynamic fire scene parameter set and the line heat stress index, and generating a probabilistic safety margin index; and S4, determining a risk level according to the probabilistic safety margin index and the line heat stress index, and outputting a visual risk assessment result. And through multi-source data fusion and probabilistic evaluation, cumulative, precise and robust safety margin judgment of the power transmission line under the forest fire is realized.

Description

Technical Field

[0001] This invention relates to the field of power systems and their automation, specifically to a method and system for assessing the safety margin of power lines under the influence of wildfires. Background Technology

[0002] As a core component of the power grid, transmission lines are widely distributed in mountainous and forested areas, posing a serious safety threat during the peak wildfire season. The high temperatures, dense smoke, and flames generated by wildfires significantly reduce the strength of air insulation, causing line flashover and tripping. At the same time, high temperatures may lead to a decline in the mechanical properties of conductors and an increase in sag, endangering the safe and stable operation of the power grid.

[0003] Existing methods for monitoring and assessing wildfires along power lines largely rely on satellite remote sensing, fixed meteorological sensors, and video surveillance. While these methods can achieve basic fire detection and alarm functions, they still have significant shortcomings in accurately assessing the safety margin of power lines. On the one hand, existing monitoring methods acquire data with limited dimensions, making it difficult to accurately capture key fire parameters affecting power line safety, such as dynamic information like flame height, radiant heat flux, and fire spread rate, resulting in insufficient assessment basis. On the other hand, existing assessment methods mostly employ deterministic models, failing to fully consider the randomness and uncertainty in the development of wildfires, thus affecting the accuracy and reliability of the assessment results.

[0004] Therefore, it is necessary to develop an assessment method that can integrate multi-dimensional fire information and effectively handle its random characteristics in order to improve the accuracy and practicality of line safety risk assessment in wildfire situations. Summary of the Invention

[0005] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide a method and system for assessing the safety margin of power lines under the influence of wildfires, so as to solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the safety margin of power lines under the influence of wildfires, comprising:

[0007] S1: Based on the power grid geographic information system, spatial information of transmission lines is obtained to generate a path evaluation point set. At the same time, multi-source real-time monitoring data of the area where the path evaluation point set is located is obtained using the integrated air-space-ground monitoring network.

[0008] S2: Synchronize and spatially register multi-source real-time monitoring data to reconstruct a dynamic fire field parameter set and calculate the line thermal stress index;

[0009] S3: Based on the dynamic fire scene parameter set and the line thermal stress index, a probabilistic risk assessment method is used to simulate the Monte Carlo scenario and generate a probabilistic safety margin index.

[0010] S4: Determine the risk level based on the probabilistic safety margin index and the line thermal stress index, and output the visualized risk assessment results.

[0011] The present invention is further configured such that S1 includes:

[0012] Data on transmission line towers is obtained from a geographic information system or a power grid resource map. The tower data includes coordinate data and elevation data.

[0013] Based on tower data, continuous line paths are generated through curve interpolation;

[0014] Sampling is performed along the route at preset basic intervals to form basic path evaluation points;

[0015] By combining historical wildfire data, digital elevation models, and vegetation cover data, high-risk areas are identified.

[0016] In high-risk sections, the basic path assessment points are sampled more densely, and the basic path assessment points are combined to form a path assessment point set.

[0017] The present invention is further configured such that the integrated air-space-ground monitoring network includes: a fixed sensor network and a UAV mobile monitoring cluster;

[0018] The fixed sensor network includes: meteorological sensors, heat flux sensors, and dual-spectrum cameras deployed at path assessment points;

[0019] The mobile monitoring cluster of drones includes drones equipped with infrared thermal imagers, multispectral imagers, lidar, and mini weather stations.

[0020] The present invention is further configured such that the dynamic fire field parameter set of S2 includes: flame height, radiant heat flux and fire spread rate.

[0021] The present invention is further configured such that the synchronization and spatial registration process of S2 includes:

[0022] Time synchronization and spatial registration of multi-source real-time monitoring data;

[0023] Based on the registered multi-source real-time monitoring data, the flame height is calculated using a stereo vision algorithm;

[0024] Based on the registered multi-source real-time monitoring data, the radiative heat flux reaching the line location is calculated using a radiative transfer model.

[0025] Based on the registered multi-source real-time monitoring data, the fire spread rate is calculated using the optical flow method.

[0026] The present invention is further configured such that the calculation of the circuit thermal stress index in S2 includes:

[0027] Based on the radiative heat flux in the dynamic fire field parameter set and the ambient temperature and wind speed data in the registered multi-source real-time monitoring data, the instantaneous heat load intensity of the line is calculated.

[0028] Based on the time series of instantaneous heat load intensity, combined with the pre-set specific heat capacity and mass per unit length of the line material, the cumulative heat exposure of the line is calculated through a time integral model with a decay function.

[0029] The cumulative heat exposure is normalized to a preset critical heat load threshold to generate a line thermal stress index.

[0030] The present invention is further configured such that S3 includes:

[0031] Based on the dynamic fire field parameter set and the line thermal stress index, probability distributions are defined for the radiative heat flux, fire spread rate and line thermal stress index.

[0032] The Monte Carlo method is used to extract a large number of random scenarios from the probability distribution to simulate the uncertainty of the fire environment and the thermal state of the power lines.

[0033] The present invention is further configured such that S3 further includes:

[0034] For each random scenario, calculate each safety margin in parallel:

[0035] The thermal stress safety margin is obtained by calculating the difference between 1 and the thermal stress index of the line under the current scenario;

[0036] The electrical insulation safety margin is obtained by calculating the ratio of the preset electrical insulation withstand strength to the line operating voltage.

[0037] Calculate mechanical load based on wind speed and conductor status from registered multi-source real-time monitoring data;

[0038] The mechanical strength safety margin is obtained by calculating the ratio of the preset material yield strength to the mechanical load.

[0039] Statistical analysis is performed on the safety margins calculated under all random scenarios to generate probabilistic safety margin indices.

[0040] The present invention is further configured such that S4 includes:

[0041] The risk level is determined by mapping the probabilistic safety margin index and the line thermal stress index through a preset risk threshold.

[0042] On the electronic map, the risk level is associated with the spatial location of the path assessment point set and visualized to form a risk corridor map;

[0043] Generate early warning information and handling suggestions corresponding to the risk level. The handling suggestions include: reducing load or shutting down the line.

[0044] This invention also provides a system for assessing the safety margin of power lines under the influence of wildfires, the system comprising:

[0045] Data perception and acquisition module: Based on the power grid geographic information system, it acquires spatial information of transmission lines, generates a path assessment point set, and at the same time uses the integrated air-space-ground monitoring network to acquire multi-source real-time monitoring data of the area where the path assessment point set is located;

[0046] Parameter reconstruction and fusion module: performs synchronization and spatial registration processing on multi-source real-time monitoring data, reconstructs a dynamic fire field parameter set, and calculates the line thermal stress index;

[0047] Safety margin assessment module: Based on dynamic fire scene parameter set and line thermal stress index, Monte Carlo scenario simulation is carried out using probabilistic risk assessment method to generate probabilistic safety margin index;

[0048] Visualization output module: Based on the probabilistic safety margin index and the line thermal stress index, the risk level is determined and the visualized risk assessment results are output.

[0049] This invention provides a method and system for assessing the safety margin of transmission lines under the influence of wildfires. The method comprises: S1: acquiring spatial information of transmission lines based on a power grid geographic information system to generate a path assessment point set, and simultaneously acquiring multi-source real-time monitoring data of the area where the path assessment point set is located using an integrated air-space-ground monitoring network; S2: performing synchronization and spatial registration processing on the multi-source real-time monitoring data to reconstruct a dynamic fire field parameter set and calculate the line thermal stress index; S3: based on the dynamic fire field parameter set and the line thermal stress index, performing Monte Carlo scenario simulation using a probabilistic risk assessment method to generate a probabilistic safety margin index; S4: determining the risk level based on the probabilistic safety margin index and the line thermal stress index, and outputting a visualized risk assessment result. The beneficial effects include:

[0050] It enables a cumulative and forward-looking assessment of the thermal stress state of power lines: by introducing the "power line thermal stress index", the instantaneous thermal load is combined with the cumulative effect over time to reflect the thermal damage process of power lines under the influence of a continuous fire.

[0051] To address the issue of weak assessment parameters and improve the accuracy of assessments: By constructing an "integrated air-space-ground monitoring network" and fusing multi-source data, a set of key dynamic fire scene parameters such as flame height and radiant heat flux can be reconstructed.

[0052] It provides robust probabilistic safety boundaries to support risk-aware decision-making: Employing a probabilistic risk assessment framework, it quantifies uncertainties in wildfire environments through Monte Carlo simulations, ultimately outputting indicators including quantile safety margins and failure probabilities. This enables decision-makers to clearly understand the confidence level of the assessment results, thereby making a scientific and reliable risk trade-off between "ensuring safety" and "ensuring power supply."

[0053] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. 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. In the drawings:

[0055] Figure 1 A flowchart illustrating a method for assessing the safety margin of a power line under the influence of a wildfire, as shown in an exemplary embodiment of the present invention;

[0056] Figure 2 This is a schematic diagram illustrating the structure of a line safety margin assessment system under the influence of wildfires, as an exemplary embodiment of the present invention. Detailed Implementation

[0057] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0058] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0059] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0060] Example 1:

[0061] A method for assessing the safety margin of power lines under the impact of wildfires, such as Figure 1 As shown, it includes:

[0062] S1: Based on the power grid geographic information system, spatial information of transmission lines is obtained to generate a path evaluation point set. At the same time, multi-source real-time monitoring data of the area where the path evaluation point set is located is obtained using the integrated air-space-ground monitoring network.

[0063] S2: Synchronize and spatially register multi-source real-time monitoring data to reconstruct a dynamic fire field parameter set and calculate the line thermal stress index;

[0064] S3: Based on the dynamic fire scene parameter set and the line thermal stress index, a probabilistic risk assessment method is used to simulate the Monte Carlo scenario and generate a probabilistic safety margin index.

[0065] S4: Determine the risk level based on the probabilistic safety margin index and the line thermal stress index, and output the visualized risk assessment results.

[0066] The present invention is further configured such that S1 includes:

[0067] Data on transmission line towers is obtained from a geographic information system or a power grid resource map. The tower data includes coordinate data and elevation data.

[0068] Based on tower data, continuous line paths are generated through curve interpolation;

[0069] Sampling is performed along the route at preset basic intervals to form basic path evaluation points;

[0070] By combining historical wildfire data, digital elevation models, and vegetation cover data, high-risk areas are identified.

[0071] Within high-risk sections, basic path assessment points are densely sampled, and combined with these basic path assessment points to form a path assessment point set. Specifically, firstly, the RESTful API interface of the power grid geographic information system (GIS platform) is called, and the latitude, longitude coordinates and elevation data of all towers on the line are obtained by inputting the target line number; then, the B-spline curve interpolation algorithm is used to interpolate the coordinates of adjacent towers to generate a continuous line path composed of dense points; then, an equal arc length sampling algorithm is executed along the continuous line path to generate basic path assessment points at fixed intervals of 50 meters. Subsequently, the basic assessment points are overlaid and analyzed with multi-source spatial data: the historical wildfire database of the past five years is read, and a 500-meter buffer zone is established with each assessment point as the center to count the frequency of fire points; the slope value of each point is calculated in combination with digital elevation model data; and the vegetation coverage is calculated using satellite imagery and the normalized vegetation index algorithm; when a section simultaneously meets any two of the following conditions: historical fire point frequency exceeds 3 times, slope is greater than 25 degrees, and vegetation coverage is greater than 70%, the continuous section is marked as a high-risk section. Finally, the equal arc length sampling algorithm is re-executed in high-risk sections to densify the sampling interval to 10 meters, generating encrypted assessment points. All base points and encrypted points are then integrated into the final point set through a spatial database merging operation. This entire process can be implemented using a PostGIS spatial database and a Python geographic data processing library, ultimately outputting path assessment points with risk-adaptive resolution.

[0072] The present invention is further configured such that the integrated air-space-ground monitoring network includes: a fixed sensor network and a UAV mobile monitoring cluster;

[0073] The fixed sensor network includes: meteorological sensors, heat flux sensors, and dual-spectrum cameras deployed at path assessment points;

[0074] The UAV mobile monitoring cluster comprises UAVs equipped with infrared thermal imagers, multispectral imagers, lidar, and miniature weather stations. Specifically, the implementation process of the integrated air-ground-space monitoring network is as follows: A fixed sensor network is deployed using solar-powered IoT monitoring devices. Meteorological sensors are installed at each path assessment point to measure wind speed, direction, temperature, and humidity. Heat flux sensors are vertically aligned with the power line conductors to measure thermal radiation energy. Dual-spectrum cameras simultaneously acquire visible light video and infrared thermal imaging data. All fixed sensors communicate with the base station via 5G / 4G networks, with sampling frequencies set as follows: 1Hz for meteorological data, 10Hz for heat flux data, and 25 frames / second for video data. The UAV mobile monitoring cluster is equipped with a UAV platform, carrying infrared thermal imagers to measure fire temperature distribution, multispectral imagers to collect five-band spectral data, lidar to generate high-precision point cloud data, and miniature weather stations to measure three-dimensional wind fields and atmospheric parameters at the flight altitude. These data, combined with visible light video, thermal radiation energy, infrared thermal imaging, lidar point clouds, wind speed and direction, temperature and humidity, three-dimensional wind fields, and atmospheric parameters, form multi-source real-time monitoring data. The drone swarm automatically patrols along a preset route. Upon receiving an alarm signal from a fixed sensor, it automatically flies to the target area. The default flight altitude is set at 100-150 meters, and the flight speed is 5-8 meters per second. The collected data is transmitted back in real time via a 5G network. The monitoring data is integrated using a unified spatiotemporal reference. Both fixed sensor data and drone-collected data are synchronized at the millisecond level using GPS and BeiDou satellite timing systems, and the spatial coordinates are unified to the WGS84 coordinate system. When the fixed sensor detects a sudden temperature change or excessive heat flux, the system automatically generates a drone patrol task. The drone quickly arrives at the scene, generates a 3D model of the fire scene through lidar scanning, identifies the fire line location by combining multispectral data, and continuously monitors the distribution of flame thermal radiation using thermal imagers, forming a three-dimensional monitoring system that coordinates space, ground, and ground.

[0075] The present invention is further configured such that the dynamic fire field parameter set of S2 includes: flame height, radiant heat flux, and fire spread rate. Specifically, the flame height parameter refers to the vertical distance from the base to the top of the flame, used to assess the risk of direct contact between the flame and the transmission line; the radiant heat flux parameter refers to the thermal radiation energy received per unit area per unit time, used to quantify the thermal impact intensity of the fire on the line; and the fire spread rate parameter refers to the distance the fire line advances per unit time, used to predict the fire development trend and warning time. These three parameters together constitute the dynamic fire field parameter set, used to assess the impact of wildfires on transmission lines.

[0076] The present invention is further configured such that the synchronization and spatial registration process of S2 includes:

[0077] Time synchronization and spatial registration of multi-source real-time monitoring data;

[0078] Based on the registered multi-source real-time monitoring data, the flame height is calculated using a stereo vision algorithm;

[0079] Based on the registered multi-source real-time monitoring data, the radiative heat flux reaching the line location is calculated using a radiative transfer model.

[0080] Based on the registered multi-source real-time monitoring data, the fire spread rate is calculated using the optical flow method. Specifically, firstly, the multi-source real-time monitoring data collected by the integrated air-space-ground monitoring network undergoes unified spatiotemporal processing. Data from all fixed sensors and UAV payloads are synchronized at the millisecond level via GPS / BeiDou satellite timing systems, and their spatial coordinates are uniformly registered to the WGS-84 coordinate system. Based on the registered data, three key parameters are calculated: flame height, radiative heat flux, and fire spread rate. Based on the registered synchronous visible light and infrared thermal images collected by dual cameras on UAVs or fixed-position dual-spectrum cameras, the flame outline is reconstructed in three dimensions using the principle of binocular stereo vision, and the vertical distance from the base to the top of the flame is calculated to obtain the flame height. Based on the registered directional radiometer measurement data and infrared thermal imaging temperature data, the radiative flux is calculated using the Stefan-Boltzmann law, and the energy transfer process from the fire source to the power line is further corrected using an atmospheric attenuation model, ultimately obtaining the accurate radiative heat flux at the location of the power line. Based on the registered temporal visible light or thermal imaging video data, an improved Horn-Schunck optical flow algorithm is used to analyze the pixel displacement of the fire line between consecutive frames. Then, combined with the spatial registration parameters of the image (such as ground sampling distance), the pixel displacement is converted into actual distance, and then the advance speed of the fire line per unit time, i.e., the fire spread speed, is calculated.

[0081] The present invention is further configured such that the calculation of the circuit thermal stress index in S2 includes:

[0082] Based on the radiative heat flux in the dynamic fire field parameter set and the ambient temperature and wind speed data in the registered multi-source real-time monitoring data, the instantaneous heat load intensity of the line is calculated.

[0083] Based on the time series of instantaneous heat load intensity, combined with the pre-set specific heat capacity and mass per unit length of the line material, the cumulative heat exposure of the line is calculated through a time integral model with a decay function.

[0084] The cumulative heat exposure is normalized to a preset critical heat load threshold to generate a line thermal stress index. Specifically, the line thermal stress index is a dimensionless indicator characterizing the cumulative heat exposure of transmission lines under wildfire conditions. The required radiative heat flux parameters are derived from the results calculated by the radiative transfer model in a dynamic fire field parameter set. The ambient temperature parameters are derived from temperature sensor measurements in a fixed sensor network, and the wind speed parameters are derived from ultrasonic anemometer measurements in the same network. First, the instantaneous heat load intensity is calculated based on these parameters: instantaneous heat load intensity refers to the net heat absorbed by a unit area of ​​conductor per unit time. This is calculated by using the algebraic sum of radiative heat flux and convective heat dissipation, where the convective heat flux is calculated according to Newton's law of cooling based on ambient temperature and wind speed. Then, the cumulative heat exposure is calculated based on the time series of the instantaneous heat load intensity: cumulative heat exposure characterizes the effective heat energy accumulated by the conductor during continuous heating. This is achieved by introducing a time integral model of the heat decay function, where the specific heat capacity and mass per unit length of the line material are derived from the standard parameters of the conductor in the transmission line design specifications. The decay function adopts an exponential decay form to simulate the actual heat dissipation process of the conductor. Finally, the cumulative heat exposure and the critical heat load threshold are normalized to generate the line thermal stress index: the critical heat load threshold is determined based on the annealing temperature of the conductor material, and the thermal energy value corresponding to 90℃, where the strength of hard aluminum stranded wire begins to decrease significantly, is used as the benchmark. Finally, a dimensionless index in the range of 0-1 is generated. The closer the value is to 1, the more severe the line thermal stress state.

[0085] The present invention is further configured such that S3 includes:

[0086] Based on the dynamic fire field parameter set and the line thermal stress index, probability distributions are defined for the radiative heat flux, fire spread rate and line thermal stress index.

[0087] The Monte Carlo method is used to extract a large number of random scenarios from probability distributions to simulate the uncertainties of the fire environment and the thermal state of the power line. Specifically, in the probability distribution definition stage, the probability distribution of radiative heat flux is modeled using a gamma distribution, and its distribution parameters are determined by maximum likelihood estimation based on the statistical characteristics of radiative flux in historical fire observation data; the probability distribution of fire spread velocity is described using a log-normal distribution, and its distribution parameters are calibrated using a stochastic differential equation model based on real-time vegetation type, terrain slope, and wind speed data; the probability distribution of the power line thermal stress index is constructed using a Bayesian parameter estimation method, with its prior distribution determined based on the thermodynamic properties of the power line materials, and its posterior distribution updated using real-time monitoring data. In the Monte Carlo scenario simulation stage, a pseudo-random number generation algorithm is used. Uniformly distributed random numbers are generated using the Mason swirl algorithm, and then random samples are extracted from the above probability distributions using an inverse transformation sampling method. Each random scenario contains a complete set of state parameters, including a randomized realization of radiative heat flux, fire spread velocity, and power line thermal stress index. The system generates 10,000 random scenarios by default to ensure statistical significance. All scenario parameters are tested for convergence using the Markov chain Monte Carlo method to ensure that the sample space fully covers all possible fire development trends and thermal state evolution paths of the lines, providing a complete probabilistic basis for subsequent safety margin calculations.

[0088] The present invention is further configured such that S3 further includes:

[0089] For each random scenario, calculate each safety margin in parallel:

[0090] The thermal stress safety margin is obtained by calculating the difference between 1 and the thermal stress index of the line under the current scenario;

[0091] The electrical insulation safety margin is obtained by calculating the ratio of the preset electrical insulation withstand strength to the line operating voltage.

[0092] Calculate mechanical load based on wind speed and conductor status from registered multi-source real-time monitoring data;

[0093] The mechanical strength safety margin is obtained by calculating the ratio of the preset material yield strength to the mechanical load.

[0094] Statistical analysis is performed on the safety margins calculated under all random scenarios to generate probabilistic safety margin indices. Specifically, for each random scenario generated by Monte Carlo simulation, the following three safety margin calculation processes are executed synchronously using multi-threaded parallel computing technology: Thermal stress safety margin calculation process: First, the line thermal stress index value in the current random scenario is read, and then a subtraction operation is performed to subtract the value 1 from the index value. The difference obtained is the thermal stress safety margin under this scenario. When the thermal stress index of a line reaches 1, the thermal stress safety margin drops to 0, indicating that the line is in a critical state of thermal failure. The calculation process for the electrical insulation safety margin is as follows: First, the standard value of the electrical insulation withstand strength of the line is retrieved from the power grid equipment parameter database. Then, the real-time line operating voltage data provided by the dispatch system is read. Finally, the standard value of withstand strength is divided by the measured value of operating voltage, and the resulting ratio is the electrical insulation safety margin in this scenario. The calculation of the mechanical strength safety margin consists of two steps: First, based on the wind speed data and conductor temperature status of the current scenario, the comprehensive mechanical load of the conductor is calculated according to the overhead line load specification. The wind speed data comes from the monitoring network, and the conductor temperature status is derived from the thermal stress index. Then, the nominal yield strength of the conductor material is obtained from the material database, and the nominal yield strength is divided by the calculated mechanical load. The resulting ratio is the mechanical strength safety margin in this scenario. After completing parallel computation of all random scenarios, the system performs statistical analysis on the three safety margin datasets. For each safety margin index, the average of the results of all scenarios is calculated as the expected safety margin, the fifth percentile value is determined as the conservative safety margin, and the proportion of scenarios with a value less than 1 is statistically analyzed as the failure probability. Finally, a probabilistic safety margin index containing these three statistics is output. The probabilistic safety margin index is a probabilistic safety assessment quantity based on a large number of random scenario statistics, used to quantify the possibility of transmission lines maintaining a safe state under wildfire conditions.

[0095] The present invention is further configured such that S4 includes:

[0096] The risk level is determined by mapping the probabilistic safety margin index and the line thermal stress index through a preset risk threshold.

[0097] On the electronic map, the risk level is associated with the spatial location of the path assessment point set and visualized to form a risk corridor map;

[0098] Early warning information and handling suggestions corresponding to the risk level are generated. The handling suggestions include: reducing load or shutting down the line. Specifically, a four-level classification standard is adopted in the risk level determination stage. When the quantile safety margin is greater than 0.8 and the line thermal stress index is less than 0.3, it is judged as a green safety level; when the quantile safety margin is between 0.5 and 0.8 or the line thermal stress index is between 0.3 and 0.5, it is judged as a yellow concern level; when the quantile safety margin is between 0.2 and 0.5 or the line thermal stress index is between 0.5 and 0.7, it is judged as an orange warning level; when the quantile safety margin is less than or equal to 0.2 or the line thermal stress index is greater than or equal to 0.7, it is judged as a red alert level. The visualization rendering phase is implemented based on the WebGIS platform. The system reads the spatial coordinate data of the path assessment point set and its corresponding risk level data, and renders it on the map interface using stripes of different colors: green represents safe sections, yellow represents sections of concern, orange represents warning sections, and red represents alert sections. Adjacent path points of the same risk level are connected into continuous risk corridors through geometric calculations, and the display is updated in real time. In the warning generation phase, corresponding contingency plans are automatically triggered based on the determined risk level: a yellow concern level generates an "enhanced monitoring" instruction, notifying inspection personnel to stand by; an orange warning level generates a "prepare for handling" instruction, sending a load reduction operation suggestion to the dispatch department; a red alert level generates an "emergency handling" instruction, sending a line shutdown suggestion to the dispatch system and simultaneously initiating the emergency response procedure. All warning information and handling suggestions are transmitted in real time to relevant responsible departments through the power dispatch data network, facilitating timely processing by relevant personnel.

[0099] Example 2:

[0100] Please see Figure 2 This exemplary system for assessing the safety margin of a power line under the impact of a wildfire includes:

[0101] Data perception and acquisition module: Based on the power grid geographic information system, it acquires spatial information of transmission lines, generates a path assessment point set, and at the same time uses the integrated air-space-ground monitoring network to acquire multi-source real-time monitoring data of the area where the path assessment point set is located;

[0102] Parameter reconstruction and fusion module: performs synchronization and spatial registration processing on multi-source real-time monitoring data, reconstructs a dynamic fire field parameter set, and calculates the line thermal stress index;

[0103] Safety margin assessment module: Based on dynamic fire scene parameter set and line thermal stress index, Monte Carlo scenario simulation is carried out using probabilistic risk assessment method to generate probabilistic safety margin index;

[0104] Visualization output module: Based on the probabilistic safety margin index and the line thermal stress index, the risk level is determined and the visualized risk assessment results are output.

[0105] It should be noted that the system for assessing the safety margin of power lines under the influence of wildfires provided in the above embodiments and the method for assessing the safety margin of power lines under the influence of wildfires provided in the above embodiments belong to the same concept. The specific methods of operation of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the system for assessing the safety margin of power lines under the influence of wildfires provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the system can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assessing the safety margin of power lines under the influence of wildfires, characterized in that, include: S1: Based on the power grid geographic information system, spatial information of transmission lines is obtained to generate a path evaluation point set. At the same time, multi-source real-time monitoring data of the area where the path evaluation point set is located is obtained using the integrated air-space-ground monitoring network. S2: Synchronize and spatially register multi-source real-time monitoring data to reconstruct a dynamic fire field parameter set and calculate the line thermal stress index; S3: Based on the dynamic fire scene parameter set and the line thermal stress index, a probabilistic risk assessment method is used to simulate the Monte Carlo scenario and generate a probabilistic safety margin index. S4: Determine the risk level based on the probabilistic safety margin index and the line thermal stress index, and output the visualized risk assessment results.

2. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 1, characterized in that, S1 includes: Data on transmission line towers is obtained from a geographic information system or a power grid resource map. The tower data includes coordinate data and elevation data. Based on tower data, continuous line paths are generated through curve interpolation; Sampling is performed along the route at preset basic intervals to form basic path evaluation points; By combining historical wildfire data, digital elevation models, and vegetation cover data, high-risk areas are identified. In high-risk sections, the basic path assessment points are sampled more densely, and the basic path assessment points are combined to form a path assessment point set.

3. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 2, characterized in that, The integrated air-space-ground monitoring network includes: a fixed sensor network and a mobile monitoring cluster of unmanned aerial vehicles (UAVs); The fixed sensor network includes: meteorological sensors, heat flux sensors, and dual-spectrum cameras deployed at path assessment points; The mobile monitoring cluster of drones includes drones equipped with infrared thermal imagers, multispectral imagers, lidar, and mini weather stations.

4. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 1, characterized in that, The dynamic fire field parameter set of S2 includes: flame height, radiant heat flux, and fire spread rate.

5. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 4, characterized in that, The synchronization and spatial registration process of S2 includes: Time synchronization and spatial registration of multi-source real-time monitoring data; Based on the registered multi-source real-time monitoring data, the flame height is calculated using a stereo vision algorithm; Based on the registered multi-source real-time monitoring data, the radiative heat flux reaching the line location is calculated using a radiative transfer model. Based on the registered multi-source real-time monitoring data, the fire spread rate is calculated using the optical flow method.

6. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 5, characterized in that, The calculation of the circuit thermal stress index in S2 includes: Based on the radiative heat flux in the dynamic fire field parameter set and the ambient temperature and wind speed data in the registered multi-source real-time monitoring data, the instantaneous heat load intensity of the line is calculated. Based on the time series of instantaneous heat load intensity, combined with the pre-set specific heat capacity and mass per unit length of the line material, the cumulative heat exposure of the line is calculated through a time integral model with a decay function. The cumulative heat exposure is normalized to a preset critical heat load threshold to generate a line thermal stress index.

7. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 1, characterized in that, S3 includes: Based on the dynamic fire field parameter set and the line thermal stress index, probability distributions are defined for the radiative heat flux, fire spread rate and line thermal stress index. The Monte Carlo method is used to extract a large number of random scenarios from the probability distribution to simulate the uncertainty of the fire environment and the thermal state of the power lines.

8. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 7, characterized in that, S3 further includes: For each random scenario, calculate each safety margin in parallel: The thermal stress safety margin is obtained by calculating the difference between 1 and the thermal stress index of the line under the current scenario; The electrical insulation safety margin is obtained by calculating the ratio of the preset electrical insulation withstand strength to the line operating voltage. Calculate mechanical load based on wind speed and conductor status from registered multi-source real-time monitoring data; The mechanical strength safety margin is obtained by calculating the ratio of the preset material yield strength to the mechanical load. Statistical analysis is performed on the safety margins calculated under all random scenarios to generate probabilistic safety margin indices.

9. The method for assessing the safety margin of power lines under the influence of wildfires according to claim 1, characterized in that, S4 includes: The risk level is determined by mapping the probabilistic safety margin index and the line thermal stress index through a preset risk threshold. On the electronic map, the risk level is associated with the spatial location of the path assessment point set and visualized to form a risk corridor map; Generate early warning information and handling suggestions corresponding to the risk level. The handling suggestions include: reducing load or shutting down the line.

10. A system for assessing the safety margin of power lines under the influence of wildfires, used to implement the method for assessing the safety margin of power lines under the influence of wildfires as described in any one of claims 1-9, characterized in that, include: Data perception and acquisition module: Based on the power grid geographic information system, it acquires spatial information of transmission lines, generates a path assessment point set, and at the same time uses the integrated air-space-ground monitoring network to acquire multi-source real-time monitoring data of the area where the path assessment point set is located; Parameter reconstruction and fusion module: performs synchronization and spatial registration processing on multi-source real-time monitoring data, reconstructs a dynamic fire field parameter set, and calculates the line thermal stress index; Safety margin assessment module: Based on dynamic fire scene parameter set and line thermal stress index, Monte Carlo scenario simulation is carried out using probabilistic risk assessment method to generate probabilistic safety margin index; Visualization output module: Based on the probabilistic safety margin index and the line thermal stress index, the risk level is determined and the visualized risk assessment results are output.

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