Power transmission line local forest fire tripping risk assessment method and device, electronic equipment and storage medium

Abstract

The invention discloses a power transmission line local forest fire tripping risk assessment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the position information of a power transmission line in a research region, and dividing the research region into a plurality of line grids and non-line grids; randomly selecting a certain line grid as a target grid; obtaining forest fire risk influence factors, wherein the influence factors comprise meteorological conditions, geographic factors and combustible conditions; determining a forest fire occurrence risk grade of the target line grid according to the forest fire risk influence factor; determining the flame height of the target grid according to the forest fire risk influence factor influencing the grid, and generating a corresponding flame height risk grade; and determining the forest fire tripping risk of the target line grid in combination with the forest fire occurrence risk level and the flame height risk level. By implementing the method, meteorological conditions, geographic factors and combustible conditions can be comprehensively considered, and the local forest fire tripping risk of the power transmission line can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a method, device, electronic equipment and storage medium for evaluating the risk of local wildfire tripping of a transmission line. Background Art

[0002] my country's energy and power loads are distributed inversely. In order to achieve the optimal allocation of power resources, a large number of transmission corridors cross forests and mountains. Affected by extreme weather, surface environment, residents' fire habits and other factors, large-scale wildfires are prone to occur around transmission corridors during the high-incidence periods of wildfires in the wild, such as the Spring Festival, Qingming Festival and autumn harvest. Under the influence of wildfires, the insulation strength of the air gap of the transmission line is reduced, which can easily cause short-circuit breakdown of the line, induce transmission line tripping, and the success rate of reclosing is low, which seriously endangers the safe and stable operation of the power grid. On the other hand, with the influence of extreme weather in recent years, the outbreak of wildfires has become more and more frequent. Statistics show that a total of 5,649 wildfires occurred on the transmission lines under the jurisdiction of the Southern Power Grid from 2023 to 2024, and the total number of trips caused by wildfire disasters reached 204 times, which has seriously affected the power supply reliability of the power grid.

[0003] The existing technology determines the degree of harm of wildfires to transmission lines based on the distance between the location of wildfires and the transmission lines monitored by satellites. The wildfire alarm of transmission lines only relies on the differentiated risk level of the distance between the fire point and the line, without comprehensively considering the meteorological conditions and combustible conditions. It is impossible to judge whether the flame intensity will cause the line to trip when the wildfire burns below the line, thereby affecting the accuracy of the assessment of the risk of wildfire tripping. For example, when a wildfire occurs, if it is just after rain or when it is raining, the vegetation on the underlying surface near or below the transmission line is flammable vegetation, or the burning height of the vegetation has a great impact on the transmission line, and the flame height of the underlying surface does not maintain a certain insulation distance from the transmission line, even if it spreads to the bottom of the transmission line, it will cause tripping. However, when it is dry and exposed to the sun, the vegetation on the underlying surface near or below the transmission line is non-flammable vegetation, or the burning height of the vegetation has little effect on the transmission line, and the flame height of the underlying surface maintains a certain insulation distance from the transmission line, even if it spreads to the bottom of the transmission line, it will not cause tripping. Summary of the invention

[0004] The embodiment of the present invention provides a method, device, electronic device and storage medium for evaluating the risk of local wildfire tripping of a power transmission line. By implementing the present invention, meteorological conditions, geographical factors and combustible conditions can be comprehensively considered to accurately evaluate the risk of local wildfire tripping of a power transmission line, thereby ensuring the safety and stability of the power grid.

[0005] An embodiment of the present invention provides a method for assessing the risk of local wildfire tripping of a transmission line, comprising:

[0006] Obtain location information of transmission lines in the study area;

[0007] The study area is rasterized to obtain several grids;

[0008] Dividing all grids according to the location information of the transmission line to obtain a number of line grids and a number of non-line grids; randomly selecting a line grid as a target line grid;

[0009] Obtain the wildfire risk influencing factors of the target line grid; wherein the wildfire risk influencing factors include: meteorological conditions, geographical factors and combustible conditions;

[0010] Determine the wildfire risk level of the target line grid according to the wildfire risk influencing factor of the target line grid;

[0011] Acquire a plurality of line grids and a plurality of non-line grids that are at a preset distance from a target line grid, and set the acquired grids and the target line grid as influencing grids;

[0012] Obtain the wildfire risk impact factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid according to the wildfire risk impact factor of each influencing grid; take the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid;

[0013] According to the flame height of the target line grid, the corresponding flame height risk level is generated;

[0014] The wildfire tripping risk of the target line grid is determined based on the wildfire occurrence risk level and flame height risk level of the target line grid.

[0015] Furthermore, all grids are divided according to the location information of the transmission line to obtain a number of line grids and a number of non-line grids, including:

[0016] generating a spatial path of the transmission line according to the location information of the transmission line;

[0017] Project the spatial paths of transmission lines onto the study area;

[0018] For each grid in the study area, determine whether the grid intersects with the spatial path of the transmission line; if so, mark the grid as a line grid; if not, mark the grid as a non-line grid.

[0019] Furthermore, determining the wildfire risk level of the target line grid according to the wildfire risk impact factor of the target line grid includes:

[0020] Discretize the wildfire risk impact factor of the target line grid to obtain a discrete wildfire risk impact factor;

[0021] The discrete wildfire risk influencing factors are input into the Bayesian risk model to generate the wildfire occurrence probability of the target line grid;

[0022] According to the wildfire occurrence probability of the target line grid and several preset wildfire risk level intervals, the wildfire occurrence probability of the target line grid is graded to generate the corresponding wildfire occurrence risk level.

[0023] Furthermore, the Bayesian risk model is constructed in the following way:

[0024] Obtain wildfire samples and no-wildfire samples for the historical period of the transmission line; each sample contains the corresponding discrete wildfire risk influencing factor;

[0025] Using the maximum likelihood estimation method, the conditional probability of each discrete wildfire risk influencing factor under the conditions of wildfire occurrence and non-occurrence is calculated to obtain several first probabilities and several second probabilities respectively;

[0026] Calculating the posterior probability of a wildfire according to the first probability;

[0027] Calculating the posterior probability that no wildfire has occurred based on the second probability;

[0028] According to the posterior probability of wildfire occurrence and the posterior probability of no wildfire occurrence, the posterior probability of wildfire occurrence is normalized to generate the probability of wildfire occurrence;

[0029] According to the wildfire occurrence probability, a Bayesian risk model is constructed; wherein the Bayesian risk model is used to output the wildfire occurrence probability of a grid by inputting the discrete wildfire risk influencing factor of the grid.

[0030] Furthermore, the wildfire occurrence probability of the target line grid is graded according to the wildfire occurrence probability of the target line grid and a plurality of preset wildfire risk level intervals to generate a corresponding wildfire occurrence risk level, including:

[0031] Compare the wildfire occurrence probability of the target line grid with several preset wildfire risk level intervals;

[0032] If the wildfire occurrence probability is within the preset low-risk wildfire level interval, the wildfire occurrence risk level of the target line grid is set to low risk;

[0033] If the wildfire occurrence probability is within the preset medium risk wildfire level interval, the wildfire occurrence risk level of the target line grid is set to medium risk;

[0034] If the probability of wildfire occurrence is within the preset medium-high risk wildfire level range, the wildfire risk level of the target line grid is set to medium-high risk;

[0035] If the probability of wildfire occurrence is within the preset high-risk wildfire level interval, the wildfire risk level of the target line grid is set to high risk.

[0036] Furthermore, according to the wildfire risk influencing factors of each influencing grid, the vegetation burning height of each influencing grid is calculated and generated, including:

[0037] According to the meteorological conditions of each influencing grid, the initial flame spread speed and wind speed correction coefficient of each influencing grid are calculated and generated;

[0038] According to the slope of each geographical factor affecting the grid, the slope correction coefficient of each grid affecting the grid is calculated and generated;

[0039] According to the vegetation type in the fuel conditions of each influencing grid, the fuel configuration pattern coefficient of each influencing grid is determined;

[0040] Calculate and generate the wildfire spread speed of each influencing grid according to the initial flame spread speed, wind speed correction coefficient, slope correction coefficient and combustible material configuration pattern coefficient;

[0041] Determine the calorific value of the combustible per unit mass of each influencing grid according to the combustible type in the combustible condition of each influencing grid;

[0042] According to the combustible load in the combustible conditions of each influencing grid, the fire line intensity of each influencing grid is calculated and generated;

[0043] According to the fire line intensity of each impact grid, the vegetation burning height of each impact grid is calculated and generated.

[0044] Furthermore, according to the flame height of the target line grid, the corresponding flame height risk level is generated, including:

[0045] Comparing the flame height of the target line grid with a plurality of preset flame height level intervals;

[0046] If the flame height of the target line grid is within the preset low intensity flame height risk level interval, the flame height risk level of the target line grid is set to low intensity;

[0047] If the flame height of the target line grid is within the preset medium intensity flame height risk level interval, the flame height risk level of the target line grid is set to medium intensity;

[0048] If the flame height of the target line grid is within a preset high intensity flame height risk level interval, the flame height risk level of the target line grid is set to high intensity.

[0049] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0050] An embodiment of the present invention provides a transmission line local wildfire tripping risk assessment device, comprising: a target grid determination module, a wildfire occurrence risk determination module, a flame height risk determination module, and a wildfire tripping risk determination module;

[0051] The target grid determination module is used to obtain the location information of the power transmission line in the study area; perform grid processing on the study area to obtain a plurality of grids; divide all grids according to the location information of the power transmission line to obtain a plurality of line grids and a plurality of non-line grids; and randomly select a certain line grid as the target line grid;

[0052] The wildfire risk determination module is used to obtain the wildfire risk impact factor of the target line grid; wherein the wildfire risk impact factor includes: meteorological conditions, geographical factors and combustible conditions; according to the wildfire risk impact factor of the target line grid, determine the wildfire risk level of the target line grid;

[0053] The flame height risk determination module is used to obtain a number of line grids and a number of non-line grids at a preset distance from the target line grid, and set the obtained grids and the target line grid as influencing grids; obtain the wildfire risk influencing factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid according to the wildfire risk influencing factor of each influencing grid; use the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; and generate the corresponding flame height risk level according to the flame height of the target line grid;

[0054] The wildfire tripping risk determination module is used to determine the wildfire tripping risk of the target line grid according to the wildfire occurrence risk level and the flame height risk level of the target line grid.

[0055] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0056] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for assessing the risk of local wildfire tripping of a transmission line as described in any one of the above-mentioned method embodiments can be implemented.

[0057] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0058] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for assessing the risk of local wildfire tripping of a transmission line as described in any one of the above method embodiments can be implemented.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The embodiment of the present invention provides a method, device, electronic device and storage medium for evaluating the risk of local wildfire tripping of a transmission line, and realizes an accurate evaluation of the risk of wildfire tripping by comprehensively considering various wildfire risk influencing factors of the target line grid. According to the wildfire risk influencing factors such as meteorological conditions, geographical factors and combustible conditions, the probability of wildfire occurrence of the target line grid is calculated, and the wildfire risk level is determined accordingly. Based on the combustible conditions of each influencing grid, the flame height risk level of the target line grid is determined. By combining the wildfire risk level and the flame height risk level, the wildfire tripping risk of the target line grid is comprehensively determined. The present invention not only comprehensively considers the wildfire risk influencing factors such as meteorological conditions, geographical factors and combustible conditions, and evaluates the probability of wildfire occurrence, but also considers the threat of flame height to the transmission line, avoiding the limitation of relying solely on the distance between the fire point and the line, and can more accurately evaluate the local wildfire tripping risk of the transmission line, thereby more effectively ensuring the safe operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a method for assessing the risk of local wildfire tripping of a transmission line provided by one embodiment of the present invention.

[0062] Figure 2 It is a schematic diagram of rasterization of a research area provided by an embodiment of the present invention.

[0063] Figure 3 It is a schematic diagram of an influence grid provided by an embodiment of the present invention.

[0064] Figure 4 It is a structural schematic diagram of a device for assessing the risk of local wildfire tripping of a transmission line provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0065] 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.

[0066] 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.

[0067] like Figure 1 As shown, an embodiment of the present invention provides a method for assessing the risk of local wildfire tripping of a transmission line, comprising at least the following steps:

[0068] Step S1, obtaining the location information of the power transmission lines in the study area;

[0069] Illustratively, the location information of the transmission lines in the study area can be obtained in a variety of ways, for example, by extracting the location information of the transmission lines from the geographic database of the study area through a geographic information system; obtaining high-resolution images through satellite remote sensing or drone aerial photography to determine the location information of the transmission lines, especially in areas with complex terrain or heavy vegetation coverage; conducting on-site surveys of the study area and using professional measuring equipment to record the precise location information of the transmission lines.

[0070] Step S2, rasterizing the study area to obtain a number of grids;

[0071] For example, Figure 2 As shown in the figure, a grid size of 1000m×1000m is selected to rasterize the study area and obtain several grids.

[0072] Depending on the specific research needs, a smaller grid size, such as 250m×250m or 500m×500m, can also be selected to ensure sufficient spatial coverage and data details. Rasterization of the study area can convert complex geographic data and environmental information into discrete grid cells, uniformly representing all types of data in the entire area. This processing method not only standardizes the data spatially, eliminates differences between different sources or formats, but also makes analysis and calculation more convenient. The rasterized area can be accurately quantitatively analyzed through a unified grid, which improves the spatial visualization of the data. By dividing the information into a regular grid, it is convenient to further perform interpolation analysis and data overlay operations. In addition, different grid sizes can flexibly respond to different accuracy requirements and provide support for refined management and decision-making analysis in high-risk areas.

[0073] Step S3, dividing all grids according to the location information of the transmission line to obtain a number of line grids and a number of non-line grids; randomly selecting a line grid as a target line grid;

[0074] Specifically, in a preferred embodiment, all grids are divided according to the location information of the transmission line to obtain a plurality of line grids and a plurality of non-line grids, including:

[0075] generating a spatial path of the transmission line according to the location information of the transmission line;

[0076] Project the spatial paths of transmission lines onto the study area;

[0077] For each grid in the study area, determine whether the grid intersects with the spatial path of the transmission line; if so, mark the grid as a line grid; if not, mark the grid as a non-line grid.

[0078] It is understandable that by dividing all grids according to the location information of the transmission lines, the areas covered by the transmission lines can be effectively distinguished from other areas, generating line grids and non-line grids. This division method ensures the segmentation and identification of the line area and its surrounding areas, so that subsequent analysis can focus on the line grid and accurately identify potential fire risks. At the same time, by randomly selecting a certain line grid as the target line grid, it is possible to flexibly respond to the risk assessment needs of different line areas, help optimize resource allocation, and avoid the computational burden brought by full area coverage analysis. This operation enhances the focus on key line areas and improves the efficiency and accuracy of wildfire tripping risk analysis.

[0079] Step S4, obtaining the wildfire risk influencing factor of the target line grid; wherein the wildfire risk influencing factor includes: meteorological conditions, geographical factors and combustible conditions;

[0080] Preferably, meteorological conditions include: relative wind level, wind speed, temperature, relative air humidity, average annual precipitation and average annual temperature; geographical factors include: altitude, slope and aspect; combustible conditions include: combustible load, vegetation type, land use type and normalized vegetation index.

[0081] It should be noted that in order to obtain the wildfire risk influencing factors of the target line grid, it is necessary to extract the geographical factors and combustible conditions related to the grid from the basic database of the study area. This information provides the natural environment background of the area where the target line is located, and the data of these geographical factors and combustible conditions are mapped to the grid of the target line; meteorological data related to the study area are extracted from the surrounding meteorological stations, including wind level, wind speed, temperature, air humidity, average annual precipitation and average annual temperature, etc. Through the interpolation algorithm, these point-like meteorological data are mapped to the target line grid so that the grid contains the corresponding meteorological information.

[0082] Step S5, determining the wildfire risk level of the target line grid according to the wildfire risk impact factor of the target line grid;

[0083] Preferably, determining the wildfire risk level of the target line grid according to the wildfire risk impact factor of the target line grid includes:

[0084] Discretize the wildfire risk impact factor of the target line grid to obtain a discrete wildfire risk impact factor;

[0085] The discrete wildfire risk influencing factors are input into the Bayesian risk model to generate the wildfire occurrence probability of the target line grid;

[0086] According to the wildfire occurrence probability of the target line grid and several preset wildfire risk level intervals, the wildfire occurrence probability of the target line grid is graded to generate the corresponding wildfire occurrence risk level.

[0087] It should be noted that, since the annual average precipitation, annual average temperature, altitude, slope, aspect, fuel load and normalized vegetation index in the factors affecting wildfire risk are continuous variables, they are discretized according to the following factor discretization standards. When discretizing the aspect, manual discretization is required from a physical perspective. Among them, the factor discretization standards are:

[0088]

[0089]

[0090] Since the vegetation type in the fuel conditions is a discrete variable, the vegetation types are classified according to the table below to obtain the discretization results of the vegetation types.

[0091] Category No. Vegetation Type 1 Desert, Other, Swamp, Cultivated Plants 2 Meadow, tussock, steppe, alpine vegetation 3 Broadleaved forest, shrub 4 Coniferous forest, mixed coniferous and broad-leaved forest

[0092] Since the land use type in the combustible material conditions is a discrete variable, the land use types are classified according to the following table to obtain the discretization results of the land use types.

[0093] Category No. Land use type 1 Paddy fields, dry land, water areas, unused land, oceans, urban and rural areas, industrial and mining areas, and residential land 2 Shrubs, low-lying grasslands 3 Sparse woodland, medium covered grassland 4 Wooded land, other wooded land, high coverage grassland

[0094] Specifically, the Bayesian risk model is constructed in the following way:

[0095] Obtain wildfire samples and no-wildfire samples for the historical period of the transmission line; each sample contains the corresponding discrete wildfire risk influencing factor;

[0096] Using the maximum likelihood estimation method, the conditional probability of each discrete wildfire risk influencing factor under the conditions of wildfire occurrence and non-occurrence is calculated to obtain several first probabilities and several second probabilities respectively;

[0097] Calculating the posterior probability of a wildfire according to the first probability;

[0098] Calculating the posterior probability that no wildfire has occurred based on the second probability;

[0099] According to the posterior probability of wildfire occurrence and the posterior probability of no wildfire occurrence, the posterior probability of wildfire occurrence is normalized to generate the probability of wildfire occurrence;

[0100] According to the wildfire occurrence probability, a Bayesian risk model is constructed; wherein the Bayesian risk model is used to output the wildfire occurrence probability of a grid by inputting the discrete wildfire risk influencing factor of the grid.

[0101] Exemplarily, the posterior probability of a wildfire is calculated by the following formula:

[0102] P(Y=1|x1,x2,...,x n )=ΠP(x i |Y=1)

[0103] Where P(Y=1|x1,x2,...,x n ) is the posterior probability of wildfire; P(x i |Y=1) is the first probability of the i-th discrete wildfire risk influencing factor;

[0104] Exemplarily, the posterior probability of a wildfire is calculated by the following formula:

[0105] P(Y=0|x1,x2,...,x n )=ΠP(x i |Y=0)

[0106] Where P(Y=0|x1,x2,...,x n ) is the posterior probability that no wildfire occurred; P(x i |Y=0) is the second probability of the i-th discrete wildfire risk influencing factor;

[0107] Exemplarily, the probability of wildfire occurrence is calculated by the following formula:

[0108]

[0109] Where P is the probability of wildfire occurrence; P(Y=1|x1,x2,...,x n ) is the posterior probability of wildfire; P(Y=0|x1,x2,...,x n ) is the posterior probability that no wildfire occurred;

[0110] Optionally, the wildfire occurrence probability of the target line grid is graded according to the wildfire occurrence probability of the target line grid and a plurality of preset wildfire risk level intervals to generate a corresponding wildfire occurrence risk level, including:

[0111] Compare the wildfire occurrence probability of the target line grid with several preset wildfire risk level intervals;

[0112] If the wildfire occurrence probability is within the preset low-risk wildfire level interval, the wildfire occurrence risk level of the target line grid is set to low risk;

[0113] If the wildfire occurrence probability is within the preset medium risk wildfire level interval, the wildfire occurrence risk level of the target line grid is set to medium risk;

[0114] If the probability of wildfire occurrence is within the preset medium-high risk wildfire level range, the wildfire risk level of the target line grid is set to medium-high risk;

[0115] If the probability of wildfire occurrence is within the preset high-risk wildfire level interval, the wildfire risk level of the target line grid is set to high risk.

[0116] For example, the correspondence between the wildfire risk level interval and the wildfire risk level is shown in the following table:

[0117] P Wildfire risk level P≤0.25 Low risk 0.25＜P≤0.5 Medium risk 0.5＜P≤0.75 Medium to high risk 0.75＜P≤1 High risk

[0118] It can be understood that by grading the wildfire probability and generating corresponding wildfire risk levels according to the wildfire probability of the target line grid and several preset wildfire risk level intervals, it is helpful to achieve accurate hierarchical management of wildfire risks.

[0119] Step S6, obtaining a plurality of line grids and a plurality of non-line grids that are at a preset distance from the target line grid, and setting the obtained grids and the target line grid as influencing grids;

[0120] In an optional embodiment, if Figure 3 As shown, several line grids and several non-line grids that are 3 kilometers away from the target line grid are obtained, among which the yellow grid is the target line grid, the blue grid is the line grid that is 3 kilometers away from the target line grid, and the red grid is the non-line grid that is 3 kilometers away from the target line grid; the yellow grid, the blue grid and the red grid together constitute the influence grid.

[0121] It can be understood that by obtaining several line grids and non-line grids that are at a preset distance from the target line grid and setting them as influencing grids, the environmental factors of the target line grid and its surrounding areas can be effectively and comprehensively analyzed, ensuring that when assessing the risk of wildfire tripping, not only the conditions of the target grid itself are taken into account, but also various factors such as the meteorological, geographical and combustible materials in the surrounding area can be combined, thereby more comprehensively reflecting the potential impact of wildfires on the target line, improving the assessment accuracy of wildfire tripping risks, and providing stronger protection for the safety of transmission lines.

[0122] Step S7, obtaining the wildfire risk impact factor of each influencing grid; calculating and generating the vegetation burning height of each influencing grid according to the wildfire risk impact factor of each influencing grid; taking the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid;

[0123] Specifically, in a preferred embodiment, the vegetation burning height of each influencing grid is calculated and generated according to the wildfire risk influencing factor of each influencing grid, including:

[0124] According to the meteorological conditions of each influencing grid, the initial flame spread speed and wind speed correction coefficient of each influencing grid are calculated and generated;

[0125] According to the slope of each geographical factor affecting the grid, the slope correction coefficient of each grid affecting the grid is calculated and generated;

[0126] According to the vegetation type in the fuel conditions of each influencing grid, the fuel configuration pattern coefficient of each influencing grid is determined;

[0127] Calculate and generate the wildfire spread speed of each influencing grid according to the initial flame spread speed, wind speed correction coefficient, slope correction coefficient and combustible material configuration pattern coefficient;

[0128] Determine the calorific value of the combustible per unit mass of each influencing grid according to the combustible type in the combustible condition of each influencing grid;

[0129] According to the combustible load in the combustible conditions of each influencing grid, the fire line intensity of each influencing grid is calculated and generated;

[0130] According to the fire line intensity of each impact grid, the vegetation burning height of each impact grid is calculated and generated.

[0131] Exemplarily, the initial flame spread speed affecting the grid is calculated by the following formula:

[0132] R0=aT+bV+c(100-H)-D

[0133] Among them, R0 is the initial flame spread speed; T is temperature, unit is ℃; V is the relative wind force level, unit is level; H is the relative humidity of air, unit is %; a, b, c and D are constant coefficients; in actual operation, a is taken as 0.03, b is taken as 0.03, c is taken as 0.03, and D is taken as 0.3.

[0134] Exemplarily, the wind speed correction factor affecting the grid is calculated by the following formula:

[0135] K v =e 0.178V

[0136] Among them, K v is the wind speed correction coefficient; V is the wind speed.

[0137] Exemplarily, the slope correction factor affecting the grid is calculated by the following formula:

[0138]

[0139] Among them, K θ is the slope correction coefficient; θ is the slope.

[0140] Exemplarily, the combustible configuration pattern coefficient affecting the grid is determined by the following table:

[0141] Vegetation Type <![CDATA[Combustible configuration pattern coefficient K s > cedar 0.8 Thatch, weeds 1.6 Straw 0.6 Secondary forest 0.7 Coniferous forest 0.4 Flat needle leaves 0.8 Dead branches and leaves 1.2 Sedge, Birch 1.8 Pasture grassland 2 Woodlands of Korean pine, Chinese pine, Yunnan pine, etc. 1

[0142] Exemplarily, the wildfire spread rate is calculated by the following formula:

[0143] R=R0KvK θ K S

[0144] Among them, R is the wildfire spread speed; R0 is the initial wildfire spread speed; K v is the wind speed correction factor; K θ is the slope correction coefficient; K S Configure pattern coefficients for combustibles.

[0145] Exemplarily, the calorific value per unit mass of combustible material affecting the grid is determined by the following table:

[0146]

[0147]

[0148] Exemplarily, the fire line intensity is calculated by the following formula:

[0149] I=qWR / 600

[0150] Where I is the intensity of the fire line, in kW / m; q is the calorific value of the combustible material per unit mass, in kJ / kg; W is the combustible material load, in t / km 2 ; R is the wildfire spread speed, in m / min.

[0151] Exemplarily, according to the fire line intensity of each influencing grid, the vegetation burning height of each influencing grid is calculated and generated, including:

[0152] The uncorrected vegetation burn height affecting the raster is calculated using the following formula:

[0153] H f =γ(I / 250) 0.5

[0154] Among them, H f is the uncorrected vegetation burning height; I is the fire line intensity, in kW / m; γ is the regional correction factor, which is usually taken as 1.

[0155] The corrected vegetation burning height affecting the grid is calculated by the following formula, and the corrected vegetation burning height is used as the vegetation burning height affecting the grid:

[0156] H=K l H f

[0157] Where H is the corrected vegetation burning height; K l is the distance correction factor; H f is the uncorrected vegetation burn height;

[0158] Step S8, generating a corresponding flame height risk level according to the flame height of the target line grid;

[0159] In a preferred embodiment, according to the flame height of the target line grid, a corresponding flame height risk level is generated, including:

[0160] Comparing the flame height of the target line grid with a plurality of preset flame height level intervals;

[0161] If the flame height of the target line grid is within the preset low intensity flame height risk level interval, the flame height risk level of the target line grid is set to low intensity;

[0162] If the flame height of the target line grid is within the preset medium intensity flame height risk level interval, the flame height risk level of the target line grid is set to medium intensity;

[0163] If the flame height of the target line grid is within a preset high intensity flame height risk level interval, the flame height risk level of the target line grid is set to high intensity.

[0164] Exemplarily, the corresponding relationship between the flame height level interval and the flame height risk level is shown in the following table:

[0165] <![CDATA[Flame height H x > Flame height risk level <![CDATA[H x ≤1.5]]> Low intensity <![CDATA[1.5<H x ≤3]]> Medium Strength <![CDATA[H x >3]]> High strength

[0166] It can be understood that by generating the corresponding flame height risk level according to the flame height of the target line grid, the potential threat of the flame to the transmission line can be accurately assessed. The flame height is directly related to whether the fire will cause damage to the line equipment. Therefore, using it as an important factor in assessing the risk of mountain fire cannon tripping can more intuitively reflect the threat level of the fire to the line safety.

[0167] Step S9: Determine the wildfire tripping risk of the target line grid according to the wildfire occurrence risk level and the flame height risk level of the target line grid.

[0168] Optionally, determine the wildfire tripping risk of the target line grid using the following table:

[0169]

[0170] It is understandable that by determining the wildfire tripping risk based on the wildfire risk level and flame height risk level of the target line grid, the potential threat of different risk factors to the transmission line can be comprehensively evaluated. This method not only combines the probability of wildfire occurrence, but also considers the direct impact of flame height on the line, thereby avoiding the limitations of single factor evaluation, providing more accurate risk judgment, and thus providing a more reliable basis for the formulation of protective measures and emergency plans for transmission lines, effectively improving the safe operation of the power grid.

[0171] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0172] like Figure 4 As shown, an embodiment of the present invention provides a device for evaluating the risk of local wildfire tripping of a transmission line, including: a target grid determination module 101, a wildfire occurrence risk determination module 102, a flame height risk determination module 103 and a wildfire tripping risk determination module 104.

[0173] The target grid determination module 101 is used to obtain the location information of the power transmission line in the study area; perform grid processing on the study area to obtain a plurality of grids; divide all grids according to the location information of the power transmission line to obtain a plurality of line grids and a plurality of non-line grids; and randomly select a certain line grid as the target line grid;

[0174] The wildfire risk determination module 102 is used to obtain the wildfire risk impact factor of the target line grid; wherein the wildfire risk impact factor includes: meteorological conditions, geographical factors and combustible conditions; according to the wildfire risk impact factor of the target line grid, determine the wildfire risk level of the target line grid;

[0175] The flame height risk determination module 103 is used to obtain a number of line grids and a number of non-line grids at a preset distance from the target line grid, and set the obtained grids and the target line grid as influencing grids; obtain the wildfire risk influencing factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid according to the wildfire risk influencing factor of each influencing grid; take the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; and generate the corresponding flame height risk level according to the flame height of the target line grid;

[0176] The wildfire tripping risk determination module 104 is used to determine the wildfire tripping risk of the target line grid according to the wildfire occurrence risk level and the flame height risk level of the target line grid.

[0177] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0178] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0179] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for assessing the risk of local wildfire tripping of a transmission line described in any one of the present invention is implemented, or when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0180] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0181] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0182] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0183] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0184] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0185] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any of the above-mentioned methods for assessing the risk of local wildfire tripping of a transmission line of the present invention.

[0186] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0187] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0188] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for assessing the risk of local wildfire tripping of a transmission line, characterized in that: include: Obtain location information of transmission lines in the study area; The study area is rasterized to obtain several grids; Dividing all grids according to the location information of the transmission line to obtain a plurality of line grids and a plurality of non-line grids; Randomly select a line grid as the target line grid; Obtain the wildfire risk influencing factors of the target line grid; wherein the wildfire risk influencing factors include: meteorological conditions, geographical factors and combustible conditions; Determine the wildfire risk level of the target line grid according to the wildfire risk influencing factor of the target line grid; Acquire a plurality of line grids and a plurality of non-line grids that are at a preset distance from a target line grid, and set the acquired grids and the target line grid as influencing grids; Obtain the wildfire risk impact factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid according to the wildfire risk impact factor of each influencing grid; take the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; According to the flame height of the target line grid, the corresponding flame height risk level is generated; The wildfire tripping risk of the target line grid is determined based on the wildfire occurrence risk level and flame height risk level of the target line grid.

2. A method for assessing the risk of local wildfire tripping of a power transmission line as claimed in claim 1, characterized in that: All grids are divided according to the location information of the transmission line to obtain a number of line grids and a number of non-line grids, including: generating a spatial path of the transmission line according to the location information of the transmission line; Project the spatial paths of transmission lines onto the study area; For each grid in the study area, determine whether the grid intersects with the spatial path of the transmission line; if so, mark the grid as a line grid; if not, mark the grid as a non-line grid.

3. A method for assessing the risk of local wildfire tripping of a power transmission line as claimed in claim 1, characterized in that: Determining the wildfire risk level of the target line grid according to the wildfire risk impact factor of the target line grid includes: Discretize the wildfire risk impact factor of the target line grid to obtain a discrete wildfire risk impact factor; The discrete wildfire risk influencing factors are input into the Bayesian risk model to generate the wildfire occurrence probability of the target line grid; According to the wildfire occurrence probability of the target line grid and a number of preset wildfire risk level intervals, the wildfire occurrence probability of the target line grid is graded to generate a corresponding wildfire occurrence risk level.

4. A method for assessing the risk of local wildfire tripping of a power transmission line as claimed in claim 3, characterized in that: The Bayesian risk model is constructed in the following way: Obtain wildfire samples and no-wildfire samples for the historical period of the transmission line; each sample contains the corresponding discrete wildfire risk influencing factor; Using the maximum likelihood estimation method, the conditional probability of each discrete wildfire risk influencing factor under the conditions of wildfire occurrence and non-occurrence is calculated to obtain several first probabilities and several second probabilities respectively; Calculating the posterior probability of a wildfire according to the first probability; Calculating the posterior probability that no wildfire has occurred based on the second probability; According to the posterior probability of wildfire occurrence and the posterior probability of no wildfire occurrence, the posterior probability of wildfire occurrence is normalized to generate the probability of wildfire occurrence; According to the wildfire occurrence probability, a Bayesian risk model is constructed; wherein the Bayesian risk model is used to output the wildfire occurrence probability of a grid by inputting the discrete wildfire risk influencing factor of the grid.

5. A method for assessing the risk of local wildfire tripping of a power transmission line as claimed in claim 3, characterized in that: The method of grading the wildfire occurrence probability of the target line grid according to the wildfire occurrence probability of the target line grid and a plurality of preset wildfire risk level intervals to generate a corresponding wildfire occurrence risk level includes: Compare the wildfire occurrence probability of the target line grid with several preset wildfire risk level intervals; If the wildfire occurrence probability is within the preset low-risk wildfire level interval, the wildfire occurrence risk level of the target line grid is set to low risk; If the wildfire occurrence probability is within the preset medium risk wildfire level interval, the wildfire occurrence risk level of the target line grid is set to medium risk; If the probability of wildfire occurrence is within the preset medium-high risk wildfire level range, the wildfire risk level of the target line grid is set to medium-high risk; If the probability of wildfire occurrence is within the preset high-risk wildfire level interval, the wildfire risk level of the target line grid is set to high risk.

6. A method for assessing the risk of local wildfire tripping of a power transmission line as claimed in claim 1, characterized in that: According to the wildfire risk influencing factors of each influencing grid, the vegetation burning height of each influencing grid is calculated and generated, including: According to the meteorological conditions of each influencing grid, the initial flame spread speed and wind speed correction coefficient of each influencing grid are calculated and generated; According to the slope of each geographical factor affecting the grid, the slope correction coefficient of each grid affecting the grid is calculated and generated; According to the vegetation type in the fuel conditions of each influencing grid, the fuel configuration pattern coefficient of each influencing grid is determined; Calculate and generate the wildfire spread speed of each influencing grid according to the initial flame spread speed, wind speed correction coefficient, slope correction coefficient and combustible material configuration pattern coefficient; Determine the calorific value of the combustible per unit mass of each influencing grid according to the combustible type in the combustible condition of each influencing grid; According to the combustible load in the combustible conditions of each influencing grid, the fire line intensity of each influencing grid is calculated and generated; According to the fire line intensity of each impact grid, the vegetation burning height of each impact grid is calculated and generated.

7. A method for assessing the risk of local wildfire tripping of a power transmission line as claimed in claim 1, characterized in that: According to the flame height of the target line grid, the corresponding flame height risk level is generated, including: Comparing the flame height of the target line grid with a plurality of preset flame height level intervals; If the flame height of the target line grid is within the preset low intensity flame height risk level interval, the flame height risk level of the target line grid is set to low intensity; If the flame height of the target line grid is within the preset medium intensity flame height risk level interval, the flame height risk level of the target line grid is set to medium intensity; If the flame height of the target line grid is within a preset high intensity flame height risk level interval, the flame height risk level of the target line grid is set to high intensity.

8. A device for assessing the risk of local wildfire tripping of a transmission line, characterized in that: include: A target grid determination module, a wildfire occurrence risk determination module, a flame height risk determination module, and a wildfire tripping risk determination module; The target grid determination module is used to obtain the location information of the power transmission lines in the study area; The study area is gridded to obtain a plurality of grids; all grids are divided according to the location information of the transmission line to obtain a plurality of line grids and a plurality of non-line grids; a line grid is randomly selected as a target line grid; The wildfire risk determination module is used to obtain the wildfire risk impact factor of the target line grid; wherein the wildfire risk impact factor includes: meteorological conditions, geographical factors and combustible conditions; according to the wildfire risk impact factor of the target line grid, determine the wildfire risk level of the target line grid; The flame height risk determination module is used to obtain a number of line grids and a number of non-line grids at a preset distance from the target line grid, and set the obtained grids and the target line grid as influencing grids; obtain the wildfire risk influencing factor of each influencing grid; calculate and generate the vegetation burning height of each influencing grid according to the wildfire risk influencing factor of each influencing grid; use the maximum value of the vegetation burning height in each influencing grid as the flame height of the target line grid; and generate the corresponding flame height risk level according to the flame height of the target line grid; The wildfire tripping risk determination module is used to determine the wildfire tripping risk of the target line grid according to the wildfire occurrence risk level and the flame height risk level of the target line grid.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for assessing the risk of local wildfire tripping of a transmission line as described in any one of claims 1 to 7 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the method for evaluating the risk of local wildfire tripping of a transmission line as described in any one of claims 1 to 7.

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