214 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 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 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 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 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 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 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 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 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.

The invention discloses a vehicle-mounted micrometeorological monitoring and camping site disaster early warning system and method, and belongs to the technical field of vehicle-mounted meteorological monitoring, and the system comprises a data collection and preprocessing module which is used for obtaining and calibrating multi-source meteorological and geographic data; the microclimate modeling module is used for generating a microclimate grid with a real-time weather predicted value by taking local microclimate data as a benchmark and fusing cloud and geographic data; the disaster risk assessment module performs quantitative risk assessment on camping disasters such as flood, tree smashing, landslide and thunderstorm based on the data of the microclimate grid; and the early warning generation module generates visual early warning information including a disaster icon, a risk level, time information and a risk avoiding instruction according to the quantitative risk assessment result. According to the invention, by optimizing the layout of the sensors and introducing a terrain correction interpolation algorithm and scene disaster assessment, the meteorological monitoring precision in a mobile environment is improved, and timely early warning of camping field micro-scale disasters is realized.

The invention discloses an agricultural unmanned aerial pesticide application method based on a real-time dynamic positioning technology, and belongs to the technical field of unmanned aerial vehicles. The method comprises the following steps: 1, AI atmosphere prediction; 2, intelligent mode switching; and multi-source data fusion is fused into the two methods, and the multi-source data is preprocessed for time alignment, spatial interpolation and 3 sigma principle abnormal value elimination. Wherein in the RTK + IMU tight coupling mode, IMU mechanical arrangement data, RTK observation values, 15-dimensional state vector modeling poses and sensor errors are fused through Kalman filtering; and in a LiDAR SLAM mode, the point cloud is subjected to ground removal through RANSAC, and after feature extraction, a digital twin map is matched, so that GNSS-free environment centimeter-level positioning is realized. After the technologies are introduced into the unmanned aerial vehicle, the positioning error of the unmanned aerial vehicle in thunderstorm days can be greatly reduced, the positioning availability of the unmanned aerial vehicle in complex terrains covered by signals reaches 98% or above, and the working efficiency in complex regions can be remarkably improved.

The invention provides an active lightning triggering device and method for a fan blade. The device comprises two copper rods with tip heads, a protection circuit, a boost lightning circuit and a metal shielding net. The two copper rods are arranged in parallel at the blade tip of the fan blade, one copper rod is directly grounded, and the other copper rod is connected with the boost lightning circuit through the protection circuit; the boost lightning guide circuit comprises a charging capacitor, an inductor, a high-frequency switch and a diode and is used for sensing positive high voltage and dynamically adjusting resonance characteristics so as to enhance voltage difference between thundercloud and a copper rod and trigger uplink and downlink leader to form a discharge channel; the metal shielding net wraps the blade tip and is connected with the grounding device to ensure that lightning current is preferentially discharged through the copper rod, and the blade tip structure is prevented from being damaged. According to the method, environmental meteorology and blade attitudes are monitored in real time, a boosted circuit is started in combination with threshold judgment, and control parameters are optimized by using a remote monitoring platform, so that intelligent lightning protection management is realized. According to the device, by actively exciting corona discharge and accurately guiding a lightning path, the lightning protection efficiency of the blade is remarkably improved, the damage risk of a non-lightning-receiving area is reduced, meanwhile, a maintenance decision is supported through datamation operation and maintenance, and the device is suitable for wind generating set protection in a thunderstorm area.

The invention provides a thunderstorm cloud spatial and temporal distribution identification method based on image processing, belongs to the field of novel power system protection, and mainly utilizes the characteristic that thunderbolt continuously generated in the continuous development process of thunderstorm cloud still presents concentrated distribution characteristics in longitude and latitude distribution characteristics. After a thundercloud thunderbolt data set is subjected to time scale stripping, thundercloud activity ranges represented by high-density thunderbolt points are integrated by utilizing a morphological processing means to obtain development time and activity space of each thundercloud, and thundercloud activity indexes acquired by a thunderbolt positioning system are utilized to obtain thundercloud thunderbolt data. And carrying out real-time judgment on thundercloud risk capable of causing lightning stroke of the fan matrix of the new energy power plant. The invention aims to solve the defects that a high-dimensional data set cannot be identified and thundercloud has space stacking in different time periods and is difficult to extract in the current generally applied density clustering method, so that the accuracy of thundercloud development time and activity range extraction is improved.

The utility model belongs to the field of water tanks, particularly relates to a lightning-protection assembly type bolted connection water tank, and aims to solve the problems that the top of an existing traditional water tank is usually provided with a maintenance port which is convenient for personnel to enter the water tank and maintain internal electric appliances, but the personnel usually carry a tool box by hands during maintenance, and the maintenance is inconvenient. In order to solve the problems that when a person climbs to the top of a water tank through a crawling ladder, the person needs to lift a tool box all the time, the tool box is inconvenient to climb, potential safety hazards are likely to occur, and the water tank cannot be subjected to lightning protection and is likely to be damaged in the thunderstorm weather using process, the following scheme is provided that the tool box comprises a foundation platform, and channel steel is fixedly arranged at the top of the foundation platform; a worker can fix the tool box to the tail end of the connecting rope through the transportation mechanism, the tool box does not need to be held by hand in the climbing process, the instability risk caused by holding with one hand is avoided, lightning current can be directly guided into the ground through the lightning protection mechanism, and the water tank and internal equipment are prevented from being damaged by lightning stroke.

The invention relates to the field of large steel structure storage tanks (tanks), and discloses a tank upside-down lifting installation construction process. The method comprises the following steps that S1, inspection before construction is conducted, specifically, when the thickness of a steel plate for the groove wall in main construction materials is larger than or equal to 22 mmm, ultrasonic inspection should be conducted according to NB / T470133, and the quality grade should meet the III-grade requirement; checking the number: when the thickness is less than or equal to 30mm, carrying out spot check on not less than two steel plates according to 20% of the number of the steel plates, and checking the steel plates one by one when disqualification is found; when the thickness is greater than 30mm, checking one by one; the Q345R steel plate with the thickness larger than or equal to 36 mmmm should be used in the normalizing state. Related hoisting calculation is carried out according to a design drawing, a hygrothermograph is additionally arranged in the process and used for monitoring the surrounding environment of construction, the weather conditions of the same season in the first few years are extracted in the early stage of construction, and shutdown materials need to be prepared in advance to protect facilities in rainy seasons and thunderstorm seasons.

The invention discloses a thunderstorm weather assessment method based on rainfall and atmospheric electric field feature weighted fusion. The method comprises the following steps: determining a target area; multi-modal features are extracted; and an IGBDT model is constructed. The invention provides a technical scheme with high physical interpretability and excellent classification performance for severe convection weather monitoring.

The application discloses a hail cloud early identification method based on polarization radar, comprising the following steps: establishing a sample library; extracting multiple determination factors of hail cloud monomer radar data 20 minutes before hail occurrence in the sample library; establishing a logistic regression model for multiple determination factors in the sample library; selecting an optimal weight algorithm by using a cross-validation mechanism; calculating the weight of multiple determination factors; determining a hail early warning index threshold; comparing the early warning index with the hail early warning index threshold; if the early warning index is greater than or equal to the hail early warning index threshold, the early hail cloud monomer is determined; otherwise, the general thunderstorm cloud is determined. The application can effectively improve the early hail cloud identification accuracy, accurately distinguish the hail cloud from the general thunderstorm cloud, and assist weather forecasting and artificial hail prevention operation based on polarization radar data and three key determination factors.