264 results about "Thunderstorm" patented technology

The invention relates to the technical field of meteorological prediction, and discloses a minute-level thunderstorm strong wind nowcasting method and system, and the method comprises the steps: obtaining multi-source meteorological observation data, and carrying out the preprocessing of the multi-source meteorological observation data, and obtaining the preprocessed data; performing time continuity judgment and fusing the processed data to obtain a training sample; processing the training samples based on a sliding window, dividing the training samples into a training set, a verification set and a test set, constructing a neural network model, and training the neural network model to obtain a forecasting model; real-time a frames of observation data are processed based on the forecasting model, a frames of wind speed forecasting images with the resolution ratio of 1 kilometer in the future are obtained, and the wind speed forecasting images comprise a wind speed continuous distribution diagram and wind area falling area forecasting results larger than or equal to 8.0 m / s and larger than or equal to 17.2 m / s. According to the invention, the comprehensive perception capability of strong convection characteristics is improved, and minute-level strong wind nowcasting is realized.

The invention discloses a physical constraint fused multi-scale extreme gale event AI identification method and device and a medium, and relates to the technical field of atmospheric science and artificial intelligence. According to the method, a novel multi-scale extreme gale event identification model is constructed by integrating multi-scale meteorological data. The model effectively captures extreme gale features under different scales through a multi-scale feature extraction module, a feature fusion sharing module and a multi-task output module. In addition, a physical constraint loss design is introduced into the model to ensure that outputs such as wind speed and air pressure conform to meteorological dynamics rules. Through deep learning analysis of meteorological data and combination of physical mechanism understanding, different types of extreme gale events such as hurricane, thunderstorm gale and tornado can be effectively distinguished, and reliable data support and scientific basis are provided for meteorological prediction and emergency response.

The application discloses a thunderstorm monitoring device and path planning method of unmanned aerial vehicle based on electric field and magnetic field detection. The device comprises a quadrature magnetic antenna array, a vertical differential electric field sensor, a signal processing module, a positioning algorithm module, a distance estimation module, an autonomous decision module, a GNSS module and an inertial measurement module. The quadrature magnetic antenna array is composed of two orthogonally arranged magnetic antennas, which are at a 45° angle with the flight direction of the unmanned aerial vehicle, and is used for receiving the horizontal magnetic field signal of lightning radiation. The vertical differential electric field sensor comprises two groups of upper and lower electrode plates and measures the differential value of the vertical electric field intensity. Through the cooperative measurement of the magnetic antenna array and the electric field sensor, the lightning azimuth (θ) is calculated by combining the magnetic field intensity ratio, and the quadrant is corrected by using the electric field polarity. Based on the magnetic field amplitude attenuation model and the waveform rise time correction factor, a distance estimation model is constructed. The autonomous decision module generates an approach path or an obstacle avoidance path according to the lightning azimuth and distance, and drives the flight control system to adjust the heading.

The invention relates to the technical field of grounding resistance detection, in particular to a power transmission line tower grounding resistance monitoring system and a use method, and the system comprises a signal collection and correction module which collects and updates various parameters of each tower in real time, builds a mathematical model of environment parameters and grounding resistance values, and obtains the real-time environment parameters and the grounding resistance values after obtaining the real-time environment parameters; automatically correcting the measured resistance value by using the mathematical model; the fault early warning module is used for collecting historical monitoring data and calculating an abnormal risk probability in combination with real-time abnormal data; the fault positioning module is used for realizing accurate positioning of a fault tower through a time difference positioning method; and the remote control module is linked with the meteorological early warning platform, starts high-frequency monitoring before thunderstorm, and dynamically adjusts a ground resistance threshold value. The measurement value is automatically corrected through the linear regression model, interference of environmental fluctuation on resistance measurement can be effectively eliminated, and errors of traditional static measurement are reduced.

The invention provides a weather forecasting method and system for thunderstorm and gale. The method comprises the following steps: acquiring multi-source weather observation data in a past preset time period; inputting the multi-source meteorological observation data into a pre-trained progressive wind speed forecasting model to extract and fuse multi-scale spatial-temporal characteristics in the multi-source meteorological observation data, and sequentially generating average wind speed forecasting results covering a plurality of future time periods; inputting the average wind speed forecast result into a pre-trained gust mapping model, and performing nonlinear mapping to obtain gust wind speed forecast results corresponding to a plurality of future time periods; and based on the gust speed forecast result, whether thunderstorm and gale risks exist in the future time period is judged. In the mode, the multi-source meteorological observation data is acquired and prediction is performed in combination with the progressive wind speed prediction model and the gust mapping model, so that the accuracy of wind speed and gust prediction can be improved, the thunderstorm and gale risk can be identified in advance, and the disaster early warning and disaster prevention and reduction capabilities are further improved.

The invention relates to a thunderstorm and gale identification method based on multi-dimensional characteristic parameter fusion, and belongs to the field of radio, and the method comprises the steps: employing a neighborhood value method to remove the electromagnetic interference of dual-line polarization Doppler weather radar data, carrying out the rollback fuzzy processing, and then carrying out the meshing; dual-polarization radar data, wind profile radar data, sounding data and ground automatic station data are respectively obtained, and respective characteristic parameters are extracted from the data; carrying out normalization processing on the characteristic parameters, and obtaining corresponding parameters; constructing a multi-parameter integrated early warning model based on the plurality of fusion parameters, and training and verifying the model; and inputting parameters into the trained model for operation, and outputting the thunderstorm gale probability P. According to the thunderstorm gale identification method, through combination of multi-source data fusion and multiple physical mechanism fusion and integration of innovation of a machine learning model, the technology of thunderstorm gale identification from single index dependence to multi-dimensional collaboration and from static threshold value to dynamic self-adaption is realized.

The invention discloses a TITAN product thunderstorm forecast field correction method and system based on multi-scale ensemble learning. The method comprises the following steps: S1, forming a unified multi-source meteorological data set and a TITAN product set; s2, constructing a meteorological target dichotomy label; s3, preprocessing the multi-source meteorological data set, and matching meteorological target dichotomy tags to generate a sample data set; s4, calculating a multi-scale feature set reflecting macro and micro features of the thunderstorm system and the non-thunderstorm system based on the sample data set; s5, generating a comprehensive multi-scale feature expression; s6, forming a plurality of sub-models with differentiated scale sensing capability; s7, training to form a final thunderstorm system identification model; s8, constructing a TITAN product forecast field reconstruction module; and S9, generating a forecast result reflecting the spatial distribution, the development trend and the potential influence area of the thunderstorm system in real time. According to the method, the time-space evolution feature capturing precision of the thunderstorm system and the prediction stability and reliability are improved.

The invention discloses a thunderstorm and gale dynamic extrapolation forecasting method based on multi-task MSTA-ConvLSTM, and relates to the technical field of atmospheric sciences, and the method comprises the steps: constructing a multi-source fusion spatio-temporal data set and a gale process record file, and obtaining a data set; a thunderstorm gale nowcasting model based on an MSTA-ConvLSTM model is constructed; according to the method, a dynamic gating mechanism of lightning data is introduced, whether a lightning probability prediction task is started or not is intelligently judged according to the lightning activity intensity, thunderstorm, gale and co-evolution characteristics of the lightning process are considered, 0-3-hour high-temporal-spatial-resolution nowcasting is achieved, the limitation of traditional single-task forecasting is broken through, a multi-task learning structure is adopted, and the prediction efficiency is improved. The model is guided to recognize the lightning occurrence probability during main task gale prediction, and the overall perception capability of a severe convection system is improved; extreme sample learning is enhanced through a weighted loss function, and output is optimized in combination with a gust coefficient model, so that the forecasting precision and practicability are effectively improved.

The invention is suitable for the technical field of thunderstorm prediction, and provides a strong thunderstorm potential risk and intensity prediction method based on an artificial neural network, and the method comprises the steps: obtaining the ground-to-ground lightning data of a target region, determining the time distribution characteristics of a thunderstorm event, and recognizing the time valley of the thunderstorm event; collecting multi-source meteorological data in a target area, and screening meteorological prediction factor data; performing space-time alignment processing on the ground-to-ground lightning data and the meteorological prediction factor data, and constructing a grid unit day-by-day sample set based on the aligned data; constructing a probability classification model of a strong thunderstorm event based on the grid unit day-by-day sample set; building a regression neural network model based on the grid units which are judged to be strong thunderstorm high risks by a probability classification model; and a collaborative prediction result is output, a predicted value of a geometric mean value of the ground-to-ground flash frequency and the lightning current amplitude is synchronously output, and a risk assessment basis of classification discrimination and numerical prediction capability is provided for extreme weather early warning and power grid disaster prevention and reduction through double-order modeling.

The invention provides a meteorological disaster prevention and control system for tobacco safe planting and an application method thereof, and the system comprises a strong wind monitoring control module, a drought monitoring control module, a flood monitoring control module, a thunderstorm monitoring control module, a frost monitoring control module and a hail monitoring control module which are all connected with a protection device. The protection device comprises a vertical protection unit arranged on the side face of the tobacco and a transverse protection unit arranged on the top face of the tobacco. All the modules are used for monitoring, calculating and early warning the tobacco plant base from precipitation, soil humidity, crop growth, moisture supply, climate change and ecological environment in an all-around mode, unmanned, automatic and intelligent intervention is conducted in time according to different meteorological disasters, and the tobacco plant base is maintained uninterruptedly in real time. And the tobacco plants are kept in a high-yield and high-quality healthy state.

The invention provides a rainstorm lightning disaster risk early warning method and system based on an analytic hierarchy process, and the method comprises the steps: obtaining historical thunderstorm frequency data and thunderstorm movement path data of a target region, and analyzing and determining the sensitivity levels of different regions in thunderstorm activities; the lightning stroke risk assessment model is decomposed into a target layer, a criterion layer and a scheme layer by using an analytic hierarchy process; according to historical data including lightning stroke historical data, thunderstorm historical data and disaster damage historical data, performing pairwise comparison on each factor of the criterion layer, and constructing a judgment matrix; acquiring spatial position information and characteristics of different types of disaster-bearing bodies, performing overlay analysis according to spatial positions of the disaster-bearing bodies and the lightning drop point probability distribution map, calculating lightning stroke probability values of areas where the disaster-bearing bodies are located, and generating a disaster-bearing body risk level distribution map.

The invention relates to the technical field of thunder and lightning early warning, and discloses a thunderstorm early warning system and method applied to a new energy station, and the system comprises a data collection module, a data processing module and an early warning decision module which are arranged in a target new energy station. The multi-source thunderstorm early warning information is sent to a data processing module; wherein the multi-source thunderstorm early warning information comprises lightning data, radar data, satellite data, micrometeorological data and atmospheric electric field data, and the data processing module performs feature extraction on the multi-source thunderstorm early warning information to obtain a first thunderstorm early warning feature vector and a second thunderstorm early warning feature vector; and the early warning decision module performs thunderstorm early warning based on the first thunderstorm early warning feature vector and the second thunderstorm early warning feature vector to obtain thunderstorm early warning information of the new energy station. According to the invention, the accuracy of the thunderstorm early warning information of the new energy station is improved.

The invention relates to the technical field of rainwater recycling, in particular to a rainwater filtering and recycling system which comprises a water storage tank with an opening in the top face, a mesh filter plate is fixedly connected to the upper portion of the inner wall of the water storage tank, the top face of the mesh filter plate is in an arc shape, and a cavity is formed in the inner lower portion of the mesh filter plate. A sealing plate is slidably mounted in a cavity of the mesh filter plate, a sliding groove communicated with the cavity is formed in the bottom face of the mesh filter plate, a right trapezoid plate is slidably connected into the sliding groove of the mesh filter plate, and the top face of the right trapezoid plate is fixedly connected with the bottom face of the sealing plate. According to the rainwater filtering and recycling system, the first rotating shaft and the connecting rod drive the scraping plate to move along the arc-shaped top face of the mesh filtering plate, impurities on the mesh filtering plate are removed, filtering holes in the mesh filtering plate are prevented from being blocked, and meanwhile evaporation of water in the water storage tank can be reduced under the condition that the weather is not in thunderstorm and gale weather.

The invention discloses a lightning multi-parameter space-time analysis method based on clustering tracking and a thunderstorm risk early warning method and system, and relates to the technical field of severe convective weather early warning. The method comprises the following steps: collecting and analyzing lightning data; data preprocessing and space-time collaborative clustering: clustering lightning belonging to the same thunderstorm monomer in the current time window by using optimized clustering parameters; cross-period target tracking and association: constructing a lightning cluster tracking chain of a continuous time window; performing multi-factor dynamic feature extraction, namely capturing real-time dynamic feature parameters on the associated cluster sequence; and constructing a hazard index model and carrying out risk early warning. According to the method, dynamic tracking, strength evaluation and danger grading of thunderstorm activities can be realized by utilizing lightweight and easily acquired lightning positioning data, the method has the characteristics of light calculation load and high real-time performance, and objective and quantitative technical support can be provided for refined short-term and temporary early warning decision making.

The invention discloses a thunderstorm activity influence factor inversion method and device, a medium and a product, and relates to the field of data inversion processing, and the method comprises the steps: dividing a to-be-inverted region into a plurality of research regions according to different appearance feature types of lightning; based on ERA5 reanalysis grid point data, calculating a severe convection parameter causing a thunderstorm activity; interpolating the severe convection parameters into the two-dimensional longitude and latitude information of the lightning activity by adopting a space-time proximity / proximity principle interpolation method, and visualizing the two-dimensional longitude and latitude information; analyzing month-by-month severe convection conditions of each sub-research area according to a visual processing result; adopting an anti-weight distance interpolation method to interpolate lightning positioning monitoring data into grid points with the same resolution as ERA5 reanalysis grid point data; and analyzing the advanced correlation coefficient of the strong convection condition and the lightning frequency after interpolation in each research sub-region so as to invert the lightning activity influence factor. According to the application, the thunderstorm early warning and forecasting accuracy can be improved, so that the lightning protection and disaster reduction capability is effectively improved.

The invention relates to the technical field of three-dimensional wind field inversion, in particular to a three-dimensional wind field inversion method in a thunderstorm cloud strong echo region based on a single Doppler weather radar, which comprises the following steps of: 1, carrying out lattice processing on a radar reflectivity factor to obtain a radar reflectivity factor; the reflectivity factor is converted from a polar coordinate system to a Cartesian rectangular coordinate system through interpolation; 2, performing same-layer continuous moment strong echo graph similarity recognition through an image similarity recognition technology; 3, calculating the horizontal movement speed of the thunderstorm cloud at different heights; and 4, comparing the observation result of the radial speed of the radar with the horizontal movement speed of the thunderstorm, and calculating the rising or sinking airflow speed. According to the method, the grid data of the single Doppler weather radar and the observation data of the radial speed of the radar can be utilized to accurately describe the three-dimensional wind field of the strong echo region in the thunderstorm cloud.

The invention relates to an intelligent thunderstorm weather forecasting method and system and a medium, belongs to the field of thunderstorm forecasting, and provides the intelligent thunderstorm weather forecasting method for solving the problem that an existing mode is insufficient in forecasting capacity, and the intelligent thunderstorm weather forecasting method comprises the following steps: collecting and processing initial data, and obtaining a multi-source fusion initial field of a unified format; constructing a short-time forecasting model based on a Swin Transform backbone network, and outputting a thunderstorm forecasting field in the future 6 hours by inputting a multi-source fusion initial field and underlying surface information; and a short-term forecasting model based on a FuXi large model is constructed, and by inputting the multi-source fusion initial field and underlying surface information obtained in the step 1, possible thunderstorm occurrence areas and intensity distribution data in more than six hours in the future are output. By introducing a Swin Transform backbone network and combining an autoregression prediction strategy and a multi-task loss function, the capturing precision and real-time performance of the thunderstorm time-space evolution process are effectively improved.

The invention discloses a hail cloud early recognition method based on a polarization radar. The method comprises the following steps: establishing a sample library; extracting a plurality of decision factors of hail cloud monomer radar data 20 minutes before hail occurs in the sample library, establishing a logistic regression model for the plurality of decision factors in the sample library, selecting an optimal weight algorithm by using a cross validation mechanism, calculating weights of the plurality of decision factors, and determining a hail early warning index threshold; the early warning index is compared with the hail early warning index threshold value, if the early warning index is larger than or equal to the hail early warning index threshold value, it is judged that the hail cloud is an early-stage hail cloud monomer, and if not, it is judged that the hail cloud is a general thunderstorm cloud. Based on polarization radar data and three key judgment factors, the early recognition accuracy of hail clouds is effectively improved, the hail clouds and general thunderstorm clouds can be accurately distinguished, and weather forecast and manual hail suppression operation are assisted.

The invention discloses a static satellite thunderstorm cloud cluster dynamic monitoring and identification method based on a long short term memory network, which comprises the following steps: constructing a thunderstorm cloud cluster dynamic monitoring and identification model based on a long short term memory network model, and finishing training by adopting a dynamic monitoring machine learning data set; the dynamic monitoring machine learning data set comprises a plurality of dynamic monitoring machine learning arrays, and each dynamic monitoring machine learning array comprises a time-space matched historical mesoscale convective cloud cluster characteristic parameter group and lightning activity generation label data; the input of the trained thunderstorm cloud cluster dynamic monitoring identification model is physical characteristic parameters corresponding to all the mesoscale convective cloud clusters identified in the target area at m continuous moments before the current moment, and the output of the trained thunderstorm cloud cluster dynamic monitoring identification model is a thunderstorm cloud cluster identification result at the current moment. Therefore, by dynamically monitoring the characteristic parameters of the mesoscale convective cloud cluster, high-precision identification of offshore and open-sea thunderstorm cloud clusters can be realized.

The invention discloses a thunderstorm gale regression analysis method and system based on multi-stream parameters, and relates to the technical field of data processing. The method comprises the following steps: collecting multi-stream parameters in real time, and constructing a real-time multi-stream parameter sequence; mapping the real-time multi-flow-parameter sequence to a pre-constructed multi-flow-parameter coupling sensitivity field model, and calculating to generate a real-time convection sensitivity field; inputting the real-time convection sensitivity field into the physical consistency constraint model to generate real-time structured convection characteristics; main regression analysis under multi-flow parameter regression is executed, and a main regression result is established; and after the real-time local disturbance index is calculated, disturbance residual compensation of the main regression result is triggered, dynamic weight adjustment is carried out on the main regression result after residual compensation by utilizing seasonal phase characteristics, and a thunderstorm gale regression analysis result is output. The technical problem that thunderstorm gale prediction accuracy is insufficient in the prior art is solved, and the technical effect of improving thunderstorm gale regression analysis result accuracy is achieved.

The invention discloses a space-time two-dimensional low-altitude thunderstorm prediction method based on incremental learning, and relates to the technical field of artificial intelligence and meteorology crossing, and the method comprises the steps: collecting electromagnetic wave image data, forming T electromagnetic wave data sets for low-altitude thunderstorm prediction, and obtaining T electromagnetic wave data sets for low-altitude thunderstorm prediction when each increment batch arrives; a new increment module and a current increment batch prediction model are established, Transform and a CNN feature extractor are adopted to carry out space-time two-dimensional feature extraction on electromagnetic wave image data, an increment learning method is utilized to carry out prediction model iteration training on multiple batches of electromagnetic wave image data, and on the premise that the model does not lose learned knowledge, the prediction efficiency of the current increment batch prediction model is improved. According to the method, a new category of electromagnetic waveform data is gradually learned, so that the identification and prediction capability of the finally obtained prediction model which is trained in the invention is ensured, the defect of fixed model parameters is avoided, and the classification precision of the method is improved.

The utility model provides a three-core aviation plug connector, which is applied to the field of connectors and comprises a male seat rubber coating, a male seat plastic shell, a female head inner core, a grounding pin, a male seat female pin, a female head male pin, a female head female pin, a locking fastener, a wire clamp and a female head rubber coating. One side of the male seat rubber coating is an input side, the other side of the male seat rubber coating is a butt joint side, the butt joint side is matched with one side of the female head inner core, the male seat plastic shell is arranged at the connecting position of the male seat rubber coating and the female head inner core, the female head inner core is embedded in the locking fastener, and the male seat rubber coating is matched with the female head inner core. The two ends of the grounding pin and the two ends of the male base female pin are connected into the male base rubber coating and the female head inner core respectively, the female head male pin and the female head female pin are connected with the male base female pin and the grounding pin respectively, and one end of the female head male pin and one end of the female head female pin are inserted into the female head inner core. The technical problems that at present, if contact is poor, signals are prone to being lost, the signals can be interfered in thunderstorm weather, and the signals are unstable are solved.

The invention discloses an overhead transmission line lightning stroke risk early warning method and device and a medium. The method comprises the following steps: training a pre-constructed neural network based on historical data of a lightning stroke event of the overhead transmission line, and generating a lightning current amplitude prediction model; based on the lightning current amplitude in the historical data, constructing a lightning current amplitude probability distribution function of each region; obtaining input data of lightning current amplitude prediction through meteorological thunderstorm data and numerical weather forecast data; inputting the input data into a lightning current amplitude prediction model, obtaining a predicted lightning current amplitude, inputting the predicted lightning current amplitude into a corresponding lightning current amplitude probability distribution function, and determining a lightning current amplitude probability; and determining a lightning trip-out rate of the overhead transmission line according to the predicted lightning current amplitude and the lightning current amplitude probability in combination with the transmission line data, and determining a lightning flashover risk early warning level of the transmission line according to the lightning trip-out rate.

The utility model discloses a radio monitoring special vehicle lightning protection grounding device comprising a cabinet which is used for collecting system equipment in the cabinet to a grounding copper bar at the back of the cabinet; a plurality of grounding copper columns are arranged in the external interface box; wherein one grounding copper column is connected with the grounding copper bar; the other grounding copper columns are in grounding connection with the vehicle body; and the external grounding system is used for connecting the plurality of grounding copper columns with the ground. The lightning protection grounding device relates to a mounting structure for solving lightning protection measures in regions with frequent thunderstorms, so that the grounding resistance is greatly reduced, and the impact current can be effectively and quickly led into the ground. The problem of large grounding resistance is solved, and the problem of grounding of the vehicle body is also solved.

The invention discloses an unmanned aerial vehicle thunderstorm monitoring device based on electric field and magnetic field detection and a path planning method. The device comprises an orthogonal magnetic antenna array, a vertical differential electric field sensor, a signal processing module, a positioning algorithm module, a distance estimation module, an autonomous decision module, a GNSS module and an inertial measurement module. The orthogonal magnetic antenna array is composed of two orthogonally arranged magnetic antennas, forms an included angle of 45 degrees with the flight direction of the unmanned aerial vehicle, and is used for receiving horizontal magnetic field signals of lightning radiation. The vertical differential electric field sensor comprises an upper group of electrode plates and a lower group of electrode plates and is used for measuring an electric field intensity differential value in the vertical direction. A lightning azimuth angle (theta) is calculated through cooperative measurement of a magnetic antenna array and an electric field sensor in combination with a magnetic field intensity ratio, and a quadrant is corrected by using electric field polarity. And constructing a distance estimation model based on the magnetic field amplitude attenuation model (E / B = c) and the waveform rise time correction factor. The autonomous decision-making module generates an abutting path or an obstacle avoidance path according to the lightning orientation and the distance, and drives the flight control system to adjust the course.

The present invention belongs to the technical field of high-voltage lightning strike fault prevention, and more specifically, relates to a lightweight lightning image instance segmentation method. The method includes: First, obtain lightning images existing in the target area during thunderstorms and clean them, removing images that do not meet the conditions, and the remaining images should have moderate exposure; Then, input the cleaned lightning image data into the YOLO-lightning network model to perform instance segmentation on the lightning image data; Finally, output the contour information of the lightning to be recognized in the image. The present invention solves the problems of insufficient accuracy and limited feature extraction ability in the instance segmentation of complex-shaped targets in the prior art.

The invention relates to the technical field of emergency plan planning, and provides a lightning protection device emergency plan planning system based on transformer substation grounding grid faults, a data collection module collects historical data and real-time data of a transformer substation, and a prediction module constructs a grounding grid corrosion prediction model based on soil environment parameters in the historical data. A thunderstorm probability prediction model is generated based on radar meteorological data, a real-time risk assessment module establishes a grounding grid lightning stroke risk association model based on prediction results of the two models, and the lightning stroke risk of the current grounding grid is judged according to real-time data; the emergency plan planning module plans an emergency plan corresponding to the lightning stroke risk based on the lightning stroke risk in combination with a built-in standard plan; the thunderstorm risk association model is constructed by combining the prediction results of the linear regression model and the logistic regression model, and the emergency plan planning module plans the emergency plan according to the thunderstorm risk association model, so that the whole-course control of the lightning stroke risk of the transformer substation from prediction to the emergency plan is realized, and the stable operation of a power grid is ensured.

The present invention discloses a method for replacing manually observed thunderstorm days, comprising the following steps: S1: acquiring lightning data through a lightning location system; S2: performing quality control on the acquired lightning data; S3: establishing a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days; S4: analyzing the consistency of the lightning location data and the number of manually observed thunderstorm days; S5: verifying the reliability of the lightning location data in replacing the number of manually observed thunderstorm days. The present invention establishes a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days after quality control of the lightning location data, performs a consistency analysis of the lightning location data and the number of thunderstorm days, and then verifies the reliability of the lightning location data in replacing the number of manually observed thunderstorm days, thereby concluding that the lightning location data is feasible in replacing the number of manually observed thunderstorm days.

The invention discloses a thunderstorm cloud electric field prediction method, device and equipment based on a deep learning algorithm, and relates to the technical field of thunderstorm cloud electric field prediction. The method comprises the following steps: firstly, acquiring a target convection parameter group of a region of interest, and acquiring historical observation data of each convection parameter in the target convection parameter group and historical observation data of an electric field in a thunderstorm cloud; constructing a convection parameter prediction model and a thunderstorm cloud internal electric field inversion model based on historical observation data; integrating the two models to obtain a thunderstorm cloud internal electric field prediction model corresponding to each convection parameter set; and performing thunderstorm cloud internal electric field prediction on the region of interest according to the thunderstorm cloud internal electric field prediction model corresponding to each convection parameter set. According to the application, convection parameter prediction and in-cloud electric field inversion are fused, and prediction of the thunderstorm in-cloud electric field can be realized by using observable convection parameters.

The invention discloses a 750kV power transmission line plateau type integrated lightning protection method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the data of a plateau region through the Internet of Things to construct a plateau data set, carrying out the lightning correlation feature screening of the plateau data set, obtaining lightning features, and carrying out the preprocessing of the lightning features, and obtaining pure features; the method comprises the following steps: collecting data of a plateau region through the Internet of Things to construct a plateau data set, performing lightning strike associated feature screening on the plateau data set to obtain lightning features, and preprocessing the lightning features to obtain pure features; establishing a comprehensive evaluation model, and inputting the pure features into the comprehensive evaluation model to obtain a feature comprehensive score; constructing and training a thunderstorm prediction model based on LSTM, and inputting the feature comprehensive score into the thunderstorm model to obtain a thunder and lightning activity prediction value; and dividing a risk interval for the lightning activity prediction value, and making a corresponding lightning protection measure according to the risk interval.