538 results about "Lightning" patented technology

The invention discloses a thunder and lightning early warning method based on atmospheric electric field networking monitoring. The method comprises the following steps: collecting multi-point electric field intensity data in a region in real time through a high-density electric field sensor network, and constructing an electric field intensity distribution matrix; for the constructed electric field intensity distribution matrix, space-time evolution characteristics of lightning activities are extracted, and lightning occurrence probability distribution is generated; acquiring temperature, humidity and air pressure parameters from multi-source meteorological data, performing data fusion with the generated thunder and lightning occurrence probability distribution, and constructing a comprehensive environment characteristic data set; and for the constructed comprehensive environment characteristic data set, calculating a comprehensive risk index, and if the index exceeds a preset thunder and lightning triggering threshold, etc. According to the lightning early warning method, the distribution matrix is constructed through the high-density electric field network, the spatial-temporal characteristics are extracted to generate the initial probability, and the physical rationality and the environment false alarm resistance of early risk identification are remarkably improved through fusion with the multi-source meteorological data.

The invention provides a lightning proximity prediction method based on a space-time separation convolutional neural network. The method comprises the following steps: S1, acquiring satellite infrared brightness temperature observation data and lightning positioning data; s2, performing spatial resolution adjustment and standardization processing on the satellite infrared brightness temperature observation data; gridding and de-noising processing is carried out on the lightning positioning data; s3, defining a prediction task; s4, establishing a space-time sequence prediction model; based on the processed data, storing the processed data in a numpy file format according to a time point sequence, dividing the processed data into a training set, a verification set and a test set, and loading the training set, the verification set and the test set to a space-time sequence prediction model for model training to obtain an optimization model; and based on the prediction task, using the optimization model to carry out lightning approaching prediction to obtain lightning prediction data. According to the method, the development of cumulus cloud is captured by using satellite infrared brightness temperature observation data and brightness temperature difference data, so that the problem that thunder and lightning inception cannot be effectively predicted is solved.

The invention relates to a lightning nowcasting method based on multi-source satellite data, and belongs to the technical field of lightning nowcasting. The method comprises the steps of obtaining and preprocessing satellite data of a multi-source channel and corresponding lightning data, constructing a U-Net lightning prediction model, collecting a historical multi-source satellite image sequence, constructing a PredRNN satellite image prediction model and predicting the lightning occurrence probability. According to the method, the PredRNN and the U-Net model are combined, the lightning distribution can be predicted at the future moment only by using the satellite image, the dependence on historical lightning data is broken through, and the method has a wide application prospect in the field of lightning nowcasting.

The invention provides a lightning early warning system for a port dangerous cargo major dangerous source tank field, and relates to the field of lightning early warning. The lightning early warning system uses a data processing technology based on artificial intelligence to carry out data set splitting on a time sequence data set of lightning characteristic parameters; performing relative position prediction based on time sequence characteristics on the time sequence data set of the split lightning position, and performing message constraint transfer based on the time sequence characteristics on the time sequence data set of the atmospheric electric field intensity value at the same time; therefore, the lightning early warning level is automatically determined according to the predicted value of the lightning relative position data and the atmospheric electric field intensity time sequence fluctuation coding characteristics after time sequence transmission. Therefore, by monitoring the change of the intensity of the atmospheric electric field in real time and accurately capturing the position and the moving path of the lightning, the occurrence time and the intensity of the lightning can be predicted more accurately, and then a reliable safety guarantee can be provided for a dangerous cargo tank field of a port.

The invention relates to an improved hail early warning method based on lightning jump, and the method comprises the steps: obtaining the two-dimensional lattice point data of a radar combined reflectivity factor CR and the two-dimensional lattice point data of a vertical integral liquid water content VIL based on the radar networking puzzle data of an early warning region; for the two-dimensional lattice point data of the radar combined reflectivity factor CR, utilizing a neighborhood method to obtain lattice point position information of a severe convective echo connected region; calculating a lightning time change rate D and a standard deviation sigma of the lightning time change rate based on the lightning data of the early warning area and the grid point position information of the strong convective echo communication area; based on the two-dimensional lattice point data of the vertical integral liquid water content VIL and the lattice point position information of the severe convective echo communication region, obtaining a jump increment GVIL of the vertical integral liquid water content VIL; based on the jump increment GVIL, the lightning time change rate D and the standard deviation sigma of the lightning time change rate, hail early warning conditions are judged, and hail early warning is achieved.

The invention provides a lightning frequency nowcasting method and system, and relates to the technical field of lightning nowcasting, and the method comprises the steps: obtaining the actual measurement data of severe convective weather of a target region in a current time period, the severe convective weather actual measurement data at least comprise weather radar echo actual measurement data, lightning frequency actual measurement data and stationary satellite multichannel actual measurement infrared brightness temperature; and based on a preset time forecasting interval, inputting the severe convective weather measured data into a multi-branch coding lightning frequency forecasting model to obtain weather radar echo forecasting data and lightning frequency forecasting data of the target area in the next time period output by the multi-branch coding lightning frequency forecasting model, the multi-branch coding lightning frequency forecasting model is obtained through training based on severe convection weather sample data marked with severe convection weather labels. According to the invention, lightning frequency forecasting is realized, and the accuracy of lightning forecasting is improved.

The invention discloses a tower disaster situation sensing and early warning system based on wireless communication, and relates to the technical field of tower disaster monitoring and early warning. The tower top monitoring module is integrated with a multi-dimensional sensor, adopts a wind-solar complementary power supply and self-calibration mechanism, and intelligently adjusts data acquisition; the timed inspection module optimizes an inspection path and identifies 10 types of defects by using an unmanned aerial vehicle carried laser radar and an AI model; the meteorological early warning module combines a micrometeorological model and a random forest algorithm to dynamically correct a threshold value, and associates multi-source data to trigger early warning; the self-protection module integrates lightning protection and block chain technologies to guarantee data security; according to the invention, the monitoring precision and the communication reliability are obviously improved, the early warning response time is shortened to be within 30 seconds, and the power grid disaster prevention capability is enhanced.

The invention relates to a lightning potential forecasting method and system based on a random forest. The method comprises the following steps: (1) collecting and preprocessing multi-source meteorological data; (2) feature extraction: cloud picture texture and shape features are extracted from satellite data, and echo intensity, top height and vertical development speed features are extracted from radar data; (3) constructing and optimizing a model; and (4) early warning thunder and lightning. The method has the advantages that the random forest serves as a typical representation of the ensemble learning algorithm, and the core of the random forest is to integrate prediction results of a plurality of decision trees. According to the integration mode based on Bagging, multi-dimensional feature information can be fully integrated, and the overfitting problem which is likely to happen to a single decision tree is effectively restrained. In a thunder and lightning potential forecasting scene, the algorithm can accurately capture a complex nonlinear relationship between meteorological elements and thunder and lightning potential by adaptively learning association between a large amount of historical meteorological data and thunder and lightning events, so that the forecasting accuracy is remarkably improved.

The invention discloses a lightning arrester on-line monitoring method and device, and relates to the field of intelligent monitoring, and the method comprises the steps: firstly collecting original current and voltage waveform data, carrying out the frequency domain decomposition operation, then introducing an intelligent prediction mechanism, carrying out the intelligent prediction of a phase error based on the fundamental voltage phase, temperature change and harmonic characteristics, and finally, carrying out the calculation of the phase error. And performing phase angle correction operation and preliminary calculation of resistive current on the fundamental current and voltage by using the corrected phase information, and introducing a historical data smoothing algorithm to perform noise reduction optimization on a result. And finally, comparing the analyzed and processed resistive current with a preset threshold value, and judging whether an alarm prompt is generated or not. Therefore, real-time evaluation and abnormal alarm of the operation state of the lightning arrester are realized, so that a powerful guarantee is provided for safe operation of a power system.

The invention provides a single-station lightning three-dimensional positioning system and method. The positioning system mainly comprises a direction detection unit, a distance detection unit, a GNSS time service unit, a positioning unit and a three-dimensional image generation unit. A plurality of VHF antennas, an electric field antenna and a magnetic field antenna synchronously collect lightning signals, the lightning signals are converted into digital signals through analog-to-digital conversion, and GNSS time service marks timestamps and stores the timestamps. The direction detection unit detects the time difference from the lightning radiation source to the VHF antenna, performs angle and time difference conversion, and calculates the elevation angle and azimuth angle of the lightning radiation source. And the distance detection unit performs fast Fourier transform on the acquired electric field signal and magnetic field signal, fits a ratio curve constant A and a ratio curve constant B, and calculates the distance between the lightning radiation source and the observation station. And matching the azimuth angle, the elevation angle and the distance information marked with the same timestamp, positioning the position of an instant lightning radiation source, and matching all positioning results of a continuous lightning event one by one according to the same timestamp to obtain a lightning three-dimensional graph in the space model.

The invention discloses a meteorological disaster early warning response method and system based on a meteorological early warning aircraft, and the system comprises a data collection module which is used for constructing a database containing the real-time meteorological data, the terrain data, the historical disaster data and the early warning aircraft operation data; the multi-disaster terrain adaptation analysis module is used for analyzing waterlogging and gale comprehensive risks by using a synchronous superposition model in plains and analyzing landslide and thunder-gale chain risks by using a time sequence chain model in mountainous areas through a double-branch fusion neural network; the early warning response strategy generation module is used for generating differentiation strategies according to regional risks and terrains; and the terminal interaction module pushes information and receives feedback. The system dynamically generates protection guidance for different geographic features such as a plain high-risk area and a mountainous area high-risk area through multi-source meteorological data integration and analysis, and improves the timeliness and coverage of early warning information.

The invention provides a time-space convolutional neural network lightning forecasting method based on double-source observation data. The method comprises the following steps: constructing a radar prediction network, constructing a double-source prediction network based on a convolutional neural network, preparing a data set, training the radar prediction network, training the double-source prediction network, performing double-source data acquisition and processing, and predicting future lightning by using the double-source prediction network. According to the method, the internal relation of the radar feature data and the lightning data in the space-time dimension is fully integrated, the defect that the traditional image channel layer direct fusion mode is poor in characterization capability on a complex meteorological scene is effectively overcome, and therefore a solid data basis is provided for more accurate meteorological prediction. Moreover, the time sequence convolutional neural network is adopted for lightning prediction, and the number of model parameters is effectively reduced while the time sequence data processing capability is maintained, so that the overhead of training and reasoning is remarkably reduced, and the meteorological condition can be predicted and evaluated more quickly and accurately.

The invention discloses a hail prediction method and device, a medium and a program product, and relates to the technical field of meteorology, and the method comprises the steps: obtaining a neural network model; acquiring historical lightning characteristic variable data and historical hail characteristic variable data; dividing the historical lightning characteristic variable data and the historical hail characteristic variable data into a training set and a verification set of corresponding characteristic variables; training the neural network model through the training set and the verification set of the corresponding feature variables, and taking the trained neural network model as a target neural network model; acquiring current lightning characteristic variable data; determining a dynamic weighting loss function according to the target neural network model; and predicting hail weather according to the current lightning characteristic variable data and the dynamic weighted loss function to obtain hail prediction parameters. According to the technology provided by the invention, misjudgment caused by forecasting the hail by independently using the radar reflectivity factor is reduced, and the hail forecasting precision is improved by indicating the factor through the lightning characteristic data.

The invention discloses a multi-source data fusion image sequence generation method and system based on space-time diffusion, and belongs to the technical field of image sequence generation, and the method comprises the steps: taking an existing satellite remote sensing image and an existing radar echo reflectivity image as input, taking an existing lightning drop point space distribution diagram as output, and training a synchronous mapping network; training a prediction network through the existing satellite remote sensing image and the existing radar echo reflectivity image to obtain a future satellite remote sensing image and a future radar echo reflectivity image; inputting a future satellite remote sensing image and a future radar echo reflectivity image into the trained synchronous mapping network to obtain a first prediction map of a future lightning drop point spatial distribution map; inputting the existing lightning drop point spatial distribution map into the space-time diffusion model to obtain a second prediction map of the future lightning drop point spatial distribution map; and the first prediction image and the second prediction image are spliced to obtain a final prediction image sequence of the future lightning drop point spatial distribution map, so that the prediction precision and robustness are remarkably improved.

The invention discloses a lightning disaster early warning method based on meteorological data. The method comprises the following steps: fusing meteorological data and topographic data, and generating a spatial-temporal distribution data set of atmospheric parameters and topographic features in a complex mountainous area environment through weighted stacking and spatial-temporal interpolation methods; analyzing an electromagnetic wave multi-path propagation effect according to the spatial-temporal distribution data set, calculating an attenuation coefficient with a high-frequency component greater than 1 gigahertz and a propagation loss with a low-frequency component less than 1 gigahertz, and generating a spectrum degradation preliminary quantization data set; and if a high-frequency component attenuation coefficient in the spectrum degradation preliminary quantization data set exceeds an attenuation threshold value, optimizing propagation path parameters through a genetic algorithm, generating a corrected spectrum degradation model and the like. The precision and the real-time performance of lightning disaster early warning in the complex mountainous area can be remarkably improved, and the disaster risk is effectively reduced.

The invention discloses a lightning approaching trend forecasting method and system. The method comprises the steps of obtaining cloud cover space distribution and a time sequence, extracting a first cloud cover texture feature vector, and generating a cloud cover hierarchical feature matrix; performing dimensionality reduction on the first cloud cover texture feature vector to generate a second cloud cover texture feature vector, and generating a regionalized cloud cover distribution feature set through clustering in combination with the cloud cover hierarchical feature matrix; extracting humidity, temperature and wind field data to generate a lightning trigger factor data set, and analyzing a regionalized cloud cover distribution feature set by adopting a time sequence analysis method and an optical flow method to form a cloud cover dynamic feature set; and according to the cloud cover dynamic feature set and the cloud cover hierarchical feature matrix, in combination with a pre-established lightning association cloud type label, generating a lightning intensity grade classification feature set and the like. According to the invention, through deep fusion and dynamic optimization of spatial-temporal characteristics, the precision and timeliness of lightning prediction are significantly improved, and efficient technical support is provided for meteorological disaster early warning.

The invention provides a meteorological station power supply line lightning stroke fault positioning method, system and device and a medium. The method comprises the following steps: firstly, synchronously acquiring transient voltage and current traveling waves based on a preset multi-stage lightning protection area boundary to obtain multi-source transient data; performing cross-measuring-point fusion reconstruction on the acquired multi-source transient data, and marking waveform characteristics of the boundary of the protection area to obtain effective waveform data; then, calculating dynamic wave impedance, and determining a wave impedance break variable in combination with a boundary impedance calibration value; and through the reconstructed effective voltage waveform data or effective current waveform data, the traveling wave arrival time is calibrated by adopting a frequency-variable wave velocity model and wavelet transform. And finally, determining the positioning value of the lightning stroke fault at the access point of the meteorological station terminal equipment according to the frequency-varying wave velocity, the calibrated traveling wave arrival time and the wave impedance break variable, and outputting the position information of the lightning stroke fault in the multi-stage lightning protection area in combination with a preset multi-stage lightning protection area boundary position mapping table. According to the scheme, the lightning stroke fault can be accurately positioned in the multi-stage lightning protection area.

The invention provides a lightning activity prediction method based on Doppler radar data and lightning activity correlation analysis, and relates to the field of lightning early warning. The method comprises the steps of performing normalization processing on Doppler radar data, and calculating radar centroid trajectory data based on the clustered Doppler radar data; synchronously extracting the ground-to-ground lightning data according to the radar timestamp, and generating a ground-to-ground lightning centroid track based on the clustered ground-to-ground lightning data; calculating a trajectory position similarity value and a trajectory direction similarity value under different combined reflectivity; and carrying out correlation comparison analysis on the trajectory position similarity value and the trajectory direction similarity value corresponding to each combined reflectivity, and selecting a target combined reflectivity meeting a preset comparison condition in the combined reflectivity to carry out lightning activity trajectory prediction. According to the invention, the problem that the lightning activity prediction accuracy is low due to the fact that the traditional lightning early warning technology often has false alarm and missing alarm under the condition of complex topographic relief or multi-series convection superposition is solved.

The utility model discloses a lightning protection circuit and a power converter, and relates to the technical field of power electronics. According to the lightning protection circuit, for the condition that a capacitor parallel branch in a bus capacitor circuit comprises a first capacitor, a second capacitor and an inductor, and the first capacitor is connected with the inductor in series and then connected with the second capacitor in parallel, a lightning protection device is arranged to be connected with the inductor in parallel; in the lightning stroke process, the voltage at the two ends of the inductor exceeds the action voltage of the lightning protection device, so that the lightning protection device is broken down, and the voltage at the two ends of the inductor is clamped at the voltage drop after the lightning protection device is broken down, so that overlarge voltage cannot be formed on the second capacitor; therefore, the problem of voltage oscillation caused by current backflow at the lightning stroke moment can be weakened, the voltage stress borne by a corresponding power device in a post-stage circuit is reduced, and the corresponding power device is effectively protected.

The invention provides a severe convective weather early warning method and system based on monomer identification and lightning jump, and is applied to the technical field of data processing. The method comprises the following steps: processing high-precision lightning positioning data and meteorological radar data to generate convection monomer characteristic parameter information; processing the ground observation data to generate ground meteorological element associated information; processing the convective monomer characteristic parameter information and the ground meteorological element association information to generate severe convective monomer identification result information; processing the high-precision lightning positioning data based on the strong convection monomer identification result information to generate lightning jump judgment information; and processing the severe convection monomer identification result information and the lightning jump judgment information based on the target severe convection weather early warning model to generate severe convection weather early warning result information.

The invention relates to a lightning trip risk assessment method and system for a current collection line of a wind power plant station, and belongs to the technical field of lightning protection of a power system, and the method comprises the following steps: collecting a multi-dimensional data set of a target wind power plant station, the multi-dimensional data set comprising lightning activity parameters, landform data, equipment structure parameters and historical lightning fault data; constructing a three-dimensional electromagnetic transient model of the current collection line, wherein the three-dimensional electromagnetic transient model of the current collection line integrates an electromagnetic transient program EMTP counterattack simulation model, a time domain finite difference method FDTD induction lightning shielding model and a terrain correction electrical geometric model EGM; calculating lightning trip-out rates based on the multi-dimensional data set and the three-dimensional electromagnetic transient model of the current collection line, wherein the lightning trip-out rates comprise an impact trip-out rate, a shielding failure trip-out rate and an induction lightning trip-out rate under fan shielding; and carrying out risk grading on the tower according to the lightning trip-out rate, establishing a terrain-tower type coupling risk assessment system, and outputting a risk assessment result.

The present invention belongs to the technical field of lightning protection of power transmission lines, and in particular, relates to a method for limiting lightning overvoltage of a direct-current power transmission line. By means of using operation date of a line itself, lightning current monitoring data, and parameters of an environment where the line is located, a line dynamic operation function curve and a line lightning-protection performance function curve are both corrected to obtain a more realistic voltage difference, thus achieving reliable protection actions and state sensing of a lightning arrester on the line. An apparatus for limiting a lightning overvoltage of a direct-current power transmission line is further provided in the present invention to cooperate with the implementation of the method.

The invention provides a lightning forecasting method and device based on multi-source data, electronic equipment and a medium, and relates to the field of lightning prediction.The method comprises the steps that satellite data and radar data of a target area in the current time period are acquired; performing interpolation processing on the radar data based on a preset time resolution and a preset spatial resolution to obtain target radar data; the trained lightning forecasting model is adopted to process satellite data and target radar data, and the probability of lightning occurrence in the future period is obtained; the lightning forecasting model is a multi-source data fusion model which is established on the basis of Lightning-Segnet-LSTM (Long Short Term Memory). According to the method, lightning identification is carried out on FY4B AGRI channel 9, channels 11-15 and radar data through Lightning-Segnet, and extrapolation forecasting of lightning is carried out in combination with a memory network LSTM.

The invention discloses a power distribution network line fault positioning method, device and equipment, and relates to the technical field of power distribution network line fault positioning, and the method comprises the steps: obtaining operation data and lightning ground lightning data; feature extraction is carried out on the operation data, spatial-temporal feature mapping is carried out on the lightning and ground lightning data, and multi-source feature parameters are generated; probabilistic reasoning is carried out on the input multi-source characteristic parameters through section agents deployed in a distributed mode and Bayesian network submodules of the section agents, and real-time fault probability values of all independent sections of the power distribution network are output; and taking the real-time fault probability value of each independent section as an input, dynamically coordinating an output result of each agent through a Nash equilibrium strategy, and generating a global consistency fault positioning decision. The method is used for solving the problem that a traditional line fault positioning method is difficult to effectively fuse heterogeneous data and is difficult to coordinate local judgment conflicts.

The invention relates to a multi-mode power transmission line state monitoring method and system based on thunder and shooting fusion, and belongs to the technical field of circuit monitoring. The method comprises the following steps: acquiring historical multi-mode power transmission line data, and constructing a lightning-shooting fusion power transmission line monitoring set according to the historical multi-mode power transmission line data; a power transmission line state monitoring model is obtained through multi-sensor fusion modeling according to the lightning shooting fusion power transmission line monitoring set; the real-time multi-mode power transmission line data is acquired, the real-time multi-mode power transmission line data is processed through the power transmission line state monitoring model to obtain the power transmission line state label, high-precision, all-weather and low-cost power transmission line state monitoring is achieved, and efficient and reliable technical guarantee is provided for safe operation of a power grid.

The utility model relates to a device for detecting lightning protection conductivity of an offshore wind turbine generator, which comprises a support rod and a resistance detector, one end of the support rod is connected with a tower drum, the other end of the support rod extends outwards and is provided with a conducting piece, and the conducting piece is positioned on the motion trail of a lightning arrester; and two terminals of the resistance detector are respectively connected with the conducting piece and the grounding wire through a first lead and a second lead. According to the utility model, the supporting rod extends out of the window in the tower drum, so that the conducting piece is positioned on the motion trail of the lightning arrester, and the resistance detector, the conducting piece, the blade, the cabin and the tower drum form a complete loop through the first lead and the second lead, so that the lightning protection conductivity detection of the blade of the wind turbine generator can be completed. The purposes of reducing the operation risk coefficient, shortening the detection period and improving the working efficiency are achieved.

The invention relates to the field of data processing, in particular to a method and system for analyzing the performance of a lightning protection device in a flammable and explosive place, and the method comprises the steps: firstly collecting resistance, temperature and humidity data in a preset number of days, and carrying out the preprocessing to construct a target resistance time sequence of each day; thirdly, clustering the days based on the day and night temperature difference, and dividing a plurality of periodic windows; secondly, decomposing the target resistance time sequence of all days, calculating the periodicity level and the abnormal degree of each layer, converting the periodicity level and the abnormal degree into weights, and performing weighted summarization to obtain a performance evaluation value; finally, if the value is smaller than or equal to a preset abnormal threshold value, it is judged that the device is abnormal. According to the method, the periodicity level and the abnormal degree of resistance change are comprehensively considered, the performance of the lightning protection grounding device can be comprehensively and accurately evaluated, potential abnormity can be found in time, and misjudgment or missed judgment caused by single-factor evaluation is avoided.

The invention discloses a radar reflectivity inversion method based on satellite infrared data and lightning observation data, which belongs to the technical field of meteorological monitoring, and comprises the following steps: taking a three-channel input image formed by the lightning observation data and two infrared images from different infrared channels as the input of a scattering model; the lightning activity is closely related to severe convective weather, so that the scattering model can be helped to perform radar reflectivity inversion in areas lacking radar data, such as an open sea, a plateau and a desert, and the defect of insufficient accurate modeling of strong weather events in the prior art is overcome; more comprehensive and multi-dimensional input information is provided for radar data reconstruction, and the accuracy of radar reflectivity inversion is remarkably improved.

The invention discloses a lightning fire occurrence risk grade forecasting method and device and a storage medium. Belongs to the technical field of forest fire prediction. The method comprises the following steps: acquiring hour-scale real-time data of a target area; extracting a real-time numerical value corresponding to each key factor in a predefined key factor set from the real-time data; inputting the real-time numerical values corresponding to all the key factors into a pre-trained risk level prediction model to obtain a lightning fire occurrence probability corresponding to each lightning in the target area; and according to the occurrence probability of the lightning fire, determining the risk level of the lightning fire caused by each lightning in the target area. Therefore, scientific screening of key factors of lightning fire forecasting is realized, hour-scale real-time data acquisition is adopted, and data processing and model forecasting logic adapted to hour-scale dynamic change are adopted, so that forecasting accuracy and real-time performance are effectively improved.

The invention is suitable for the technical field of distribution network risk assessment, and provides a distribution line risk assessment method and system based on a lightning and associated disaster coupling probability model, and the method comprises the steps: obtaining meteorological data, geographic data and distribution line parameters of a target region; calculating a first fault probability of the lightning disaster lower line section or the tower and a second fault probability of the associated disaster lower line section or the tower; combining the disaster type conversion probability and the disaster accompanying time coefficient, determining the third fault probability of the lower segment or the tower of the comprehensive disaster, calculating the failure probability of the distribution line, and screening a typical fault scene set by using the system information entropy; calculating a line fault consequence value, and generating a line risk index in combination with the failure probability; and finally, generating a target risk line list according to the sorting of the line risk indexes. Based on the influence on the line fault probability under the condition of expected associated disasters of lightning disasters, the accuracy of line fault prediction in extreme weather is improved.