A power grid lightning stroke fault risk prediction and dynamic early warning method, system and device for artificial lightning suppression operation and a storage medium

Abstract

This invention provides a method, system, device, and storage medium for predicting and dynamically warning of power grid lightning fault risks in manual lightning suppression operations. The method includes: collecting multi-source data related to manual lightning suppression and fusing the data to obtain a multi-dimensional comprehensive dataset; calculating the lightning fault risk index for each tower or line segment within the target area in future time windows using a power grid equipment lightning vulnerability index model and a dynamic prediction model for lightning fault risks based on the multi-dimensional comprehensive dataset; and classifying warning levels based on the lightning fault risk index. This invention quantifies the multi-source data related to power grid equipment into the lightning resistance weakness of towers or lines through the power grid equipment lightning vulnerability index model, and further quantifies the lightning fault risk through the dynamic prediction model for lightning fault risks, thus solving the fundamental defect of traditional lightning warnings that "there is lightning but no power grid."

Description

Technical Field

[0001] This invention relates to the field of power grid disaster prevention and mitigation and weather modification technology, specifically to a method, system, equipment and storage medium for predicting and dynamically warning of power grid lightning fault risks for artificial lightning suppression operations. Background Technology

[0002] With the intensification of global climate change and the increasing frequency of severe convective weather events, lightning activity poses a serious threat to the safe operation of power grids. Statistics show that lightning-induced power outages are one of the leading causes of transmission line failures. To reduce the risk of lightning damage to power grid equipment at its source, artificial lightning suppression technology is being explored for application in power grid lightning protection—by seeding high-performance suppression materials into severe convective thunderstorm clouds, it is hoped that the intensity of lightning activity can be suppressed or weakened.

[0003] However, manual lightning suppression is a complex engineering task that is costly, time-sensitive, and involves multi-departmental collaboration. Before implementation, a key decision-making question urgently needs to be answered: Has the risk of lightning strikes in the target power grid area reached the threshold requiring manual intervention in the current and next few hours? The existing technology has the following shortcomings in this decision-making process: (1) Existing lightning warning systems are geared towards general disaster prevention and lack a perspective on power grid equipment. Currently, the lightning warning systems deployed by meteorological departments and power grid companies typically provide regional "lightning occurrence probability" or "lightning density predictions." The warning targets are lightning activity itself, rather than the disaster risk posed by lightning activity to specific power grid equipment. The same lightning activity can have vastly different disaster risks on different lines—for example, with the same ground flash density, the risk of lightning tripping can differ by several times between a ±800kV UHVDC line with an insulation level as high as 1950kV and a 220kV line with an insulation level of only 950kV—and existing warning systems cannot reflect this difference. The essence of this fundamental defect is that existing technology equates "lightning activity warning" with "power grid lightning disaster warning," ignoring the crucial intermediate variable of equipment vulnerability that must be passed between "meteorological disaster factors" and "power grid equipment failure."

[0004] (2) Lack of lightning fault risk prediction model that integrates the vulnerability of power grid equipment. Existing lightning warning systems do not incorporate the differentiated characteristics of power grid equipment (line voltage level, insulation level, tower grounding resistance, lightning protection angle, terrain conditions, historical tripping records, etc.) into the risk prediction framework, resulting in the warning results being unable to distinguish between "high risk for ordinary lines" and "high risk for UHV lines", and failing to meet the refined needs of manual lightning suppression operation decision-making.

[0005] (3) Lack of a "risk-intervention" triggering threshold system for human intervention decision-making. Even if the predicted value of lightning fault risk at the power grid equipment level can be obtained, the existing technology has not established a standardized judgment rule for "when the predicted risk value reaches what level should manual lightning suppression operations be triggered". Whether or not to start manual lightning suppression operations still mainly depends on experience judgment, and lacks quantifiable and replicable scientific decision-making basis.

[0006] Therefore, there is an urgent need for a method for predicting and dynamically warning of power grid lightning fault risks in the context of manual lightning suppression operations. This method should be able to deeply integrate lightning nowcast data with power grid equipment vulnerability models, output dynamic early warning levels of equipment-level lightning fault risks, and determine whether to trigger manual lightning suppression operations. Summary of the Invention

[0007] To address the shortcomings of existing lightning warning technologies, such as the lack of a power grid equipment perspective, the absence of integrated equipment vulnerability models, and the lack of a trigger threshold system for manual intervention, this invention proposes a method for predicting and dynamically warning of power grid lightning fault risks for manual lightning suppression operations, comprising: Collect and fuse multi-source data related to artificial lightning suppression to obtain a multi-dimensional comprehensive dataset; Based on the multi-dimensional comprehensive dataset, the lightning fault risk index of each tower or line segment in the target area is calculated in each future time window using the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model. Early warning levels are determined based on the aforementioned lightning strike risk index; The power grid equipment lightning vulnerability index model integrates multi-dimensional data to quantify the lightning resistance weakness of towers or lines; the lightning fault risk dynamic prediction model quantifies the lightning fault risk based on the lightning resistance weakness.

[0008] Preferably, the step of collecting and fusing multi-source data related to artificial lightning suppression to obtain a multi-dimensional comprehensive dataset includes: Collect lightning nowcast data, atmospheric environmental parameters, power grid equipment characteristic data, and topographic data as multi-source data related to artificial lightning suppression; Based on the geographic information system platform, the multi-source data related to artificial mine suppression are unified under the same geographic coordinate system, spatially aggregated according to grid cells of preset size, time-aligned according to preset time steps, and synchronized using time interpolation methods for multi-source data related to artificial mine suppression with inconsistent time resolutions. For multi-source data related to artificial mine suppression with inconsistent spatial resolutions, inverse distance weighted interpolation or kriging interpolation methods are used to unify them to the target grid cells, resulting in a multi-dimensional comprehensive dataset.

[0009] Preferably, the step of calculating the lightning fault risk index for each tower or line segment within the target area in future time windows using the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model based on the multi-dimensional comprehensive dataset includes: Based on the aforementioned multi-dimensional comprehensive dataset, a comprehensive lightning vulnerability index is calculated using the power grid equipment lightning vulnerability index model; Based on lightning nowcast data and comprehensive lightning vulnerability index from multi-source data related to artificial lightning suppression, a dynamic prediction model for lightning fault risk is used to calculate the lightning fault risk index of each tower or line segment in the target area in future time windows.

[0010] Preferably, the formula for calculating the comprehensive lightning vulnerability index is as follows:

[0011] In the formula, To comprehensively assess the vulnerability index to lightning strikes, It is a sub-index of insulation vulnerability. for The weight, For grounding vulnerability sub-index, for The weight, To mask the vulnerability sub-index, for The weight, For terrain vulnerability sub-index, for The weight, For historical failure vulnerability sub-index, for The weights; The insulation vulnerability sub-index The calculation formula is as follows:

[0012] In the formula, The lightning impulse withstand voltage of the insulator string. Reference voltage; The grounding vulnerability sub-index The calculation formula is as follows:

[0013] In the formula, This is the measured value of the tower grounding resistance. Reference grounding resistance; The shielding vulnerability sub-index The calculation formula is as follows:

[0014] In the formula, For the protection angle of the lightning conductor, For reference protection angle; The historical failure vulnerability sub-index The calculation formula is as follows:

[0015] In the formula, This refers to the cumulative number of lightning-induced power outages for that tower or line section within a preset time period over the past year. This represents the historical fault weighting coefficient.

[0016] Preferably, the process of obtaining the terrain vulnerability sub-index includes: Based on the digital elevation model in the topographic data, the topographic relief and the relative elevation of the tower are calculated within a preset radius centered on each tower. Based on the terrain undulation and the relative elevation of the towers, the terrain type is automatically determined and the corresponding terrain vulnerability sub-index is matched using a predefined classification function.

[0017] Preferably, the formula for calculating the lightning strike risk index is as follows:

[0018] In the formula, for Time Grid Lightning strike risk index for poles or line sections; for Time Grid Predicted lightning density at the location; For grid The probability factor of lightning striking the line at that location; For grid The comprehensive lightning vulnerability index at the location; for Time Grid The time adjustment factor at the location.

[0019] Preferably, the step of classifying the early warning level based on the lightning strike fault risk index includes: If the lightning strike risk index is greater than or equal to the first grade threshold, the warning level is Level I; If the lightning strike failure risk index is greater than or equal to the second-level threshold and less than the first-level threshold, the warning level is Level II; If the lightning strike risk index is greater than or equal to the third-level threshold and less than the second-level threshold, the warning level is Level III. If the lightning strike risk index is less than the third-level threshold, the warning level is Level IV; The values ​​of the first, second, and third graded thresholds decrease sequentially; the values ​​of the first, second, and third graded thresholds are calibrated based on the statistical distribution of historical lightning tripping events.

[0020] Preferably, after classifying the early warning level based on the lightning strike fault risk index, the method further includes: Based on the aforementioned warning level and combined with auxiliary judgment conditions, a comprehensive suggestion for triggering manual lightning suppression operations is given. The warning level results and suggestions for triggering manual lightning suppression operations will be displayed on the geographic information system.

[0021] Preferably, the step of providing a comprehensive suggestion for triggering manual lightning suppression operations based on the warning level and auxiliary judgment conditions includes: When the total length of the towers or line segments covered by Level I and / or Level II early warning exceeds the preset length threshold, it is determined that the spatial coverage condition is met. When the duration of a Level I and / or Level II warning exceeds a preset time threshold, the time duration condition is deemed to be met. When atmospheric environmental parameters meet the intervention standards, the conditions for interventionability are deemed met. If the spatial coverage condition, the temporal continuity condition, and the interventionability condition are met simultaneously, and there is a Level I warning area, then an immediate operation instruction will be triggered. If the spatial coverage condition is met but the time continuity condition is not met, and the Level II warning area exceeds the preset proportion, then the preparation operation instruction will be triggered. If only a Level III warning exists, a continuous monitoring instruction will be triggered; If only a Level IV warning exists, an "No Work Required" instruction will be triggered.

[0022] Based on the same inventive concept, this invention also provides a power grid lightning fault risk prediction and dynamic early warning system for manual lightning suppression operations, including: a multi-source data acquisition and fusion module, a lightning fault risk dynamic prediction module, and a dynamic early warning level classification module. The multi-source data acquisition and fusion module is used to collect multi-source data related to artificial mine suppression and perform data fusion to obtain a multi-dimensional comprehensive dataset. The lightning fault risk dynamic prediction module is used to calculate the lightning fault risk index of each tower or line segment in the target area in each future time window based on the multi-dimensional comprehensive dataset using the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model. The dynamic early warning level classification module is used to classify early warning levels based on the lightning strike fault risk index; The power grid equipment lightning vulnerability index model integrates multi-dimensional data to quantify the lightning resistance weakness of towers or lines; the lightning fault risk dynamic prediction model quantifies the lightning fault risk based on the lightning resistance weakness.

[0023] Preferably, the multi-source data acquisition and fusion module is specifically used for: Collect lightning nowcast data, atmospheric environmental parameters, power grid equipment characteristic data, and topographic data as multi-source data related to artificial lightning suppression; Based on the geographic information system platform, the multi-source data related to artificial mine suppression are unified under the same geographic coordinate system, spatially aggregated according to grid cells of preset size, time-aligned according to preset time steps, and synchronized using time interpolation methods for multi-source data related to artificial mine suppression with inconsistent time resolutions. For multi-source data related to artificial mine suppression with inconsistent spatial resolutions, inverse distance weighted interpolation or kriging interpolation methods are used to unify them to the target grid cells, resulting in a multi-dimensional comprehensive dataset.

[0024] Preferably, the dynamic prediction module for lightning strike fault risk is specifically used for: Based on the aforementioned multi-dimensional comprehensive dataset, a comprehensive lightning vulnerability index is calculated using the power grid equipment lightning vulnerability index model; Based on lightning nowcast data and comprehensive lightning vulnerability index from multi-source data related to artificial lightning suppression, a dynamic prediction model for lightning fault risk is used to calculate the lightning fault risk index of each tower or line segment in the target area in future time windows.

[0025] Preferably, the formula for calculating the comprehensive lightning vulnerability index in the dynamic prediction module for lightning strike failure risk is as follows:

[0026] In the formula, To comprehensively assess the vulnerability index to lightning strikes, It is a sub-index of insulation vulnerability. for The weight, For grounding vulnerability sub-index, for The weight, To mask the vulnerability sub-index, for The weight, For terrain vulnerability sub-index, for The weight, For historical failure vulnerability sub-index, for The weights; The insulation vulnerability sub-index The calculation formula is as follows:

[0027] In the formula, The lightning impulse withstand voltage of the insulator string. Reference voltage; The grounding vulnerability sub-index The calculation formula is as follows:

[0028] In the formula, This is the measured value of the tower grounding resistance. Reference grounding resistance; The shielding vulnerability sub-index The calculation formula is as follows:

[0029] In the formula, For the protection angle of the lightning conductor, For reference protection angle; The historical failure vulnerability sub-index The calculation formula is as follows:

[0030] In the formula, This refers to the cumulative number of lightning-induced power outages for that tower or line section within a preset time period over the past year. This represents the historical fault weighting coefficient.

[0031] Preferably, the process of obtaining the terrain vulnerability sub-index in the dynamic prediction module for lightning strike failure risk includes: Based on the digital elevation model in the topographic data, the topographic relief and the relative elevation of the tower are calculated within a preset radius centered on each tower. Based on the terrain undulation and the relative elevation of the towers, the terrain type is automatically determined and the corresponding terrain vulnerability sub-index is matched using a predefined classification function.

[0032] Preferably, the calculation formula for the lightning fault risk index in the dynamic prediction module for lightning fault risk is as follows:

[0033] In the formula, for Time Grid Lightning strike risk index for poles or line sections; for Time Grid Predicted lightning density at the location; For grid The probability factor of lightning striking the line at that location; For grid The comprehensive lightning vulnerability index at the location; for Time Grid The time adjustment factor at the location.

[0034] Preferably, the dynamic early warning level classification module is specifically used for: If the lightning strike risk index is greater than or equal to the first grade threshold, the warning level is Level I; If the lightning strike failure risk index is greater than or equal to the second-level threshold and less than the first-level threshold, the warning level is Level II; If the lightning strike risk index is greater than or equal to the third-level threshold and less than the second-level threshold, the warning level is Level III. If the lightning strike risk index is less than the third-level threshold, the warning level is Level IV; The values ​​of the first, second, and third graded thresholds decrease sequentially; the values ​​of the first, second, and third graded thresholds are calibrated based on the statistical distribution of historical lightning tripping events.

[0035] Preferably, it also includes: a manual lightning suppression operation trigger determination module and a visualization and output module; The manual lightning suppression operation triggering determination module is used to provide a comprehensive suggestion for triggering manual lightning suppression operations based on the warning level and auxiliary determination conditions. The visualization and output module is used to display the warning level results and suggestions for triggering manual lightning suppression operations on the geographic information system.

[0036] Preferably, the manual lightning suppression operation triggering determination module is specifically used for: When the total length of the towers or line segments covered by Level I and / or Level II early warning exceeds the preset length threshold, it is determined that the spatial coverage condition is met. When the duration of a Level I and / or Level II warning exceeds a preset time threshold, the time duration condition is deemed to be met. When atmospheric environmental parameters meet the intervention standards, the conditions for interventionability are deemed met. If the spatial coverage condition, the temporal continuity condition, and the interventionability condition are met simultaneously, and there is a Level I warning area, then an immediate operation instruction will be triggered. If the spatial coverage condition is met but the time continuity condition is not met, and the Level II warning area exceeds the preset proportion, then the preparation operation instruction will be triggered. If only a Level III warning exists, a continuous monitoring instruction will be triggered; If only a Level IV warning exists, an "No Work Required" instruction will be triggered.

[0037] In another aspect, the present invention also provides an electronic device, comprising: at least one processor and a memory; The memory is used to store one or more programs; When the one or more programs are executed by the one or more processors, a method for predicting and dynamically warning of power grid lightning strike fault risks, as described above, is implemented.

[0038] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the above-described method for predicting and dynamically warning of power grid lightning strike fault risks.

[0039] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method, system, equipment, and storage medium for predicting and dynamically warning of power grid lightning fault risks in manual lightning suppression operations. The method includes: collecting multi-source data related to manual lightning suppression and fusing the data to obtain a multi-dimensional comprehensive dataset; calculating the lightning fault risk index for each tower or line segment within a target area in future time windows using a power grid equipment lightning vulnerability index model and a dynamic prediction model for lightning fault risks based on the multi-dimensional comprehensive dataset; and classifying warning levels based on the lightning fault risk index. Specifically, the power grid equipment lightning vulnerability index model integrates multi-dimensional data to quantify the lightning resistance weakness of towers or lines; the dynamic prediction model for lightning fault risks quantifies the lightning fault risk based on the degree of lightning resistance weakness. This invention quantifies multi-source data related to power grid equipment into the lightning resistance weakness of towers or lines through the power grid equipment lightning vulnerability index model, and further quantifies the lightning fault risk through the dynamic prediction model for lightning fault risks, thus solving the fundamental defect of traditional lightning warnings that "there is lightning but no power grid." The essence of this leap is that a quantitative transmission mechanism for equipment vulnerability, a key intermediate variable, is established between "meteorological disaster-causing factors" and "power grid equipment failures." Attached Figure Description

[0040] Figure 1 A flowchart of a method for predicting and dynamically warning of power grid lightning fault risks for manual lightning suppression operations provided by the present invention; Figure 2 A flowchart illustrating a specific example of a method for predicting and dynamically warning of power grid lightning fault risks for manual lightning suppression operations provided by this invention. Figure 3 The power grid equipment lightning vulnerability index provided by this invention V A schematic diagram of its structure; Figure 4 The lightning strike failure risk index provided by this invention A diagram illustrating the four-level early warning system; Figure 5 The flowchart of the triggering and determination logic for manual lightning suppression operations provided by this invention; Figure 6 A schematic diagram of a power grid lightning fault risk prediction and dynamic early warning system for manual lightning suppression operations provided by the present invention; Figure 7 This is a schematic diagram of an electronic device structure provided by the present invention. Detailed Implementation

[0041] This invention proposes a method, system, device, and storage medium for predicting and dynamically warning of power grid lightning fault risks for manual lightning suppression operations. It aims to solve the problems in existing technologies such as the lack of a power grid equipment perspective in lightning early warning, the lack of integration of equipment vulnerability models, and the lack of a manual intervention trigger threshold system. It realizes full-process quantitative decision-making from "lightning proximity forecast" to "equipment-level lightning fault risk prediction" and then to "manual lightning suppression operation trigger determination".

[0042] Example 1: A method for predicting and dynamically warning of power grid lightning fault risks for manual lightning suppression operations, such as... Figure 1 As shown, it includes: Step 1: Collect multi-source data related to artificial lightning suppression and perform data fusion to obtain a multi-dimensional comprehensive dataset; Step 2: Based on the multi-dimensional comprehensive dataset, use the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model to calculate the lightning fault risk index of each tower or line segment in the target area in each future time window; Step 3: Classify early warning levels based on the lightning strike fault risk index; Among them, the power grid equipment lightning vulnerability index model integrates multi-dimensional data to quantify the lightning resistance weakness of towers or lines; the lightning fault risk dynamic prediction model quantifies the lightning fault risk based on the lightning resistance weakness.

[0043] A specific example of the process for a power grid lightning fault risk prediction and dynamic early warning method for manual lightning suppression operations is as follows: Figure 2 As shown.

[0044] Step 1 specifically includes: Step S101: Multi-source data acquisition and fusion The following multi-source data were collected and subjected to spatiotemporal matching and fusion processing: lightning nowcast data, atmospheric environmental parameters, power grid equipment characteristic data, and topographic data. Lightning nowcasting data includes lightning density predictions (unit: times / (km²·h)) for the next 0-3 hours with a spatial resolution of no less than 1km×1km, lightning activity area movement vectors (movement direction and speed), thunderstorm cloud top height predictions, and lightning type predictions (cloud-to-ground lightning ratio predictions). Atmospheric environmental parameters include convective available potential energy (CAPE) (unit: J / kg), vertical wind shear (unit: m / (s·km)), 0°C layer height (unit: m), and -10°C layer height (unit: m). Power grid equipment characteristic data: including transmission line ledger information within the target area (line voltage level, tower coordinates, insulator string parameters, lightning protection wire configuration, tower grounding resistance), substation / converter station equipment parameters, and historical lightning trip records (time, location, and fault type of each lightning trip in the past 3-5 years); Topographic data: including digital elevation model (DEM) with a spatial resolution of no less than 30m and soil resistivity distribution.

[0045] Spatiotemporal matching was performed on lightning nowcast data (10-minute temporal resolution, 1-km spatial resolution), atmospheric environmental parameters (sounding data, 12-hour temporal resolution), power grid equipment characteristic data (static parameters), and topographic data (DEM, 30-m spatial resolution). Based on a GIS (Geographic Information System) platform, the multi-source data were unified to the same geographic coordinate system, spatially aggregated using 1km×1km grid cells, and time-aligned using a 10-minute time step. For data sources with inconsistent temporal resolutions, temporal interpolation methods were used for synchronization; for data sources with inconsistent spatial resolutions, inverse distance weighted interpolation (IDW) or Kriging interpolation methods were used to unify them to the target grid, forming a multi-dimensional comprehensive dataset.

[0046] Step 2 specifically includes: Step S102: Construct a lightning strike vulnerability index model for power grid equipment Reference Figure 3 For each tower and each section of line within the target area, a differentiated lightning vulnerability index is established. . It is composed of the following five sub-indices weighted together: (a) Insulation vulnerability index : ,in Lightning impulse withstand voltage of insulator string (unit: kV). This is the reference voltage (1000kV can be used). The higher the value, the weaker the insulation, and the higher the probability of flashover under the same lightning conditions.

[0047] Derived from measured data obtained from lightning impulse tests according to GB / T 16927.1 standard, the essence of this formula is "normalization of the actual withstand capability of the insulator relative to the reference value". The larger the value, the weaker the insulation; in power system lightning protection engineering, the core basis for insulation coordination is the insulator. This value is a common practice in standards such as IEC 60071 and GB / T 311.3.

[0048] (b) Grounding vulnerability sub-index : ,in The measured value of the tower grounding resistance (unit: Ω). The reference grounding resistance (can be taken as 10Ω). The higher the value, the higher the risk of a power outage.

[0049] Derived from "tower-by-tower data obtained through actual measurements using a grounding resistance tester," grounding resistance directly affects the probability of backflashover tripping—in the electrical geometric model (EGM), the backflashover probability is positively correlated with grounding resistance because the tower top potential ≈ ; =10Ω is the recommended limit for tower grounding resistance in DL / T 620 "Overvoltage Protection and Insulation Coordination of AC Electrical Installations".

[0050] (c) Masking vulnerability sub-index : ,in Lightning protection angle (unit: °). This is a reference protection angle (15° is acceptable). The higher the value, the higher the risk of tripping due to skewing.

[0051] These are engineering design parameters on the line design drawings, derived from "the protection angle data of each tower measured by laser point cloud scanning on the line design drawings." The relationship between the protection angle and the probability of backflashover has a mature theoretical description in the electrical geometric model—the larger the protection angle, the worse the shielding effect of the lightning protection wire on the conductor, and the higher the probability of backflashover. =15° is the upper limit of the protection angle recommended in IEEE Std 1243; (d) Topographic vulnerability sub-index The value is assigned based on the terrain features of the tower's location. The specific assignment rules are as follows: Based on the DEM (Digital Elevation Model), the terrain undulation (terrain undulation = highest point elevation - lowest point elevation) and relative elevation (tower elevation - surrounding average elevation) within a 500m radius around each tower are calculated. Then, a predefined classification function is used to automatically determine the terrain type and match the corresponding terrain type. value; Specifically: Based on the DEM digital elevation model (spatial resolution not less than 30m), the terrain undulation is calculated within a radius of 500m centered on each tower. ,in and These are the elevations of the highest and lowest points within the range, respectively, and the relative elevations of the towers. ,in The elevation of the tower. (The average elevation within this range). Based on the degree of undulation. and relative elevation The combination automatically determines the terrain type and matches it. Value: When and It was determined to be a mountain peak / ridge ( (Take 1.5~2.0); when and It was determined to be a hillside ( (Take 1.2~1.5); when It was determined to be a hilly area at the time. (Take 1.0~1.2); when It was determined to be a plain / valley ( (Values ​​range from 0.8 to 1.0). The vulnerability sub-indices for different terrains are shown in Table 1.

[0052] Table 1: Vulnerability sub-indices for different terrains

[0053] In practical applications, the terrain type around the tower can be automatically determined and the corresponding values ​​can be matched based on the DEM digital elevation model.

[0054] (e) Historical Failure Vulnerability Sub-Indices : ,in This refers to the cumulative number of power outages caused by lightning strikes over the past 3-5 years for this tower / line section. This is the historical fault weighting coefficient (0.5 is recommended). This formula ensures that tower locations with a history of lightning strikes receive a higher vulnerability assignment.

[0055] The data comes from the "correlation and matching data between the power grid fault recording system or lightning location system (LLS) and the line tripping records", which is the actual number of lightning trips of the tower in the past 3 to 5 years, and comes from the objective records of the power grid fault recording system. Using logarithmic function form nonlinear functions The reason is that the contribution of the number of historical trips to vulnerability is diminishing marginally—the first trip reveals far more information than the fifth. Comprehensive Lightning Vulnerability Index :

[0056] in, ~ These are the weighting coefficients for each sub-index. + + + + =1. The default weight is... =0.30、 =0.25、 =0.20、 =0.15、 =0.10, or can be calibrated based on actual operating experience.

[0057] Step S103: Construct a dynamic prediction model for lightning strike failure risk The lightning nowcast data obtained in step S101 is spatiotemporally coupled with the power grid equipment lightning vulnerability index constructed in step S102 to calculate the lightning fault risk index of each tower / section of line in the target area in each future time window. :

[0058] in: for t Time, Grid Lightning strike risk index for poles / line sections; for t Time, Grid Predicted lightning density at the location (unit: lightning / (km²·h)); For grid The probability factor of lightning striking a power line is related to the height of the power line above the ground and the prominence of the terrain. Mesh calculated for step S102 Comprehensive lightning strike vulnerability index; for t Time, Grid The time adjustment factor at that moment reflects the correction of the cloud-to-ground lightning ratio prediction to the actual ground lightning probability.

[0059] Step 3 specifically includes: Step S104: Classification of Dynamic Early Warning Levels for Lightning Strike Fault Risk Reference Figure 4 The lightning strike risk index calculated based on step S103 The risk of lightning strikes on each tower / line section within the target area is divided into four warning levels as shown in Table 2: Table 2: Classification of Warning Levels

[0060] in, , , The grading threshold is based on the time of occurrence of historical lightning strike tripping events. Statistical distribution determined — Take the time of occurrence of historical lightning strike trip events The 75th percentile of the value, Take the 50th percentile. Take the 25th percentile. This is based on historical lightning strike tripping events. The threshold calibration method of statistical distribution makes the warning level closely related to the actual disaster history of the power grid, rather than a fixed value preset by humans.

[0061] After step 3, the following also includes: Step S105: Triggering determination of manual lightning suppression operation Reference Figure 5 Based on the warning level in step S104 and combined with auxiliary judgment conditions, a comprehensive suggestion for triggering manual lightning suppression operations is given: (a) Spatial coverage determination: When the total length of the tower / line segment covered by Level I and / or Level II early warning exceeds the preset length threshold. When the coverage is 20km, the spatial coverage condition is considered met.

[0062] (b) Duration determination: When the duration of a Level I or Level II warning exceeds the preset time threshold. When the time duration is 30 minutes (i.e., 3 consecutive time steps), the time continuity condition is determined to be met.

[0063] (c) Weather system interventionability determination: When the convective effective potential energy (CAPE) is in the range of 500~3000 J / kg, the vertical wind shear is less than 20 m / (s·km), and the 0°C and -10°C layers are within the seedable range, the interventionability conditions are met.

[0064] (d) Comprehensive trigger decision: Triggering the "Immediate Operation" command: The following conditions must be met simultaneously: spatial coverage, temporal continuity, and operability; and a Level I warning area must exist. Trigger the "Prepare for Operation" command: if the spatial coverage condition is met but the time continuity condition is not yet met, or if the warning level is Level II. Triggering the "Continuous Monitoring" command: Only Level III warning exists; Triggering the "No Operation Required" command: Only Level IV warning is triggered.

[0065] Step S106: Output of early warning results The warning level results of step S104 and the trigger determination results of step S105 are displayed on the GIS map in a color-coded manner, with the lightning nowcast layer and thunderstorm cloud movement vector overlaid. The line sections that meet the intervention trigger conditions are highlighted, and a technical briefing including the trigger determination conclusion, warning coverage statistics, recommended operation timing, and operation area is output.

[0066] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention represents the first fundamental leap from "lightning activity prediction" to "equipment-level lightning fault risk prediction". It is the first invention to quantify the differentiated vulnerabilities of power grid equipment (insulation level, grounding resistance, protection angle, terrain, historical faults) into a comprehensive vulnerability index using a five-factor weighted model. Furthermore, by spatiotemporally coupling it with lightning nowcasting data, it solves the fundamental flaw of traditional lightning warnings that "there is lightning but no power grid." The essence of this leap is that it establishes a quantitative transmission mechanism for the key intermediate variable of equipment vulnerability between "meteorological disaster-causing factors" and "power grid equipment failure."

[0067] (2) Establish a four-level dynamic early warning system for manual lightning suppression operations and a threshold calibration system based on historical fault statistics. This invention establishes a level-I to IV early warning classification mechanism, with its level thresholds... , , It is not a fixed value preset by humans, but is based on the timing of historical lightning strike tripping events. Percentile statistical calibration of values—ensuring a close correlation between warning levels and the actual disaster history of the power grid. Based on this, and combining three auxiliary judgment conditions—spatial coverage, temporal duration, and weather system maneuverability—a standardized four-level trigger decision for manual lightning suppression operations is output for the first time: "Immediate Operation," "Prepare for Operation," "Continuous Monitoring," or "No Operation Required." This provides a quantifiable and replicable scientific basis for "when to intervene," addressing the long-standing industry pain point of relying on manual experience-based judgment.

[0068] (3) Deep fusion of multi-source heterogeneous data significantly improves prediction accuracy and relevance. This invention integrates five types of heterogeneous data: lightning nowcasting, atmospheric environmental parameters, power grid equipment characteristics, topography and geomorphology, and historical fault records. Compared with traditional methods that rely on only a single meteorological data, it achieves a refined prediction leap from "regional lightning probability" to "tower-level fault risk".

[0069] (4) The multi-source data fusion and automatic calculation scheme adopted in this invention avoids the subjectivity of relying on manual experience in traditional risk assessment, and improves the objectivity and reproducibility of the assessment results.

[0070] Example 2: The following is an implementation example of a method for predicting and dynamically warning of power grid lightning fault risks in manual lightning suppression operations.

[0071] Decision-making for artificial lightning suppression operations for ±800kV UHVDC transmission lines Scenario: A ±800kV UHVDC transmission line passes through a lightning-prone area in East China, with a total length of approximately 1200km and about 3200 towers. The average lightning trip rate for this line over the past three years is 1.85 times / (100km·a), translating to an estimated annual lightning trip rate of approximately 2.2 times / year. The method of this invention is deployed to dynamically predict the lightning fault risk of the target line and determine whether to trigger manual lightning suppression operations based on this prediction.

[0072] Step S101: Multi-source data acquisition and fusion Connect to the lightning nowcasting system to obtain the following forecast products: lightning density prediction, thunderstorm cloud movement vector (northwest → southeast, speed 32km / h), thunderstorm cloud top height prediction (12.5~15.8km), and cloud-to-ground lightning ratio prediction.

[0073] Atmospheric environmental parameters extracted from radiosonde data: CAPE = 1850 J / kg, vertical wind shear = 12 m / (s·km), 0℃ layer height = 4800 m, -10℃ layer height = 6500 m.

[0074] Line parameters were extracted from the power grid GIS ledger system: voltage level ±800kV, insulator string. =1950kV, total number of towers approximately 3200, tower grounding resistance 5~30Ω, lightning protection angle 8°.

[0075] Extract terrain data from SRTM 30m DEM.

[0076] Step S102: Calculate the lightning vulnerability index V for each tower. Taking tower No. 156 as an example, the basic parameters of the tower are first obtained.

[0077] Table 3: Foundation Parameters of the Tower

[0078] Then, five vulnerability sub-indices are calculated.

[0079] Table 4: Vulnerability Sub-Indices

[0080] Finally, a weighted composite vulnerability index is calculated. Using default weights ( =0.30, =0.25, =0.20, =0.15, =0.10).

[0081] Table 5: Values ​​of Sub-Indices and Weights

[0082] = 0.154 + 0.450 + 0.107 + 0.203 + 0.155 = 1.069 The tower The value is approximately 1.07, slightly higher than the benchmark value of 1.0. The main source of vulnerability is the high grounding resistance. =1.800) and historical lightning strike records ( =1.549), which is an object that needs to be closely monitored.

[0083] The above calculations were performed on each of the approximately 3,200 towers along the entire line to obtain the vulnerability distribution of the entire line.

[0084] Step S103: Calculate the lightning strike failure risk index

[0085] Taking the grid where tower number 45 is located at time T0 (14:30) as an example: =2.8 times / (km²·h), =0.72, =1.068, =0.85.

[0086] = 2.8 × 0.72 × 1.068 × 0.85 = 1.831.

[0087] Step S104: Dynamic Early Warning Level Classification Based on historical lightning strike tripping events Statistical distribution threshold setting: Historical data on the timing of each lightning-induced power outage on this line Value statistics: P25=0.35, P50=0.80, P75=1.50.

[0088] therefore: =1.50, =0.80, =0.35.

[0089] =1.831> → Classified as Level I (Red Alert).

[0090] Overall early warning statistics: Level I: 186 base stations (62km, 9.3%), Level II: 342 base stations (114km, 17.1%), Level III: 518 base stations (173km, 26.0%), Level IV: 2154 base stations (718km, 47.6%).

[0091] Step S105: Triggering determination of manual lightning suppression operation Spatial coverage: Level I+II coverage 176km> =20km → Satisfied.

[0092] Temporal continuity: The coverage length has shown an increasing trend over the past 3 steps (14:10~14:30) (98km→135km→176km) → Satisfies the requirement.

[0093] Interventionability: CAPE = 1850 J / kg (within the range of 500~3000), vertical wind shear = 12 m / (s·km) (<20), -10℃ layer = 6500 m (within the range of 5000~7500) → All conditions are met.

[0094] Comprehensive judgment: If the conditions of spatial coverage, temporal continuity and operability are met simultaneously, and there is a Level I warning area → trigger the "Immediate Operation" command.

[0095] Step S106: Output of early warning results Generate a GIS early warning map, highlight the Level I warning section as the priority intervention target area, and output a technical brief: the warning period is 14:30~17:30, the trigger decision is to immediately start manual lightning suppression operation, the recommended operation area is the section between towers No. 35 and No. 97, and the recommended operation time is within the next 30 minutes.

[0096] Example 3: Decision-making for manual lightning suppression operations targeting the 220kV regional backbone network Similar to Example 2, the main difference is: the line voltage level is 220kV. =950kV, protection angle 12°, overall vulnerability index is relatively high (1.1~1.8). Based on the historical lightning tripping events of this line. The statistical distribution calibration early warning threshold is =1.20. Under the same lightning imminent forecast conditions, 220kV lines are more likely to trigger "immediate operation" due to their lower insulation level and higher grounding resistance of some towers, which is consistent with the actual operation of the power grid—the risk of lightning tripping on 220kV lines deserves priority for manual intervention.

[0097] Example 4: Based on the same inventive concept, this invention also provides a power grid lightning fault risk prediction and dynamic early warning system for manual lightning suppression operations, such as... Figure 6 As shown, it includes: Multi-source data acquisition and fusion module, lightning strike fault risk dynamic prediction module, and dynamic early warning level classification module; The multi-source data acquisition and fusion module is used to collect multi-source data related to artificial mine suppression and perform data fusion to obtain a multi-dimensional comprehensive dataset. The lightning fault risk dynamic prediction module is used to calculate the lightning fault risk index of each tower or line segment in the target area in each future time window based on the multi-dimensional comprehensive dataset using the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model. The dynamic early warning level classification module is used to classify early warning levels based on the lightning strike fault risk index; The power grid equipment lightning vulnerability index model integrates multi-dimensional data to quantify the lightning resistance weakness of towers or lines; the lightning fault risk dynamic prediction model quantifies the lightning fault risk based on the lightning resistance weakness.

[0098] Preferably, the multi-source data acquisition and fusion module is specifically used for: Collect lightning nowcast data, atmospheric environmental parameters, power grid equipment characteristic data, and topographic data as multi-source data related to artificial lightning suppression; Based on the geographic information system platform, the multi-source data related to artificial mine suppression are unified under the same geographic coordinate system, spatially aggregated according to grid cells of preset size, time-aligned according to preset time steps, and synchronized using time interpolation methods for multi-source data related to artificial mine suppression with inconsistent time resolutions. For multi-source data related to artificial mine suppression with inconsistent spatial resolutions, inverse distance weighted interpolation or kriging interpolation methods are used to unify them to the target grid cells, resulting in a multi-dimensional comprehensive dataset.

[0099] Preferably, the dynamic prediction module for lightning strike fault risk is specifically used for: Based on the aforementioned multi-dimensional comprehensive dataset, a comprehensive lightning vulnerability index is calculated using the power grid equipment lightning vulnerability index model; Based on lightning nowcast data and comprehensive lightning vulnerability index from multi-source data related to artificial lightning suppression, a dynamic prediction model for lightning fault risk is used to calculate the lightning fault risk index of each tower or line segment in the target area in future time windows.

[0100] Preferably, the formula for calculating the comprehensive lightning vulnerability index in the dynamic prediction module for lightning strike failure risk is as follows:

[0101] In the formula, To comprehensively assess the vulnerability index to lightning strikes, It is a sub-index of insulation vulnerability. for The weight, For grounding vulnerability sub-index, for The weight, To mask the vulnerability sub-index, for The weight, For terrain vulnerability sub-index, for The weight, For historical failure vulnerability sub-index, for The weights; The insulation vulnerability sub-index The calculation formula is as follows:

[0102] In the formula, The lightning impulse withstand voltage of the insulator string. Reference voltage; The grounding vulnerability sub-index The calculation formula is as follows:

[0103] In the formula, This is the measured value of the tower grounding resistance. Reference grounding resistance; The shielding vulnerability sub-index The calculation formula is as follows:

[0104] In the formula, For the protection angle of the lightning conductor, For reference protection angle; The historical failure vulnerability sub-index The calculation formula is as follows:

[0105] In the formula, This refers to the cumulative number of lightning-induced power outages for that tower or line section within a preset time period over the past year. This represents the historical fault weighting coefficient.

[0106] Preferably, the process of obtaining the terrain vulnerability sub-index in the dynamic prediction module for lightning strike failure risk includes: Based on the digital elevation model in the topographic data, the topographic relief and the relative elevation of the tower are calculated within a preset radius centered on each tower. Based on the terrain undulation and the relative elevation of the towers, the terrain type is automatically determined and the corresponding terrain vulnerability sub-index is matched using a predefined classification function.

[0107] Preferably, the calculation formula for the lightning fault risk index in the dynamic prediction module for lightning fault risk is as follows:

[0108] In the formula, for Time Grid Lightning strike risk index for poles or line sections; for Time Grid Predicted lightning density at the location; For grid The probability factor of lightning striking the line at that location; For grid The comprehensive lightning vulnerability index at the location; for Time Grid The time adjustment factor at the location.

[0109] Preferably, the dynamic early warning level classification module is specifically used for: If the lightning strike risk index is greater than or equal to the first grade threshold, the warning level is Level I; If the lightning strike failure risk index is greater than or equal to the second-level threshold and less than the first-level threshold, the warning level is Level II; If the lightning strike risk index is greater than or equal to the third-level threshold and less than the second-level threshold, the warning level is Level III. If the lightning strike risk index is less than the third-level threshold, the warning level is Level IV; The values ​​of the first, second, and third graded thresholds decrease sequentially; the values ​​of the first, second, and third graded thresholds are calibrated based on the statistical distribution of historical lightning tripping events.

[0110] Preferably, it also includes: a manual lightning suppression operation trigger determination module and a visualization and output module; The manual lightning suppression operation triggering determination module is used to provide a comprehensive suggestion for triggering manual lightning suppression operations based on the warning level and auxiliary determination conditions. The visualization and output module is used to display the warning level results and suggestions for triggering manual lightning suppression operations on the geographic information system.

[0111] Preferably, the manual lightning suppression operation triggering determination module is specifically used for: When the total length of the towers or line segments covered by Level I and / or Level II early warning exceeds the preset length threshold, it is determined that the spatial coverage condition is met. When the duration of a Level I and / or Level II warning exceeds a preset time threshold, the time duration condition is deemed to be met. When atmospheric environmental parameters meet the intervention standards, the conditions for interventionability are deemed met. If the spatial coverage condition, the temporal continuity condition, and the interventionability condition are met simultaneously, and there is a Level I warning area, then an immediate operation instruction will be triggered. If the spatial coverage condition is met but the time continuity condition is not met, and the Level II warning area exceeds the preset proportion, then the preparation operation instruction will be triggered. If only a Level III warning exists, a continuous monitoring instruction will be triggered; If only a Level IV warning exists, an "No Work Required" instruction will be triggered.

[0112] Example 5: like Figure 7As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0113] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the power grid lightning fault risk prediction and dynamic early warning method for manual lightning suppression operations in the above embodiments.

[0114] Example 6: Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the above-described method for predicting and dynamically warning of power grid lightning strike fault risks for manual lightning suppression operations.

[0115] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0116] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0117] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0118] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0119] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for predicting and dynamically warning of power grid lightning fault risks in manual lightning suppression operations, characterized in that, include: Collect and fuse multi-source data related to artificial lightning suppression to obtain a multi-dimensional comprehensive dataset; Based on the multi-dimensional comprehensive dataset, the lightning fault risk index of each tower or line segment in the target area is calculated in each future time window using the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model. Early warning levels are determined based on the aforementioned lightning strike risk index; The lightning vulnerability index model for power grid equipment integrates multi-dimensional data to quantify the lightning resistance of towers or lines. The dynamic prediction model for lightning strike risk quantifies the risk of lightning strike based on the degree of lightning resistance weakness.

2. The method for predicting and dynamically warning of power grid lightning strike fault risks according to claim 1, characterized in that, The process involves collecting and fusing multi-source data related to artificial lightning suppression to obtain a multi-dimensional comprehensive dataset, including: Collect lightning nowcast data, atmospheric environmental parameters, power grid equipment characteristic data, and topographic data as multi-source data related to artificial lightning suppression; Based on the geographic information system platform, the multi-source data related to artificial mine suppression are unified under the same geographic coordinate system, spatially aggregated according to grid cells of preset size, time-aligned according to preset time steps, and synchronized using time interpolation methods for multi-source data related to artificial mine suppression with inconsistent time resolutions. For multi-source data related to artificial mine suppression with inconsistent spatial resolutions, inverse distance weighted interpolation or kriging interpolation methods are used to unify them to the target grid cells, resulting in a multi-dimensional comprehensive dataset.

3. The method for predicting and dynamically warning of power grid lightning strike fault risks according to claim 2, characterized in that, The calculation of the lightning fault risk index for each tower or line segment within the target area in future time windows, based on the multi-dimensional comprehensive dataset and utilizing the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model, includes: Based on the aforementioned multi-dimensional comprehensive dataset, a comprehensive lightning vulnerability index is calculated using the power grid equipment lightning vulnerability index model; Based on lightning nowcast data and comprehensive lightning vulnerability index from multi-source data related to artificial lightning suppression, a dynamic prediction model for lightning fault risk is used to calculate the lightning fault risk index of each tower or line segment in the target area in future time windows.

4. The method for predicting and dynamically warning of power grid lightning strike fault risks according to claim 3, characterized in that, The formula for calculating the comprehensive lightning vulnerability index is as follows: In the formula, To comprehensively assess the vulnerability index to lightning strikes, It is a sub-index of insulation vulnerability. for The weight, For grounding vulnerability sub-index, for The weight, To mask the vulnerability sub-index, for The weight, For terrain vulnerability sub-index, for The weight, For historical failure vulnerability sub-index, for The weights; The insulation vulnerability sub-index The calculation formula is as follows: In the formula, The lightning impulse withstand voltage of the insulator string. Reference voltage; The grounding vulnerability sub-index The calculation formula is as follows: In the formula, This is the measured value of the tower grounding resistance. Reference grounding resistance; The shielding vulnerability sub-index The calculation formula is as follows: In the formula, For the protection angle of the lightning conductor, For reference protection angle; The historical failure vulnerability sub-index The calculation formula is as follows: In the formula, This refers to the cumulative number of lightning-induced power outages for that tower or line section within a preset time period over the past year. This represents the historical fault weighting coefficient.

5. The method for predicting and dynamically warning of power grid lightning strike fault risks according to claim 4, characterized in that, The process of obtaining the terrain vulnerability sub-index includes: Based on the digital elevation model in the topographic data, the topographic relief and the relative elevation of the tower are calculated within a preset radius centered on each tower. Based on the terrain undulation and the relative elevation of the towers, the terrain type is automatically determined and the corresponding terrain vulnerability sub-index is matched using a predefined classification function.

6. The method for predicting and dynamically warning of power grid lightning strike fault risks according to claim 3, characterized in that, The formula for calculating the lightning strike risk index is as follows: In the formula, for Time Grid Lightning strike risk index for poles or line sections; for Time Grid Predicted lightning density at the location; For grid The probability factor of lightning striking the line at that location; For grid The comprehensive lightning vulnerability index at the location; for Time Grid The time adjustment factor at the location.

7. The method for predicting and dynamically warning of power grid lightning fault risks according to claim 1, characterized in that, The classification of early warning levels based on the lightning strike fault risk index includes: If the lightning strike risk index is greater than or equal to the first grade threshold, the warning level is Level I; If the lightning strike failure risk index is greater than or equal to the second-level threshold and less than the first-level threshold, the warning level is Level II; If the lightning strike risk index is greater than or equal to the third-level threshold and less than the second-level threshold, the warning level is Level III. If the lightning strike risk index is less than the third-level threshold, the warning level is Level IV; The values ​​of the first, second, and third graded thresholds decrease sequentially; the values ​​of the first, second, and third graded thresholds are calibrated based on the statistical distribution of historical lightning tripping events.

8. The method for predicting and dynamically warning of power grid lightning strike fault risks according to claim 7, characterized in that, After classifying the early warning level based on the lightning strike fault risk index, the method further includes: Based on the aforementioned warning level and combined with auxiliary judgment conditions, a comprehensive suggestion for triggering manual lightning suppression operations is given. The warning level results and suggestions for triggering manual lightning suppression operations will be displayed on the geographic information system.

9. The method for predicting and dynamically warning of power grid lightning fault risks according to claim 8, characterized in that, Based on the warning level and combined with auxiliary judgment conditions, a comprehensive suggestion for triggering manual lightning suppression operations is given, including: When the total length of the towers or line segments covered by Level I and / or Level II early warning exceeds the preset length threshold, it is determined that the spatial coverage condition is met. When the duration of a Level I and / or Level II warning exceeds a preset time threshold, the time duration condition is deemed to be met. When atmospheric environmental parameters meet the intervention standards, the conditions for interventionability are deemed met. If the spatial coverage condition, the temporal continuity condition, and the interventionability condition are met simultaneously, and there is a Level I warning area, then an immediate operation instruction will be triggered. If the spatial coverage condition is met but the time continuity condition is not met, and the Level II warning area exceeds the preset proportion, then the preparation operation instruction will be triggered. If only a Level III warning exists, a continuous monitoring instruction will be triggered; If only a Level IV warning exists, an "No Work Required" instruction will be triggered.

10. A power grid lightning fault risk prediction and dynamic early warning system for manual lightning suppression operations, characterized in that, include: Multi-source data acquisition and fusion module, lightning strike fault risk dynamic prediction module, and dynamic early warning level classification module; The multi-source data acquisition and fusion module is used to collect multi-source data related to artificial mine suppression and perform data fusion to obtain a multi-dimensional comprehensive dataset. The lightning fault risk dynamic prediction module is used to calculate the lightning fault risk index of each tower or line segment in the target area in each future time window based on the multi-dimensional comprehensive dataset using the power grid equipment lightning vulnerability index model and the lightning fault risk dynamic prediction model. The dynamic early warning level classification module is used to classify early warning levels based on the lightning strike fault risk index; The lightning vulnerability index model for power grid equipment integrates multi-dimensional data to quantify the lightning resistance of towers or lines. The dynamic prediction model for lightning strike risk quantifies the risk of lightning strike based on the degree of lightning resistance weakness.

11. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 10, characterized in that, The multi-source data acquisition and fusion module is specifically used for: Collect lightning nowcast data, atmospheric environmental parameters, power grid equipment characteristic data, and topographic data as multi-source data related to artificial lightning suppression; Based on the geographic information system platform, the multi-source data related to artificial mine suppression are unified under the same geographic coordinate system, spatially aggregated according to grid cells of preset size, time-aligned according to preset time steps, and synchronized using time interpolation methods for multi-source data related to artificial mine suppression with inconsistent time resolutions. For multi-source data related to artificial mine suppression with inconsistent spatial resolutions, inverse distance weighted interpolation or kriging interpolation methods are used to unify them to the target grid cells, resulting in a multi-dimensional comprehensive dataset.

12. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 11, characterized in that, The dynamic prediction module for lightning strike failure risk is specifically used for: Based on the aforementioned multi-dimensional comprehensive dataset, a comprehensive lightning vulnerability index is calculated using the power grid equipment lightning vulnerability index model; Based on lightning nowcast data and comprehensive lightning vulnerability index from multi-source data related to artificial lightning suppression, a dynamic prediction model for lightning fault risk is used to calculate the lightning fault risk index of each tower or line segment in the target area in future time windows.

13. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 12, characterized in that, The formula for calculating the comprehensive lightning vulnerability index in the dynamic prediction module for lightning strike failure risk is as follows: In the formula, To comprehensively assess the vulnerability index to lightning strikes, It is a sub-index of insulation vulnerability. for The weight, For grounding vulnerability sub-index, for The weight, To mask the vulnerability sub-index, for The weight, For terrain vulnerability sub-index, for The weight, For historical failure vulnerability sub-index, for The weights; The insulation vulnerability sub-index The calculation formula is as follows: In the formula, The lightning impulse withstand voltage of the insulator string. Reference voltage; The grounding vulnerability sub-index The calculation formula is as follows: In the formula, This is the measured value of the tower grounding resistance. Reference grounding resistance; The shielding vulnerability sub-index The calculation formula is as follows: In the formula, For the protection angle of the lightning conductor, For reference protection angle; The historical failure vulnerability sub-index The calculation formula is as follows: In the formula, This refers to the cumulative number of lightning-induced power outages for that tower or line section within a preset time period over the past year. This represents the historical fault weighting coefficient.

14. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 13, characterized in that, The process of obtaining the terrain vulnerability sub-index in the dynamic prediction module for lightning strike failure risk includes: Based on the digital elevation model in the topographic data, the topographic relief and the relative elevation of the tower are calculated within a preset radius centered on each tower. Based on the terrain undulation and the relative elevation of the towers, the terrain type is automatically determined and the corresponding terrain vulnerability sub-index is matched using a predefined classification function.

15. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 12, characterized in that, The formula for calculating the lightning fault risk index in the dynamic prediction module for lightning fault risk is as follows: In the formula, for Time Grid Lightning strike risk index for poles or line sections; for Time Grid Predicted lightning density at the location; For grid The probability factor of lightning striking the line at that location; For grid The comprehensive lightning vulnerability index at the location; for Time Grid The time adjustment factor at the location.

16. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 10, characterized in that, The dynamic early warning level classification module is specifically used for: If the lightning strike risk index is greater than or equal to the first grade threshold, the warning level is Level I; If the lightning strike failure risk index is greater than or equal to the second-level threshold and less than the first-level threshold, the warning level is Level II; If the lightning strike risk index is greater than or equal to the third-level threshold and less than the second-level threshold, the warning level is Level III. If the lightning strike risk index is less than the third-level threshold, the warning level is Level IV; The values ​​of the first, second, and third graded thresholds decrease sequentially; the values ​​of the first, second, and third graded thresholds are calibrated based on the statistical distribution of historical lightning tripping events.

17. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 16, characterized in that, Also includes: Manual lightning suppression operation trigger determination module and visualization and output module; The manual lightning suppression operation triggering determination module is used to provide a comprehensive suggestion for triggering manual lightning suppression operations based on the warning level and auxiliary determination conditions. The visualization and output module is used to display the warning level results and suggestions for triggering manual lightning suppression operations on the geographic information system.

18. The power grid lightning strike fault risk prediction and dynamic early warning system according to claim 17, characterized in that, The manual lightning suppression operation triggering determination module is specifically used for: When the total length of the towers or line segments covered by Level I and / or Level II early warning exceeds the preset length threshold, it is determined that the spatial coverage condition is met. When the duration of a Level I and / or Level II warning exceeds a preset time threshold, the time duration condition is deemed to be met. When atmospheric environmental parameters meet the intervention standards, the conditions for interventionability are deemed met. If the spatial coverage condition, the temporal continuity condition, and the interventionability condition are met simultaneously, and there is a Level I warning area, then an immediate operation instruction will be triggered. If the spatial coverage condition is met but the time continuity condition is not met, and the Level II warning area exceeds the preset proportion, then the preparation operation instruction will be triggered. If only a Level III warning exists, a continuous monitoring instruction will be triggered; If only a Level IV warning exists, an "No Work Required" instruction will be triggered.

19. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the method for predicting and dynamically warning of power grid lightning fault risks as described in any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium, characterized in that, It contains an execution program, which, when executed, implements the power grid lightning fault risk prediction and dynamic early warning method as described in any one of claims 1 to 9.

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