Power transmission line forest fire risk assessment method and system driven by flame combustion model

Abstract

The invention provides a power transmission line forest fire risk assessment method and system driven by a flame combustion model, relates to the technical field of electrical engineering, establishes a multi-source data fusion fire point monitoring system, and comprehensively utilizes three monitoring modes of satellite remote sensing, a ground monitoring station and unmanned aerial vehicle inspection to collect fire point data information. According to the method, multi-source cooperative complementary monitoring is realized, the satellite remote sensing, the ground monitoring station and the unmanned aerial vehicle are comprehensively used for routing inspection, the limitation of a traditional monitoring means is overcome, the satellite remote sensing coverage is wide, and large-area fire points can be found in time; a ground monitoring station carries out precise monitoring in a close-range and complex terrain area; the unmanned aerial vehicle is flexible in routing inspection and high in resolution, low-altitude detailed reconnaissance can be realized, and the three parts cooperate to comprehensively improve the monitoring accuracy and reduce missing report and false report.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a method and system for assessing forest fire risk of power transmission lines driven by a flame combustion model. Background Art

[0002] With global climate change and the impact of human activities on the natural environment, the frequency and scale of wildfires have shown an upward trend in recent years. Many transmission lines inevitably pass through densely vegetated and flammable areas such as forests and mountains, which makes them extremely vulnerable to wildfires. When a wildfire occurs, the high temperature, thick smoke, and flames it brings will cause direct damage to the transmission lines. High temperatures may cause the conductor material of the transmission line to soften or even melt, affecting the normal transmission of electric energy; particulate matter and chemicals in the thick smoke may adhere to the surface of the insulator, reducing its insulation performance and increasing the risk of flashover in the line; flames may directly burn the transmission line towers, conductors and other facilities, causing line interruption.

[0003] According to the Chinese publication number "CN104915775A", a method for risk assessment and emergency decision-making based on wildfire disasters on transmission lines is disclosed. This method combines the wildfire tripping mechanism of the transmission line to calculate the probability of the transmission line tripping caused by the on-site wildfire, and calculates the load loss after the line trips, and evaluates the tripping risk value based on the tripping probability and load loss. Then, according to the voltage level of the transmission line, the comprehensive load loss of the wildfire tripping, and the wildfire tripping risk value, the priority of the on-site wildfire handling of the transmission line is calculated, and the optimal decision-making plan for the configuration of available resources is scientifically given. The use of this method should be able to achieve the effect of quickly and accurately handling wildfires with the best solution, and it should also be novel in thinking and clear in process, which can provide a technical means for the on-site handling of wildfires on transmission lines that is both efficient and economical.

[0004] The above patent documents and prior art have the following technical problems when used:

[0005] Problem 1: In the above patent documents and prior art, traditional monitoring methods mainly rely on manual inspections and limited fixed monitoring points, which have problems such as small monitoring range, poor timeliness, and high labor costs. For example, manual inspections are difficult to achieve real-time monitoring in large areas, and early signs of wildfires are easily missed. Fixed monitoring points have limited coverage, and the monitoring capabilities for remote areas and areas with complex terrain are insufficient. At the same time, although some new technologies such as satellite remote sensing and drone inspections are gradually being applied, these technologies have their own limitations when used alone. Satellite remote sensing is affected by factors such as resolution and weather, and may result in misjudgment or omission. Drone inspections have limited endurance, and the real-time performance of data processing and transmission needs to be improved. At the same time, data fusion between different monitoring technologies is not efficient enough to form comprehensive and accurate fire point monitoring information.

[0006] Question 2: Most of the existing risk assessment methods in the above documents are based on simple statistical models or empirical judgments, lacking an in-depth understanding and comprehensive consideration of the wildfire combustion process and the physical characteristics of the transmission line. For example, when calculating the probability of a transmission line tripping due to a wildfire, only a few factors such as the distance between the fire point and the line are often considered, while the complex influence of multiple key factors such as the intensity of the wildfire, the direction of the fire spread, the type of vegetation, and meteorological conditions on the insulation performance of the transmission line and the breakdown of the air gap are ignored. For the calculation of the probability of air gap breakdown and the probability of relative breakdown of the transmission line under wildfire conditions, there is a lack of models based on precise physical principles and experimental data, resulting in low accuracy of the assessment results and failure to provide a reliable basis for making effective emergency decisions. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention provides a transmission line wildfire risk assessment method driven by a flame combustion model, which solves the problems of traditional monitoring methods having a small monitoring range, poor timeliness, high labor costs, and a lack of comprehensive consideration of the wildfire combustion process and the physical characteristics of the transmission line in risk assessment.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A method for assessing wildfire risk of power transmission lines driven by a flame combustion model comprises the following steps:

[0010] Use satellite remote sensing, ground monitoring stations and drone inspections to collect fire point data information, and integrate the fire point data obtained by satellite remote sensing, ground monitoring stations and drone inspections;

[0011] According to the fire point coordinates of the fused fire point data and the stored line tower coordinates, the straight-line distance between the fire point and the nearest tower and the azimuth information relative to the tower are calculated to generate wildfire warning information;

[0012] According to the wildfire warning information, video collection and measured data recording of wildfire scene information are carried out;

[0013] Based on the wildfire site information, the probability of transmission line tripping due to wildfire is calculated, the probability of air gap breakdown under wildfire conditions is calculated, the probability of relative ground breakdown of the transmission line is calculated, and then the risk value is obtained.

[0014] Furthermore, the fire point data collection further includes the following contents:

[0015] Acquire infrared satellite remote sensing data released by the National Meteorological Satellite Center in real time, capture thermal radiation information on the earth's surface, and process satellite images using image processing algorithms. First, perform radiation correction to eliminate the noise and error of the sensor itself using the following formula:

[0016] L corrected =L observed -L noise

[0017] Where L observed is the radiation value observed by the satellite, L noise is the noise radiation value determined by the sensor calibration parameters, L corrected It is the corrected radiation value, and then geometric correction is performed. Using geographic coordinate reference data and satellite orbit parameters, the pixel coordinates of the satellite image are converted into geographic coordinates through a mathematical model to ensure that each point in the image accurately corresponds to the actual location on the earth;

[0018] Real-time monitoring of the temperature distribution of the surrounding environment along the transmission line. Based on the relationship between the infrared radiation energy and temperature of the object, the temperature distribution of the object is presented in the form of an image. When an abnormally high temperature area is detected, the data collection function is automatically triggered, and the temperature data and corresponding location information are transmitted to the data center. Real-time monitoring of the smoke concentration in the air is carried out, and the smoke concentration is determined by detecting the scattering or absorption of light by smoke particles. When the smoke concentration exceeds the set threshold, an alarm signal and related data are sent;

[0019] Inspect the areas around the transmission lines, take visible light images of the ground, capture the shape and characteristics of ground objects, obtain thermal infrared images of the ground, detect temperature abnormalities through thermal infrared images, and process the collected data. By comparing images and temperature data at different times, potential fire points are discovered and their locations and ranges are determined. An image difference algorithm is used to compare the current image with historical images to discover areas of pixel value changes, identify fire point areas, and then determine the geographical location of the fire point by combining the shooting location information and shooting angle.

[0020] Furthermore, after the satellite remote sensing, ground monitoring station and drone inspection data are collected, a data fusion and verification mechanism is established to fuse the fire point data obtained by satellite remote sensing, ground monitoring station and drone inspection;

[0021] Data fusion algorithm: The weighted average fusion algorithm is used. First, the weight is set according to the accuracy and reliability of the data source. The weight of satellite remote sensing data is set to W. satellite , the ground monitoring station data weight is set to W ground , the weight of drone inspection data is set to W drone , and W satellite +W ground +W drone =1;

[0022] For the location information (x, y) of the fire point, the fused location coordinates (x fused ,yfused ), the calculation formula is:

[0023] x fused =W satellite x satellite +W ground x ground +W drone x drone

[0024] y fused =W satellite y satellite +W ground y ground +W drone y drone

[0025] Where (x satellite ,y satellite )、(x ground ,y ground )、(x drone ,y drone ), are the fire point location coordinates detected by satellite remote sensing, ground monitoring stations and drone inspections, respectively. For the fire area A, the fused area A fused The calculation formula is:

[0026] A fused =W satellite A satellite +W ground A ground +W drone A drone

[0027] Among them A satellite , A ground and A drone The fire areas were estimated by satellite remote sensing, ground monitoring stations and drone inspections.

[0028] Furthermore, after the data is fused, the data verification further includes the following contents:

[0029] The fused fire point data is verified using historical fire data and geographic information data. The currently detected fire point locations are compared with areas where fires have occurred historically. Fire points are detected in areas with a high historical fire incidence and are determined to be reliable fire point data. At the same time, the rationality of the fire points is judged in combination with geographic information data.

[0030] Furthermore, the transmission line generates wildfire warning information further including the following contents:

[0031] The wildfire warning information includes the pole tower number closest to the fire point, the straight-line distance from the pole tower, and the direction relative to the pole tower. The stored transmission line tower coordinate information is recorded in the database in the form of geographic coordinates. Each pole tower has a unique identification number. The geographic information system technology is used to match the fire point coordinates with the pole tower coordinates. First, the fire point coordinates and the pole tower coordinates are converted to the same geographic coordinate system, and then the distance between each fire point and all the pole towers is calculated through the spatial query algorithm; for each fire point, find the pole tower closest to it, and record the number of the pole tower, calculate the straight-line distance between the fire point and the nearest pole tower, and use the Euclidean distance formula to calculate, that is,

[0032]

[0033] Where (x fire -y fire ) is the coordinate of the fire point, (x tower ,y tower ) is the coordinate of the nearest tower, and d is the straight-line distance between them;

[0034] Determine the direction of the fire point relative to the tower by calculating the angle between the line connecting the fire point coordinates and the nearest tower coordinates and the true north direction. Use the inverse tangent function to calculate, that is,

[0035]

[0036] Where θ is the azimuth of the fire point relative to the tower, unit: degree;

[0037] When the distance between the fire point and the tower reaches the preset warning threshold, an alarm signal is issued.

[0038] Furthermore, in the acquisition of wildfire site information, the wildfire site information includes the topography of the wildfire site, the line height and line spacing of the transmission lines, the vegetation near the wildfire, the wind speed and direction, the temperature characteristics under the transmission lines, and the smoke concentration at the site. First, the topography is measured, and the terrain of the wildfire site is measured in detail. Control points are selected on site, and a local terrain reference framework is established by measuring the three-dimensional coordinates of the longitude, latitude and elevation of the control points. Then, point-by-point measurements are performed along the preset measurement route to obtain the elevation data of the ground points and record the slope and slope direction information of the terrain.

[0039] Furthermore, the acquisition of on-site information includes transmission line parameter measurement and vegetation information investigation, measuring the height of the line tower to the ground and the horizontal and vertical distances between adjacent towers to determine the line spacing, and recording the model and material information of the transmission line. At the same time, using a vegetation identification manual and with the help of a mobile application, the type of vegetation is identified, including tree species, shrub species and herbaceous plants, and the coverage and density of vegetation are estimated. In addition, the growth status and health status of the vegetation and the presence of flammable vegetation are observed.

[0040] Furthermore, the acquisition of on-site information includes meteorological condition monitoring and smoke concentration detection, real-time monitoring of wind speed and wind direction at the site, measurement of temperature characteristics under the transmission line, recording of temperature data at different times and locations at the bottom of the transmission line tower, and detection of smoke concentration at the site.

[0041] Furthermore, the risk calculation further includes the following contents:

[0042] Calculation of the probability of power transmission line tripping due to wildfire:

[0043] First, the voltage level, tower structure, and insulation configuration information of the transmission line are obtained. At the same time, the vegetation type, fire size, flame temperature, wildfire spread speed, wildfire and transmission line location information are collected, and the wind speed, wind direction, and air humidity meteorological data are obtained. Then, a tripping probability model is established. Based on the collected data, the data of transmission line tripping caused by historical wildfires are used for statistical analysis. The impact of thermal radiation and smoke particles generated by wildfires on the insulation performance of transmission lines is analyzed, and a theoretical calculation model is established. When the thermal radiation intensity exceeds the threshold, the line is at risk of tripping. The tripping probability P is estimated by the following formula t1 :

[0044]

[0045] Where I is the thermal radiation intensity generated by the wildfire, I th is the thermal radiation intensity threshold that causes the line to trip, σ is the standard deviation of the thermal radiation intensity distribution obtained based on historical data statistics, and then the collected wildfire and meteorological data are substituted into the tripping probability model for calculation;

[0046] Calculation of the probability of air gap breakdown under wildfire conditions:

[0047] Firstly, a breakdown probability model is established based on gas discharge theory and experimental data. According to Paschen's law, the original breakdown voltage of the air gap is assumed to be U b0 , the air density under wildfire conditions becomes ρ, the temperature becomes T, and the corrected breakdown voltage U b Calculated by the following formula:

[0048]

[0049] Where ρ0 and T0 are the air density and temperature under standard conditions, respectively. Then, according to the actual operating voltage U of the transmission line, the breakdown probability P is calculated. b :

[0050]

[0051] Where δ is the standard deviation of breakdown voltage distribution obtained from experimental data statistics;

[0052] When calculating, first measure or estimate the air temperature and density changes in the wildfire area, substitute them into the above breakdown voltage correction formula, and calculate the breakdown voltage U of the air gap under wildfire conditions. b ;

[0053] Calculation of the probability of phase-to-ground breakdown of transmission lines:

[0054] The air gap breakdown probability model and ground resistance change model under wildfire conditions are combined to calculate the relative ground breakdown probability. Assuming the ground resistance is R g , the ground resistance before the wildfire is R g0 The ground resistance change rate is According to circuit theory, the line voltage relative to ground is U r , which can be expressed as

[0055] Where U is the phase voltage of the transmission line. Then, combined with the air gap breakdown probability model, the relative-to-ground breakdown probability model is calculated to calculate the relative-to-ground breakdown probability P. r , when the relative voltage U of the air gap is calculated r The breakdown probability under b,r When considering the comprehensive influence of ground resistance change on line insulation, the relative ground breakdown probability P r Calculated by the following formula:

[0056] P r =P b,r (1+Kα)

[0057] Where K is a coefficient determined based on experience or experiment, reflecting the influence of the change in ground resistance on the relative ground breakdown probability.

[0058] Furthermore, it also includes:

[0059] Determine the voltage level coefficient of the transmission line, find and determine the voltage level coefficient of the current transmission line according to the pre-established voltage level and coefficient correspondence table, calculate the load loss of the transmission line wildfire tripping, calculate the transmission line wildfire tripping risk value, the transmission line wildfire tripping risk value is the sum of the transmission line wildfire tripping risk values ​​generated by all fire points in the area, and calculate the transmission line wildfire disposal priority;

[0060] When large-scale wildfires occur at the same time and existing emergency decision-making resources are limited, all wildfires are sorted according to the calculated handling priorities of each wildfire site, and resources are allocated in descending order of priority to fight wildfires and protect power transmission lines in combination with existing emergency decision-making resources.

[0061] Furthermore, the emergency decision-making and resource allocation further include the following contents:

[0062] First, according to the determined priority factors, a calculation model for the priority of wildfire disposal of transmission lines is established. A weighted summation method is used to assign a weight coefficient to each factor. Then, the value of each factor is multiplied by the corresponding weight coefficient and added together to obtain the final priority value. Then, a priority calculation model is established. The specific method is as follows:

[0063] Assume that the weight of the risk value of wildfire tripping is W1, the weight of the fire point scale is W2, the weight of the distance between the fire point and important facilities is W3, the weight of the wildfire spread trend is W4, and the weight of the difficulty of firefighting is W5. For a single fire point, the wildfire tripping risk value is R, the fire point scale value is S, the distance between the fire point and important facilities is D, the wildfire spread trend value is M, and the difficulty of firefighting is E. The disposal priority value of the fire point is:

[0064]

[0065] Then, based on the calculated priority value, all wildfires in the area are sorted to determine the order of their treatment. When allocating resources, they are classified and marked according to their nature and purpose. Resources are divided into fire-fighting resources, protection resources, emergency repair resources, and communication resources. The four types of resources are numbered and marked, and resources are allocated in descending order according to the priority of the wildfire scene.

[0066] A transmission line wildfire risk assessment system driven by a flame combustion model, comprising:

[0067] The fire point data collection module uses satellite remote sensing, ground monitoring stations and drone inspections to collect fire point data information, and integrates the fire point data obtained by satellite remote sensing, ground monitoring stations and drone inspections;

[0068] The transmission line wildfire alarm information acquisition module calculates the straight-line distance between the fire point and the nearest tower and the azimuth information relative to the tower based on the fire point coordinates of the fused fire point data and the stored line tower coordinates, and generates wildfire alarm information;

[0069] The on-site information acquisition module collects video and records measured data of the wildfire scene information based on the wildfire alarm information;

[0070] The risk calculation module calculates the probability of transmission line tripping due to wildfire, the probability of air gap breakdown under wildfire conditions, the probability of relative ground breakdown of transmission lines, and then derives the risk value based on the wildfire site information.

[0071] Furthermore, the fire point data collection module includes:

[0072] The satellite remote sensing data collection module obtains the infrared satellite remote sensing data released by the National Meteorological Satellite Center in real time through satellite data receiving equipment, captures the thermal radiation information of the earth's surface through satellite sensors, and processes satellite images using the image processing module;

[0073] The data collection module of the ground monitoring station includes an infrared thermal imager and a smoke sensor. The infrared thermal imager monitors the temperature distribution of the surrounding environment in real time. The infrared thermal imager presents the temperature distribution of the object in the form of an image based on the relationship between the infrared radiation energy and temperature of the object. When an abnormally high temperature area is detected, the data collection function is automatically triggered, and the temperature data and corresponding location information are transmitted to the data center. The smoke sensor monitors the smoke concentration in the air in real time and determines the smoke concentration by detecting the scattering or absorption of light by smoke particles. When the smoke concentration exceeds the set threshold, an alarm signal and related data are sent;

[0074] The drone inspection data collection module uses a drone equipped with a camera and an infrared thermal imager. The camera is used to capture visible light images of the ground and capture the shape and characteristics of ground objects. The infrared thermal imager obtains thermal infrared images of the ground and detects temperature abnormalities through thermal infrared images. The ground control station processes the data collected by the drone and discovers potential fire points by comparing images and temperature data at different times, and determines their locations and ranges. An image difference algorithm is used to compare the current image with the historical image to discover areas where pixel values ​​change, identify the fire point area, and then determine the geographical location of the fire point by combining the drone's location information and shooting angle.

[0075] Furthermore, the module for obtaining transmission line wildfire alarm information is used to convert the fire point coordinates and the tower coordinates into the same geographic coordinate system, and then calculate the distance between each fire point and all the towers through a spatial query algorithm; for each fire point, find the tower closest to it, record the tower number, and calculate the straight-line distance between the fire point and the nearest tower; determine the direction of the fire point relative to the tower, and determine the direction by calculating the angle between the line between the fire point coordinates and the nearest tower coordinates and the north direction; when the distance between the fire point and the tower reaches a preset warning threshold, an alarm signal is issued.

[0076] Furthermore, the on-site information acquisition module includes a topographic surveying instrument, a laser rangefinder, a temperature sensor, an anemometer and a smoke concentration detector. The topographic surveying instrument is used for topographic measurement; the laser rangefinder measures the height of the line tower to the ground and the horizontal and vertical distances between adjacent towers; the anemometer monitors the wind speed and direction at the site in real time, the temperature sensor measures the temperature characteristics under the transmission line, and the temperature sensor is installed at the bottom of the transmission line tower; the smoke concentration detector detects the smoke concentration at the site.

[0077] Furthermore, the risk calculation module includes:

[0078] The transmission line tripping probability calculation module due to wildfire calculates the transmission line tripping probability due to wildfire based on the fire point data information integrated by the fire point data collection module, the wildfire site information collected by the site information acquisition module, the measured data records and the historical statistical data;

[0079] The air gap breakdown probability calculation module under wildfire conditions is used to establish a breakdown probability model, and calculate the air gap breakdown probability under wildfire conditions by measuring or estimating the air temperature and density changes in the wildfire area;

[0080] The transmission line relative-to-ground breakdown probability calculation module calculates the transmission line relative-to-ground breakdown probability through the air gap breakdown probability model under wildfire conditions and the grounding resistance change model.

[0081] Furthermore, it also includes emergency decision-making module and resource allocation module.

[0082] The emergency decision-making module is used to determine the voltage level coefficient of the transmission line. According to the pre-established voltage level and coefficient correspondence table, the voltage level coefficient of the current transmission line is searched and determined, the load loss of the transmission line wildfire tripping is calculated, and the risk value of the transmission line wildfire tripping is calculated. The risk value of the transmission line wildfire tripping is the sum of the risk values ​​of the transmission line wildfire tripping generated by all fire points in the area, and the priority of the transmission line wildfire disposal is calculated;

[0083] The resource allocation module is used to sort all wildfires according to the calculated handling priorities of each wildfire site when large-scale wildfires occur simultaneously and the existing emergency decision-making resources are limited. In order of priority from high to low, resources are allocated to wildfire fighting and power transmission line protection in combination with existing emergency decision-making resources.

[0084] A computer device comprising:

[0085] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned flame combustion model-driven transmission line wildfire risk assessment method.

[0086] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned flame combustion model-driven transmission line wildfire risk assessment method.

[0087] The present invention provides a method for assessing the risk of wildfires in power transmission lines driven by a flame combustion model. It has the following beneficial effects:

[0088] 1. The present invention realizes multi-source collaborative and complementary monitoring, and comprehensively uses satellite remote sensing, ground monitoring stations and drone inspections to overcome the limitations of traditional monitoring methods. Satellite remote sensing has a wide coverage and can detect large-area fire points in a timely manner; ground monitoring stations can accurately monitor at close range and in complex terrain areas; drone inspections are flexible and have high resolution, and can conduct detailed reconnaissance at low altitudes. The three work together to comprehensively improve monitoring accuracy and reduce missed reports and false alarms. For example, in forest areas, satellite remote sensing scans the entire area, drone inspections focus on local areas, and ground monitoring stations monitor the surrounding areas in real time to form an all-round monitoring network and innovate data fusion and processing. Weighted averaging, Kalman filtering and other algorithms are used to fuse multi-source data, and weights are set according to accuracy and reliability to improve data quality. Establish a strict verification mechanism, combine historical fire and geographic information data verification to ensure that the data is authentic and reliable, such as integrating satellite, ground and drone data to accurately determine fire point information, provide an accurate basis for subsequent work, and avoid decision-making errors.

[0089] 2. The present invention improves the risk assessment model, comprehensively considers multiple factors, such as the distance between the fire point and the line, the intensity of burning, the direction of spread, the type of vegetation, meteorological conditions, etc., which affect the transmission line, and establishes a comprehensive and scientific risk assessment model. Based on the gas discharge theory and experimental data, a calculation model for the probability of air gap breakdown and relative ground breakdown is constructed to improve the accuracy of the assessment. For example, a variety of factors are comprehensively evaluated to assess the risk of a certain area, provide a scientific basis for the formulation of emergency measures, use quantitative analysis methods to determine the voltage level coefficient, calculate the amount of loss and risk value, establish a disposal priority model, and weight the weights of the factors to determine the priority. Classify and mark according to the nature and use of resources, and reasonably allocate resources according to priority to improve configuration efficiency and rationality. For example, when facing multiple wildfire sites, give priority to protecting resources in high-risk areas to ensure timely control of wildfires and repair of lines, ensure power supply and personnel safety, and improve the effectiveness and efficiency of emergency disposal.

[0090] 3. From the monitoring perspective, the present invention solves the problems of small traditional monitoring range, poor timeliness and data fusion through multi-source collaboration and efficient data processing, realizes comprehensive, real-time and accurate monitoring, and provides strong support for timely detection of wildfires and grasping their development trends. In terms of risk assessment, the model that comprehensively considers multiple factors makes the assessment results closer to the actual situation, provides a reliable basis for scientific decision-making, avoids blind response, and reduces the risk and loss of transmission line failures caused by wildfires. The scientific nature of emergency decision-making and resource allocation ensures that resources can be deployed quickly and effectively when wildfires occur, improves the efficiency of firefighting and emergency repairs, shortens power outages, and reduces the impact on social production and life. At the same time, it ensures the safety of staff and smooth communications, and overall improves the ability and level of responding to wildfires on transmission lines, ensuring the safe and stable operation of the power system and the normal development of the social economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 The figure is a schematic block diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0092] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment 1

[0094] like Figure 1 As shown, a method for assessing the risk of wildfires in power transmission lines driven by a flame combustion model comprises the following steps:

[0095] Fire point data collection: Establish a fire point monitoring system with multi-source data fusion, comprehensively utilize satellite remote sensing, ground monitoring stations and drone inspections to collect fire point data information, establish a data fusion and verification mechanism, and integrate the fire point data obtained from satellite remote sensing, ground monitoring stations and drone inspections;

[0096] Obtaining wildfire alarm information on power transmission lines: The collected fire point information is imported into the power transmission line wildfire monitoring and early warning system. The system uses geographic information system (GIS) technology to match and analyze the coordinates of the fire point with the coordinates of the line tower stored in the system, calculate the straight-line distance between the fire point and the nearest tower and the azimuth information relative to the tower, and generate detailed wildfire alarm information, including the number of the nearest tower from the fire point, the straight-line distance from the tower, and the azimuth relative to the tower. When the distance between the fire point and the tower reaches the preset warning threshold, the system automatically sends an alarm signal and notifies relevant personnel through SMS, system pop-up windows, etc.

[0097] On-site information acquisition: Based on the alarm information, the transmission line operation and maintenance unit quickly dispatched line operation and maintenance specialists with professional monitoring equipment to the wildfire site. After arriving at the site, the operation and maintenance specialists comprehensively measured and recorded the topography, line height and line spacing of the transmission lines, vegetation near the wildfire, wind speed and direction, temperature characteristics under the transmission lines, and smoke concentration at the site. The videos and measured data collected on site were uploaded to the database of the transmission line wildfire warning center in real time through the mobile network.

[0098] Risk calculation: Calculate the probability of transmission line tripping due to wildfire, calculate the probability of air gap breakdown under wildfire conditions, calculate the probability of relative ground breakdown of transmission lines, and then derive the risk value;

[0099] Emergency decision-making: determine the voltage level coefficient of the transmission line. According to the pre-established voltage level and coefficient correspondence table, find and determine the voltage level coefficient of the current transmission line, calculate the load loss of the transmission line wildfire tripping, calculate the transmission line wildfire tripping risk value, the transmission line wildfire tripping risk value is the sum of the transmission line wildfire tripping risk values ​​generated by all fire points in the area, and calculate the transmission line wildfire handling priority;

[0100] Resource allocation: When large-scale wildfires occur at the same time and the existing emergency decision-making resources are limited, all wildfires are sorted according to the calculated handling priorities of each wildfire scene. In order of priority from high to low, combined with the existing emergency decision-making resources, resources are reasonably allocated to carry out wildfire fighting and power transmission line protection.

[0101] By combining satellite remote sensing, ground monitoring stations and drone inspections, we have achieved all-round, multi-level monitoring of wildfires around power transmission lines. Different monitoring methods are complementary in terms of spatial resolution, temporal resolution and monitoring range, which can effectively improve the accuracy and timeliness of fire point monitoring and reduce the occurrence of missed reports and false reports.

[0102] The new data fusion technology uses a data fusion algorithm based on weighted average and Kalman filtering, which can fully integrate the advantages of data obtained by different monitoring methods and improve the quality and availability of data. This data fusion technology is innovative in the field of transmission line wildfire monitoring and provides a more accurate data basis for subsequent wildfire warnings, risk calculations and emergency decision-making.

[0103] A data verification mechanism combining on-site verification, cross-comparison and third-party data verification has been established to ensure the authenticity and reliability of fire point data. This strict data quality control measure is leading in the industry and can effectively improve the credibility and practicality of the wildfire monitoring system. Specific embodiment 2

[0105] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0106] Satellite remote sensing data collection: Using infrared satellite remote sensing technology, the infrared satellite remote sensing data released by the National Meteorological Satellite Center is obtained in real time through satellite data receiving equipment. The thermal radiation information of the earth's surface is captured by satellite sensors. The satellite images are processed using image processing algorithms. First, radiation correction is performed to eliminate the noise and error of the sensor itself through the following formula:

[0107] L corrected =L observed -L noise

[0108] Where L observed is the radiation value observed by the satellite, L noise is the noise radiation value determined by the sensor calibration parameters, L corrected It is the corrected radiation value, and then geometric correction is performed. Using geographic coordinate reference data and satellite orbit parameters, the pixel coordinates of the satellite image are converted into geographic coordinates through mathematical models such as collinearity equations to ensure that each point in the image can accurately correspond to the actual location on the earth.

[0109] Data collection at ground monitoring stations: Ground monitoring stations are set up at key locations along the transmission lines. Each monitoring station is equipped with an infrared thermal imager and a smoke sensor. The infrared thermal imager monitors the temperature distribution of the surrounding environment in real time. The infrared thermal imager presents the temperature distribution of the object in the form of an image based on the relationship between the infrared radiation energy and temperature of the object. When an area with abnormally high temperature is detected, the data collection function is automatically triggered, and the temperature data and corresponding location information are transmitted to the data center. The smoke sensor monitors the smoke concentration in the air in real time and determines the smoke concentration by detecting the scattering or absorption of light by smoke particles. When the smoke concentration exceeds the set threshold, an alarm signal and related data are immediately sent to provide supplementary information for the early detection of fire points.

[0110] UAV inspection data collection: UAVs are regularly dispatched to inspect areas around power transmission lines. The UAVs are equipped with high-definition cameras and infrared thermal imagers and fly according to preset routes. The high-definition cameras are used to capture visible light images of the ground to clearly capture the shape and characteristics of objects on the ground. The infrared thermal imager is used to obtain thermal infrared images of the ground, and the thermal infrared images are used to detect abnormal temperature areas. During the flight, the image data is transmitted back to the ground control station in real time through wireless transmission technology. The staff of the ground control station uses image analysis software to process the data collected by the UAV. By comparing images and temperature data at different times, potential fire points are discovered in a timely manner, and the location and range are accurately determined. The image difference algorithm is used to compare the current image with the historical image, and areas with large changes in pixel values ​​are found, which are identified as fire areas. The geographical location of the fire point is determined by combining the location information and shooting angle of the UAV.

[0111] Satellite remote sensing data collection, satellite data receiving equipment: using advanced dish-shaped satellite antennas, with high-precision pointing and tracking capabilities, can accurately align with the satellite signal source of the National Meteorological Satellite Center. The antenna is equipped with a low-noise amplifier and signal demodulation module, which can effectively improve the sensitivity and quality of signal reception. The data receiving equipment has a real-time data caching function. When there is a brief interruption or fluctuation in the satellite signal transmission, the incompletely received data will be temporarily stored in the local cache, and the signal will continue to be received after it becomes stable to ensure the integrity of the data.

[0112] Image processing algorithm, radiation correction: When performing radiation correction, in addition to using the above formulas, a scene-based radiation correction method is also introduced. By analyzing typical areas of different types of land objects (water bodies, vegetation, urban buildings) in satellite images, radiation characteristic models of different land objects are established. Based on these models, the entire image is partitioned and corrected to further improve the accuracy of radiation correction. For water areas, since their radiation characteristics are relatively stable and have specific spectral characteristics, the radiation model of water bodies can more accurately eliminate the noise and errors of sensors in this area.

[0113] The sensor calibration parameters are updated and optimized regularly. By comparing and analyzing the actual measurement data of the ground radiation calibration field, the sensor calibration parameters are adjusted in time to adapt to slight changes and long-term drift in sensor performance, ensuring the accuracy and stability of radiation correction.

[0114] Geometric correction: When using the collinear equation mathematical model for geometric correction, high-precision digital elevation model (DEM) data is introduced. Combined with the satellite's orbital parameters and DEM data, the impact of the terrain undulations on the geometric deformation of satellite images can be more accurately considered. For areas with complex terrain such as mountainous areas, DEM data can more accurately calculate the image point displacement caused by terrain height differences, thereby achieving more accurate geometric correction.

[0115] Multi-source geographic coordinate reference data is used for joint correction. In addition to using traditional map coordinate data, the global positioning system (GPS) measured control point data and high-precision satellite positioning data (such as data provided by the Beidou satellite navigation system) are combined to optimize and verify the geometric correction model. Through the mutual verification and supplement of multi-source data, the accuracy and reliability of geometric correction are improved to ensure that each point in the satellite image can accurately correspond to the actual position on the earth, and the error is controlled within the specified accuracy range.

[0116] Data collection at ground monitoring stations, infrared thermal imager: The infrared thermal imager uses a highly sensitive infrared detector that can sense tiny temperature changes and can achieve fine temperature monitoring of large areas. The thermal imager is equipped with automatic focus and intelligent image enhancement functions. When an area with abnormally high temperature is detected, it can automatically and quickly focus on the area and highlight the temperature abnormality points through image enhancement algorithms, allowing staff to observe abnormal conditions more clearly. In order to improve the accuracy of temperature measurement, the infrared thermal imager has multiple built-in temperature calibration points and performs automatic calibration regularly. At the same time, the equipment has real-time temperature compensation function, which can automatically adjust the measurement parameters according to changes in ambient temperature and reduce the impact of ambient temperature on the measurement results. In terms of data transmission, a high-speed Ethernet interface is used to ensure that temperature data and location information can be transmitted to the data center quickly and stably.

[0117] Infrared thermal imagers and smoke sensors have undergone a number of innovative optimizations in performance and functionality. High-resolution infrared detectors, automatic focusing and image enhancement functions, and real-time temperature compensation technologies have improved the accuracy and efficiency of temperature monitoring; the light scattering principle of smoke sensors combined with laser technology, automatic sensitivity adjustment, and multi-transmission mode support have enhanced the accuracy and reliability of smoke detection. These innovations enable ground monitoring stations to more effectively capture early signs of fire points, providing a strong guarantee for timely warnings.

[0118] UAV inspection data collection, the UAV adopts advanced electric multi-rotor design, and at the ground control station, the image analysis software uses an image recognition algorithm based on deep learning to process and analyze the image data collected by the UAV. Through a large number of fire point image sample training, an efficient fire point recognition model was established. This model can not only accurately identify the temperature abnormal area in the thermal infrared image, but also make a comprehensive judgment based on the multimodal information such as smoke and flame characteristics in the visible light image, so as to improve the accuracy of fire point recognition. When using the image difference algorithm, the threshold setting and regional growth strategy of the algorithm are further optimized. By analyzing and counting a large amount of image data at different times and under different environmental conditions, the threshold of the difference algorithm is adaptively adjusted to avoid misjudgment caused by factors such as ambient temperature changes and lighting differences. At the same time, after discovering the area with large pixel value changes, the regional growth algorithm is used in combination with the texture and color features of the image to more accurately determine the scope and boundaries of the fire point. When determining the geographical location of the fire point, the real-time positioning information, flight altitude, shooting angle and high-precision map data of the UAV are combined, and the latitude and longitude coordinates of the fire point are accurately calculated through the spatial geometry calculation model.

[0119] The introduction of deep learning-based image recognition algorithms and optimized image difference algorithms has achieved accurate identification and positioning of fire points. This intelligent image analysis method is innovative in the processing of drone inspection data, which can greatly improve the efficiency and accuracy of fire point monitoring and provide advanced technical means for timely detection and treatment of wildfire hazards. At the same time, through multimodal information fusion and precise geographic location calculation, the overall performance and practical value of the drone inspection system have been further improved. Specific embodiment 3

[0121] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0122] After the satellite remote sensing, ground monitoring station and drone inspection data are collected, a data fusion and verification mechanism is established to fuse the fire point data obtained by satellite remote sensing, ground monitoring stations and drone inspections:

[0123] Data fusion algorithm: weighted average fusion algorithm is used. First, weights are set according to the accuracy and reliability of the data source. Satellite remote sensing data has a wide coverage but relatively low resolution, so the weight is set to W. satellite , the ground monitoring station data has high accuracy but limited coverage, and the weight is set to W ground , the UAV inspection data has high flexibility and real-time, and the weight is set to W drone , and W satellite +W ground +W drone =1.

[0124] For the location information (x, y) of the fire point, the fused location coordinates (x fused ,y fused ), the calculation formula is:

[0125] x fused =W satrllite x satellite +W ground x ground +W drone x drone

[0126] y fused =W satellite y satellite +W ground y ground +W drone y drone

[0127] Where (x satellite ,y satellite )、(x ground ,y ground )、(x drone ,y drone ), are the fire point location coordinates detected by satellite remote sensing, ground monitoring stations and drone inspections, respectively. For the fire area A, the fused area A fused The calculation formula is:

[0128] A fused =W satellite A satellite +W ground A ground +W drone A drone

[0129] Among them A satellite , A ground and A drone The fire areas were estimated by satellite remote sensing, ground monitoring stations and drone inspections. Specific embodiment 4

[0131] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0132] After data fusion, data verification further includes the following: using historical fire data and geographic information data to verify the fused fire point data, comparing the current detected fire point location with the area where fires have occurred in history, and if a fire point is detected in an area with a high incidence of historical fires, the fire point data is considered reliable. At the same time, the geographic information data, including terrain (obtained through digital elevation models) and vegetation types (obtained through land use data), is combined to judge the rationality of the fire point. Specific embodiment 5

[0134] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0135] The collected fire point information is imported into the power transmission line wildfire monitoring and early warning system. The system stores the transmission line tower coordinate information and records it in the database in the form of geographic coordinates. Each tower has a unique identification number. The system uses geographic information system (GIS) technology to match the fire point coordinates with the tower coordinates. First, the fire point coordinates and the tower coordinates are converted to the same geographic coordinate system, and then the distance between each fire point and all towers is calculated through a spatial query algorithm. For each fire point, find the nearest tower and record the tower number. Calculate the straight-line distance between the fire point and the nearest tower. The Euclidean distance formula can be used to calculate, that is,

[0136]

[0137] Where (x fire -y fire ) is the coordinate of the fire point, (x tower ,y tower ) are the coordinates of the nearest tower, and d is the straight-line distance between them.

[0138] Determine the direction of the fire point relative to the tower by calculating the angle between the line connecting the fire point coordinates and the nearest tower coordinates and the true north direction. The inverse tangent function can be used to calculate, that is,

[0139]

[0140] Where θ is the azimuth of the fire point relative to the tower (in degrees).

[0141] The system has comprehensive coordinate format recognition and high-precision coordinate conversion capabilities, introduces advanced coordinate conversion models and parameter libraries, takes into account a variety of influencing factors, and ensures the accuracy of coordinate conversion. This precise coordinate processing technology is innovative in the field of transmission line wildfire monitoring, provides a reliable foundation for the subsequent matching of fire point and tower coordinates, and can effectively improve the accuracy and reliability of the monitoring system. The query algorithm based on spatial index and distributed computing framework are adopted to optimize the efficiency of distance calculation between fire point and tower. The use of spatial index greatly reduces the amount of calculation, and distributed computing improves the speed and real-time performance of large-scale data processing. This innovative calculation method has significant advantages in processing a large number of fire point and transmission line data, which can meet the needs of rapid response and efficient processing of the system in practical applications, and improve the overall performance and practicality of the wildfire monitoring and early warning system. When using the Euclidean distance formula to calculate the straight-line distance between the fire point and the nearest tower, in order to improve the calculation accuracy, the system pre-processes the coordinate data. The decimal places of the coordinate values ​​of the fire point and the tower are unified and standardized to avoid calculation errors caused by inconsistent data accuracy. At the same time, considering that the surface of the earth is not a completely flat plane, when calculating the straight-line distance of long distances, the system introduces the ellipsoid distance calculation method in geodesy for correction. According to the parameters of the earth's ellipsoid and the longitude and latitude coordinates of the fire point and the tower, a more accurate ellipsoid distance is calculated using the geodetic theme solution algorithm. When the distance exceeds a certain threshold, it automatically switches to the ellipsoid distance calculation mode to ensure the accuracy of the straight-line distance calculation. When using the inverse tangent function to calculate the azimuth of the fire point relative to the tower, the system optimizes the calculation process considering the domain and range characteristics of the inverse tangent function, as well as the coordinates of different quadrants. First, according to the relative position of the fire point coordinates and the tower coordinates, determine which quadrant the fire point is located in. Then, according to the conditions of different quadrants, the calculation results of the inverse tangent function are appropriately adjusted and converted to obtain an accurate azimuth range (0° to 360°). For example, when the fire point is located in the second quadrant of the tower, add 180° to the angle value calculated by the inverse tangent function; when it is located in the third quadrant, add 180°; when it is located in the fourth quadrant, add 360°. This ensures that the calculation result of the azimuth is accurate and consistent with the actual geographical orientation relationship. In order to improve the calculation accuracy of the azimuth, the system uses a high-precision mathematical library and calculation method in the calculation process. Perform sensitivity analysis on small changes in coordinate data to ensure that the influence of subtle differences in coordinates on the orientation can be accurately captured when calculating the azimuth. When outputting the azimuth results, the system can provide different unit representations according to user needs. In the straight-line distance calculation, the shape of the earth's surface is considered and the ellipsoidal distance calculation method is introduced for correction. At the same time, by comparing with the actual measurement data and error analysis, a distance value containing the error range is provided.This comprehensive distance calculation and error control measure is innovative in the industry, improving the accuracy and reliability of distance calculation, providing more accurate data support for wildfire monitoring and emergency decision-making, optimizing the azimuth calculation process, taking into account quadrant conditions to ensure accurate azimuth range, and providing multiple unit representation and precision control options. This flexible azimuth calculation and output method meets the needs of different users for azimuth information in different scenarios, is innovative and practical, and helps to more intuitively and accurately understand the relative position relationship between the fire point and the tower. Specific embodiment 6

[0143] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0144] According to the alarm information, the transmission line operation and maintenance unit quickly dispatched line operation and maintenance specialists with professional monitoring equipment to the wildfire site. The required monitoring equipment includes topographic survey instruments, temperature sensors, wind speed and direction instruments, and smoke concentration detectors. First, the topographic and geomorphological measurements are carried out. The terrain of the wildfire site is measured in detail using a total station. Control points are selected on site. By measuring the three-dimensional coordinates (longitude, latitude, and elevation) of the control points, a local terrain reference framework is established. Then, point-by-point measurements are carried out along the preset measurement route to obtain the elevation data of the ground points, record the slope and slope information of the terrain, and the transmission lines are monitored. When measuring road parameters and investigating vegetation information, for the line height and line spacing information of the transmission line, the operation and maintenance specialist uses a laser rangefinder to quickly measure the height of the line tower to the ground and the horizontal and vertical distances between adjacent towers to determine the line spacing. At the same time, the model and material information of the transmission line are recorded. At the same time, the operation and maintenance specialist uses the vegetation identification manual and the mobile application to identify the type of vegetation, including tree species, shrub species and herbaceous plants, and estimates the coverage and density of vegetation. In addition, the growth status and health status of the vegetation and the presence of flammable vegetation are observed. Specific embodiment 7

[0146] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0147] When monitoring meteorological conditions and detecting smoke concentration, an anemometer is used to monitor the wind speed and direction at the site in real time. A temperature sensor is used to measure the temperature characteristics under the transmission line. The temperature sensor is installed at the bottom of the transmission line tower to record temperature data at different times and locations. A smoke concentration detector is used to detect the smoke concentration at the site. The risk calculation further includes the following:

[0148] Calculation of the probability of power transmission line tripping due to wildfire:

[0149] First, the voltage level, tower structure, and insulation configuration information of the transmission line are obtained through operation and maintenance personnel. At the same time, the vegetation type, fire size, flame temperature, wildfire spread speed, wildfire and transmission line location information are collected, and wind speed, wind direction, and air humidity meteorological data are obtained. Then, a tripping probability model is established. Based on the collected data, the data of transmission line tripping caused by historical wildfires are used for statistical analysis. Combined with the physical principles of heat transfer and electricity, the influence of thermal radiation and smoke particles generated by wildfires on the insulation performance of transmission lines is analyzed, and a theoretical calculation model is established. When the thermal radiation intensity exceeds the threshold, the line is at risk of tripping. The tripping probability P is estimated by the following formula t1 :

[0150]

[0151] Where I is the thermal radiation intensity generated by the wildfire, I th is the thermal radiation intensity threshold that causes the line to trip, σ is the standard deviation of the thermal radiation intensity distribution obtained based on historical data statistics, and then the collected wildfire and meteorological data are substituted into the tripping probability model for calculation.

[0152] Calculation of the probability of air gap breakdown under wildfire conditions:

[0153] Firstly, a breakdown probability model is established based on gas discharge theory and experimental data. According to Paschen's law, the original breakdown voltage of the air gap is assumed to be U b0 , the air density under wildfire conditions becomes ρ, the temperature becomes T, and the corrected breakdown voltage U b Calculated by the following formula:

[0154]

[0155] Where ρ0 and T0 are the air density and temperature under standard conditions, respectively. Then, according to the actual operating voltage U of the transmission line, the breakdown probability P is calculated. b :

[0156]

[0157] Where δ is the standard deviation of the breakdown voltage distribution obtained from experimental data.

[0158] First, by measuring or estimating the air temperature and density changes in the wildfire area, substitute them into the above breakdown voltage correction formula to calculate the breakdown voltage U of the air gap under wildfire conditions. b ;

[0159] Calculation of the probability of phase-to-ground breakdown of transmission lines:

[0160] The air gap breakdown probability model and ground resistance change model under wildfire conditions are combined to calculate the relative ground breakdown probability. Assuming the ground resistance is R g , the ground resistance before the wildfire is R g0 The ground resistance change rate is According to circuit theory, the line voltage relative to ground is U r , which can be expressed as

[0161] Where U is the phase voltage of the transmission line. Then, combined with the air gap breakdown probability model, the relative-to-ground breakdown probability model is calculated to calculate the relative-to-ground breakdown probability P. r , when the relative voltage U of the air gap is calculated r The breakdown probability under b,r When considering the comprehensive influence of ground resistance change on line insulation, the relative ground breakdown probability P r It can be calculated by the following formula:

[0162] P r =P b,r (1+Kα)

[0163] Where K is a coefficient determined based on experience or experiment, reflecting the influence of the change in ground resistance on the relative ground breakdown probability. Specific embodiment 8

[0165] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0166] First, according to the determined priority factors, a calculation model for the priority of wildfire disposal of transmission lines is established. A weighted summation method is used to assign a weight coefficient to each factor. Then, the value of each factor is multiplied by the corresponding weight coefficient and added together to obtain the final priority value. Then, a priority calculation model is established. The specific method is as follows:

[0167] Assume that the weight of the risk value of wildfire tripping is W1, the weight of the fire point scale is W2, the weight of the distance between the fire point and important facilities is W3, the weight of the wildfire spread trend is W4, and the weight of the difficulty of firefighting is W5. For a single fire point, the wildfire tripping risk value is R, the fire point scale value is S, the distance between the fire point and important facilities is D, the wildfire spread trend value is M, and the difficulty of firefighting is E. The disposal priority value of the fire point is:

[0168]

[0169] Then, according to the calculated priority value, all wildfires in the area are sorted to determine the order of treatment. When allocating resources, they are classified and marked according to their nature and purpose. Resources are divided into fire-fighting resources (fire trucks and fire extinguishers), protective resources (fire-resistant clothing and insulating gloves), emergency repair resources (power repair vehicles, work shovels), and communication resources (walkie-talkies and satellite phones). The four resources are numbered and marked, and the resources are allocated in descending order according to the priority of the wildfire scene. Specific embodiment 9

[0171] like Figure 1 As shown, according to the contents in the above specific embodiments, the following contents are further disclosed:

[0172] The entire system is a comprehensive system that integrates fire point data collection, analysis, early warning, risk assessment and emergency decision-making and disposal. Through the fire point monitoring system with multi-source data fusion, satellite remote sensing, ground monitoring stations and drone inspections are used to comprehensively collect fire point data, and a data fusion and verification mechanism is established to ensure data quality. After the fire point information is imported into the transmission line wildfire monitoring and early warning system, the geographic information system (GIS) technology is used to match the fire point and the tower coordinates to generate alarm information. After the on-site operation and maintenance specialist obtains detailed on-site information, the system performs risk calculations, including the probability of transmission line tripping due to wildfires, the probability of air gap breakdown and the probability of relative ground breakdown, etc., and then realizes emergency decision-making, determines the voltage level coefficient, calculates the trip load loss and risk value, and finally allocates resources according to the disposal priority to achieve a reasonable arrangement of wildfire fighting and transmission line protection.

[0173] The comprehensive use of three different monitoring methods achieves complementarity in space, time and monitoring scope at the data collection level, creatively improving the accuracy and timeliness of fire point monitoring.

[0174] The data fusion algorithm uses weighted averaging and Kalman filtering, combined with scene-based radiation correction, high-precision DEM data and multi-source geographic coordinate reference data, as well as deep learning-based image recognition algorithms and optimized image difference algorithms. It has significant innovations in data processing and analysis, providing a more accurate and reliable data foundation for the entire system.

[0175] A probability calculation model for transmission line tripping due to wildfires was established that comprehensively considers multiple factors. A probability calculation model for air gap breakdown under wildfire conditions was established by combining gas discharge theory and experimental data. A probability model for relative ground breakdown of transmission lines was also established by combining the ground resistance change model. These innovative risk assessment models more scientifically and comprehensively assess the risk level of wildfires to transmission lines. In emergency decision-making, a weighted summation method was used to establish a priority calculation model for wildfire handling of transmission lines, providing an innovative decision-making basis for the rational allocation of resources.

[0176] Practical on-site monitoring and information acquisition. On-site operation and maintenance specialists are equipped with professional monitoring equipment to comprehensively measure and record the landforms, transmission line parameters, vegetation conditions, meteorological conditions and smoke concentration, and upload data in real time, providing rich actual on-site information for the system's accurate analysis and decision-making. It is very practical. The system's series of optimization and innovative technologies in coordinate conversion, distance and azimuth calculation, data transmission and processing are closely centered on actual application needs, ensuring the feasibility and effectiveness of the system in complex transmission line wildfire monitoring scenarios. Novel technical applications and concepts introduce advanced satellite remote sensing technology, drone inspection technology, geographic information system technology, deep learning algorithms and other cutting-edge technologies, and organically integrate them into the field of transmission line wildfire monitoring and emergency response. It is very novel. The full process design from fire point monitoring to emergency response, as well as the establishment of a strict data verification mechanism, a multi-departmental linkage alarm notification method and resource allocation strategy, all reflect the novel system design concept and workflow, and provide a new solution for ensuring the safety of transmission lines and responding to wildfire disasters.

[0177] A computer device comprising:

[0178] A processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute the above-mentioned flame combustion model-driven transmission line wildfire risk assessment method.

[0179] A storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned flame combustion model-driven transmission line wildfire risk assessment method.

[0180] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0181] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A flame combustion model driven transmission line wildfire risk assessment method, characterized in that , including the following steps: Use satellite remote sensing, ground monitoring stations and drone inspections to collect fire point data information, and integrate the fire point data obtained by satellite remote sensing, ground monitoring stations and drone inspections; According to the fire point coordinates of the fused fire point data and the stored line tower coordinates, the straight-line distance between the fire point and the nearest tower and the azimuth information relative to the tower are calculated to generate wildfire warning information; According to the wildfire warning information, video collection and measured data recording of wildfire scene information are carried out; Based on the wildfire site information, the probability of transmission line tripping due to wildfire is calculated, the probability of air gap breakdown under wildfire conditions is calculated, the probability of relative ground breakdown of the transmission line is calculated, and then the risk value is obtained.

2. The method for assessing forest fire risk of power transmission lines driven by a flame combustion model according to claim 1, characterized in that: The fire point data collection further includes the following: Acquire infrared satellite remote sensing data released by the National Meteorological Satellite Center in real time, capture thermal radiation information on the earth's surface, and process satellite images using image processing algorithms. First, perform radiation correction to eliminate the noise and error of the sensor itself using the following formula: L corrected =L observed -L noise Where L observed is the radiation value observed by the satellite, L noise is the noise radiation value determined by the sensor calibration parameters, L corrected It is the corrected radiation value, and then geometric correction is performed. Using geographic coordinate reference data and satellite orbit parameters, the pixel coordinates of the satellite image are converted into geographic coordinates through a mathematical model to ensure that each point in the image accurately corresponds to the actual location on the earth; Real-time monitoring of the temperature distribution of the surrounding environment along the transmission line. Based on the relationship between the infrared radiation energy and temperature of the object, the temperature distribution of the object is presented in the form of an image. When an abnormally high temperature area is detected, the data collection function is automatically triggered, and the temperature data and corresponding location information are transmitted to the data center. Real-time monitoring of the smoke concentration in the air is carried out, and the smoke concentration is determined by detecting the scattering or absorption of light by smoke particles. When the smoke concentration exceeds the set threshold, an alarm signal and related data are sent; Inspect the areas around the transmission lines, take visible light images of the ground, capture the shape and characteristics of ground objects, obtain thermal infrared images of the ground, detect temperature abnormalities through thermal infrared images, and process the collected data. By comparing images and temperature data at different times, potential fire points are discovered and their locations and ranges are determined. An image difference algorithm is used to compare the current image with historical images to discover areas of pixel value changes, identify fire point areas, and then determine the geographical location of the fire point by combining the shooting location information and shooting angle.

3. The method for assessing the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 2, characterized in that: After the satellite remote sensing, ground monitoring station and drone inspection data are collected, a data fusion and verification mechanism is established to fuse the fire point data obtained by satellite remote sensing, ground monitoring station and drone inspection; Data fusion algorithm: The weighted average fusion algorithm is used. First, the weight is set according to the accuracy and reliability of the data source. The weight of satellite remote sensing data is set to W. satellite , the ground monitoring station data weight is set to W ground , the weight of drone inspection data is set to W drone , and W satellite +W ground +W drone =1; For the location information (x, y) of the fire point, the fused location coordinates (x fused ,y fused ), the calculation formula is: x fused =W satellite x satellite +W ground x ground +W drone x drone y fused =W satellite y satellite +W ground y ground +W drone y drone Where (x satellite ,y satellite )、(x ground ,y ground )、(x drone ,y drone ), are the fire point location coordinates detected by satellite remote sensing, ground monitoring stations and drone inspections, respectively. For the fire area A, the fused area A fused The calculation formula is: A fused =W satellite A satellite +W ground A ground +W drone A drone Among them A satellite , A ground and A drone The fire areas were estimated by satellite remote sensing, ground monitoring stations and drone inspections.

4. The method for assessing forest fire risk of power transmission lines driven by a flame combustion model according to claim 3 is characterized in that: After the data is fused, data verification further includes the following contents: The fused fire point data is verified using historical fire data and geographic information data. The currently detected fire point locations are compared with areas where fires have occurred historically. Fire points are detected in areas with a high historical fire incidence and are determined to be reliable fire point data. At the same time, the rationality of the fire points is judged in combination with geographic information data.

5. The method for evaluating the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 1, characterized in that: The transmission line generating mountain fire warning information further includes the following contents: The wildfire warning information includes the pole tower number closest to the fire point, the straight-line distance from the pole tower, and the direction relative to the pole tower. The stored transmission line tower coordinate information is recorded in the database in the form of geographic coordinates. Each pole tower has a unique identification number. The geographic information system technology is used to match the fire point coordinates with the pole tower coordinates. First, the fire point coordinates and the pole tower coordinates are converted to the same geographic coordinate system, and then the distance between each fire point and all the pole towers is calculated through the spatial query algorithm; for each fire point, find the pole tower closest to it, and record the number of the pole tower, calculate the straight-line distance between the fire point and the nearest pole tower, and use the Euclidean distance formula to calculate, that is, Where (x fire -y fire ) is the coordinate of the fire point, (x tower ,y tower ) is the coordinate of the nearest tower, and d is the straight-line distance between them; Determine the direction of the fire point relative to the tower by calculating the angle between the line connecting the fire point coordinates and the nearest tower coordinates and the true north direction. Use the inverse tangent function to calculate, that is, Where θ is the azimuth of the fire point relative to the tower, unit: degree; When the distance between the fire point and the tower reaches the preset warning threshold, an alarm signal is issued.

6. The method for evaluating the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 1, characterized in that: In the method of obtaining the wildfire site information, the wildfire site information includes the topography of the wildfire site, the line height and line spacing of the transmission lines, the vegetation near the wildfire, the wind speed and direction, the temperature characteristics under the transmission lines, and the smoke concentration at the site. First, the topography is measured, and the terrain of the wildfire site is measured in detail. Control points are selected on site, and a local terrain reference framework is established by measuring the three-dimensional coordinates of the longitude, latitude and elevation of the control points. Then, point-by-point measurements are performed along a preset measurement route to obtain the elevation data of the ground points and record the slope and slope direction information of the terrain.

7. The method for assessing the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 6, characterized in that: The acquisition of on-site information includes transmission line parameter measurement and vegetation information investigation, measuring the height of the line tower to the ground and the horizontal and vertical distances between adjacent towers to determine the line spacing, and recording the model and material information of the transmission line. At the same time, using a vegetation identification manual and with the help of a mobile application, the type of vegetation, including tree species, shrub species and herbaceous plants, is identified, and the coverage and density of vegetation is estimated. In addition, the growth status and health status of the vegetation and the presence of flammable vegetation are observed.

8. The method for assessing forest fire risk of power transmission lines driven by a flame combustion model according to claim 6, characterized in that: The acquisition of on-site information includes meteorological condition monitoring and smoke concentration detection, real-time monitoring of wind speed and wind direction on site, measuring temperature characteristics under the transmission line, recording temperature data at different times and locations at the bottom of the transmission line tower, and detecting smoke concentration on site.

9. The method for evaluating the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 1, characterized in that: The risk calculation further includes the following: Calculation of the probability of power transmission line tripping due to wildfire: First, the voltage level, tower structure, and insulation configuration information of the transmission line are obtained. At the same time, the vegetation type, fire size, flame temperature, wildfire spread speed, wildfire and transmission line location information are collected, and the wind speed, wind direction, and air humidity meteorological data are obtained. Then, a tripping probability model is established. Based on the collected data, the data of transmission line tripping caused by historical wildfires are used for statistical analysis. The impact of thermal radiation and smoke particles generated by wildfires on the insulation performance of transmission lines is analyzed, and a theoretical calculation model is established. When the thermal radiation intensity exceeds the threshold, the line is at risk of tripping. The tripping probability P is estimated by the following formula t1 : Where I is the thermal radiation intensity generated by the wildfire, I th is the thermal radiation intensity threshold that causes the line to trip, σ is the standard deviation of the thermal radiation intensity distribution obtained based on historical data statistics, and then the collected wildfire and meteorological data are substituted into the tripping probability model for calculation; Calculation of the probability of air gap breakdown under wildfire conditions: Firstly, a breakdown probability model is established based on gas discharge theory and experimental data. According to Paschen's law, the original breakdown voltage of the air gap is assumed to be U b0 , the air density under wildfire conditions becomes ρ, the temperature becomes T, and the corrected breakdown voltage U b Calculated by the following formula: Where ρ0 and T0 are the air density and temperature under standard conditions, respectively. Then, according to the actual operating voltage U of the transmission line, the breakdown probability P is calculated. b : Where δ is the standard deviation of breakdown voltage distribution obtained from experimental data statistics; When calculating, first measure or estimate the air temperature and density changes in the wildfire area, substitute them into the above breakdown voltage correction formula, and calculate the breakdown voltage U of the air gap under wildfire conditions. b ; Calculation of the probability of phase-to-ground breakdown of transmission lines: The air gap breakdown probability model and ground resistance change model under wildfire conditions are combined to calculate the relative ground breakdown probability. Assuming the ground resistance is R g , the ground resistance before the wildfire is R g0 The ground resistance change rate is According to circuit theory, the line voltage relative to ground is U r , which can be expressed as Where U is the phase voltage of the transmission line. Then, combined with the air gap breakdown probability model, the relative-to-ground breakdown probability model is calculated to calculate the relative-to-ground breakdown probability P. r , when the relative voltage U of the air gap is calculated r The breakdown probability under b,r When considering the comprehensive influence of ground resistance change on line insulation, the relative ground breakdown probability P r Calculated by the following formula: P r =P b,r (1+Kα) Where K is a coefficient determined based on experience or experiment, reflecting the influence of the change in ground resistance on the relative ground breakdown probability.

10. The method for evaluating the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 1, characterized in that: Also includes: Determine the voltage level coefficient of the transmission line, find and determine the voltage level coefficient of the current transmission line according to the pre-established voltage level and coefficient correspondence table, calculate the load loss of the transmission line wildfire tripping, calculate the transmission line wildfire tripping risk value, the transmission line wildfire tripping risk value is the sum of the transmission line wildfire tripping risk values ​​generated by all fire points in the area, and calculate the transmission line wildfire disposal priority; When large-scale wildfires occur at the same time and existing emergency decision-making resources are limited, all wildfires are sorted according to the calculated handling priorities of each wildfire site, and resources are allocated in descending order of priority to fight wildfires and protect power transmission lines in combination with existing emergency decision-making resources.

11. The method for evaluating the risk of wildfires in power transmission lines driven by a flame combustion model according to claim 10, characterized in that: The emergency decision-making and resource allocation further include the following: First, according to the determined priority factors, a calculation model for the priority of wildfire disposal of transmission lines is established. A weighted summation method is used to assign a weight coefficient to each factor. Then, the value of each factor is multiplied by the corresponding weight coefficient and added together to obtain the final priority value. Then, a priority calculation model is established. The specific method is as follows: Assume that the weight of the risk value of wildfire tripping is W1, the weight of the fire point scale is W2, the weight of the distance between the fire point and important facilities is W3, the weight of the wildfire spread trend is W4, and the weight of the difficulty of firefighting is W5. For a single fire point, the wildfire tripping risk value is R, the fire point scale value is S, the distance between the fire point and important facilities is D, the wildfire spread trend value is M, and the difficulty of firefighting is E. The disposal priority value of the fire point is: Then, based on the calculated priority value, all wildfires in the area are sorted to determine the order of their treatment. When allocating resources, they are classified and marked according to their nature and purpose. Resources are divided into fire-fighting resources, protection resources, emergency repair resources, and communication resources. The four types of resources are numbered and marked, and resources are allocated in descending order according to the priority of the wildfire scene.

12. The risk assessment system of the power transmission line wildfire risk assessment method driven by a flame combustion model according to claim 1 is characterized in that ,include: The fire point data collection module uses satellite remote sensing, ground monitoring stations and drone inspections to collect fire point data information, and integrates the fire point data obtained by satellite remote sensing, ground monitoring stations and drone inspections; The transmission line wildfire alarm information acquisition module calculates the straight-line distance between the fire point and the nearest tower and the azimuth information relative to the tower based on the fire point coordinates of the fused fire point data and the stored line tower coordinates, and generates wildfire alarm information; The on-site information acquisition module collects video and records measured data of the wildfire scene information based on the wildfire alarm information; The risk calculation module calculates the probability of transmission line tripping due to wildfire, the probability of air gap breakdown under wildfire conditions, the probability of relative ground breakdown of transmission lines, and then derives the risk value based on the wildfire site information.

13. A flame combustion model driven power line wildfire risk assessment system according to claim 12, characterized in that ,The fire point data collection module includes: The satellite remote sensing data collection module obtains the infrared satellite remote sensing data released by the National Meteorological Satellite Center in real time through satellite data receiving equipment, captures the thermal radiation information of the earth's surface through satellite sensors, and processes satellite images using the image processing module; The data collection module of the ground monitoring station includes an infrared thermal imager and a smoke sensor. The infrared thermal imager monitors the temperature distribution of the surrounding environment in real time. The infrared thermal imager presents the temperature distribution of the object in the form of an image based on the relationship between the infrared radiation energy and temperature of the object. When an abnormally high temperature area is detected, the data collection function is automatically triggered, and the temperature data and corresponding location information are transmitted to the data center. The smoke sensor monitors the smoke concentration in the air in real time and determines the smoke concentration by detecting the scattering or absorption of light by smoke particles. When the smoke concentration exceeds the set threshold, an alarm signal and related data are sent; The drone inspection data collection module uses a drone equipped with a camera and an infrared thermal imager. The camera is used to capture visible light images of the ground and capture the shape and characteristics of ground objects. The infrared thermal imager obtains thermal infrared images of the ground and detects temperature abnormalities through thermal infrared images. The ground control station processes the data collected by the drone and discovers potential fire points by comparing images and temperature data at different times, and determines their locations and ranges. An image difference algorithm is used to compare the current image with the historical image to discover areas where pixel values ​​change, identify the fire point area, and then determine the geographical location of the fire point by combining the drone's location information and shooting angle.

14. A flame combustion model driven power line wildfire risk assessment system according to claim 12, characterized in that The module for obtaining the forest fire alarm information of the transmission line is used to convert the coordinates of the fire point and the tower coordinates into the same geographic coordinate system, and then calculate the distance between each fire point and all the towers through the spatial query algorithm; for each fire point, find the tower closest to it, record the number of the tower, and calculate the straight-line distance between the fire point and the nearest tower; determine the direction of the fire point relative to the tower, and determine the direction by calculating the angle between the line connecting the fire point coordinates and the nearest tower coordinates and the true north direction; When the distance between the fire point and the tower reaches the preset warning threshold, an alarm signal is issued.

15. The flame combustion model driven transmission line wildfire risk assessment system according to claim 12, characterized in that The on-site information acquisition module includes a topographic surveying instrument, a laser rangefinder, a temperature sensor, an anemometer and a smoke concentration detector. The topographic surveying instrument is used for topographic measurement; the laser rangefinder measures the height of the line tower to the ground and the horizontal and vertical distances between adjacent towers; the anemometer monitors the wind speed and direction of the site in real time, the temperature sensor measures the temperature characteristics under the transmission line, and the temperature sensor is installed at the bottom of the transmission line tower. The smoke concentration detector detects the smoke concentration on site.

16. The flame combustion model driven power line wildfire risk assessment system according to claim 12, characterized in that ,The risk calculation module includes: The transmission line tripping probability calculation module due to wildfire calculates the transmission line tripping probability due to wildfire based on the fire point data information integrated by the fire point data collection module, the wildfire site information collected by the site information acquisition module, the measured data records and the historical statistical data; The air gap breakdown probability calculation module under wildfire conditions is used to establish a breakdown probability model, and calculate the air gap breakdown probability under wildfire conditions by measuring or estimating the air temperature and density changes in the wildfire area; The transmission line relative-to-ground breakdown probability calculation module calculates the transmission line relative-to-ground breakdown probability through the air gap breakdown probability model under wildfire conditions and the grounding resistance change model.

17. The flame combustion model driven power line wildfire risk assessment system according to claim 12, characterized in that ,It also includes emergency decision-making module and resource allocation module, The emergency decision-making module is used to determine the voltage level coefficient of the transmission line. According to the pre-established voltage level and coefficient correspondence table, the voltage level coefficient of the current transmission line is searched and determined, the load loss of the transmission line wildfire tripping is calculated, and the risk value of the transmission line wildfire tripping is calculated. The risk value of the transmission line wildfire tripping is the sum of the risk values ​​of the transmission line wildfire tripping generated by all fire points in the area, and the priority of the transmission line wildfire disposal is calculated; The resource allocation module is used to sort all wildfires according to the calculated handling priorities of each wildfire site when large-scale wildfires occur simultaneously and the existing emergency decision-making resources are limited. In order of priority from high to low, resources are allocated to wildfire fighting and power transmission line protection in combination with existing emergency decision-making resources.

18. A computer device, characterized in that: include: processor; and a memory configured to store machine-readable instructions, which, when executed by the processor, execute a method for assessing the risk of wildfires in power transmission lines driven by a flame combustion model as described in any one of claims 1 to 11.

19. A storage medium, characterized in that: The storage medium stores a computer program, and the computer program is executed by a processor according to a method for assessing forest fire risks of power transmission lines driven by a flame combustion model as described in any one of claims 1 to 11.

Images