352 results about "Cyclone" patented technology

The invention provides a power equipment meteorological monitoring and early warning system based on artificial intelligence. The power equipment meteorological monitoring and early warning system based on artificial intelligence comprises a data acquisition module, a data preprocessing module, a spatio-temporal feature fusion module and a meteorological disaster prediction model, the meteorological disaster prediction model adopts a deep reinforcement learning framework, inputs a multi-dimensional spatio-temporal feature matrix, and carries out meteorological disaster prediction on the multi-dimensional spatio-temporal feature matrix. And outputting meteorological disaster risk levels and key parameter predicted values in a future preset time period, including a wind speed, precipitation, temperature anomaly and tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The power equipment meteorological monitoring and early warning system based on artificial intelligence provided by the invention has the advantages that the data interpolation precision of a complex terrain region can be improved, high-precision prediction of a typhoon path, short-time strong wind and an icing risk can be realized, and the early warning accuracy and defense response efficiency of a power system to meteorological disasters can be comprehensively improved.

The invention discloses a TCBL wind field high-resolution LES simulation method facing gray area parameterization, a medium and a program product. According to the method, a TCBL idealized initial gradient wind field is constructed based on a modified Rankine vortex model, controllability and stability of initial conditions are ensured by specifying key kinetic parameters, and thermodynamic profiles under different wind speed conditions are obtained based on synthetic analysis of tropical cyclone sounding data. And correcting the bottom of the boundary layer by adopting a quadratic and logarithmic function fitting method. Through high-resolution grid configuration and dynamic boundary conditions, secondary grid turbulence is processed in combination with an SGS-TKE closed model, and adaptability of the model to strong rotation and high wind shear environments is optimized by adopting a dynamic correction mechanism. Besides, the stability of thermodynamic variables is maintained through large-scale tendency correction, and high-resolution three-dimensional wind field and turbulence statistical reference data are output and used for verifying and improving a turbulence parameterization scheme of the TCBL under gray region resolution.

The invention belongs to the technical field of ocean remote sensing, and relates to a spaceborne microwave radiometer tropical cyclone wind speed inversion method based on physical constraint deep learning, comprising the following steps: acquiring tropical cyclone parameter information; extracting multi-band brightness temperature data of the satellite-borne microwave radiometer in a corresponding space-time range, and constructing a sample data set; a deep learning inversion algorithm model with physical constraints is loaded and trained; the physical constraint deep learning inversion algorithm model comprises an input layer, a network layer, a physical layer and an output layer; the network layer is composed of a residual neural network, the physical layer is composed of a D matrix remote sensing statistical formula and provides physical constraints for the whole model, and the D matrix is a nonlinear D matrix; and outputting the tropical cyclone wind speed in the current space-time range. According to the method, the brightness temperature information of the satellite-borne microwave radiometer is obtained by utilizing the tropical cyclone trajectory information, and the network model with physical information is constructed by utilizing the combination of the D matrix equation and the neural network, so that more accurate and high-precision tropical cyclone wind speed information is obtained.

The invention discloses a flocculation treatment device and method for removing high turbidity of a water sample. The method comprises the following steps: monitoring an inflow turbidity parameter in real time, continuously collecting original optical characteristic data of water through an optical sensor array, and calculating a turbidity fluctuation rate parameter after noise reduction treatment; carrying out graded rotational flow mixing treatment, forming a pressure gradient through three stages of tapered rotational flow pipes in sequence, activating an ultrasonic frequency field, and synchronously adding main and auxiliary flocculants; the method comprises the following steps: introducing a water sample into a multi-stage gradient flocculation reaction zone, and sequentially executing turbulent flow enhanced flocculation, slow stirring flocculation and pulsed electric field assisted flocculation to form a floc structure with different particle size distribution; running parameters of a sedimentation module are adjusted according to real-time effluent turbidity feedback, and the inclination angle of an inclined plate assembly and the opening degree of a mud discharging valve are dynamically adjusted; a parameter set is updated through a prediction model based on periodically collected system operation data, a closed-loop optimization control strategy is formed, the adaptability to extreme turbidity is enhanced, the solid-liquid separation efficiency is improved, the overall energy consumption is reduced, and the efficient and stable requirements of emergency treatment are met.

The invention discloses a method for improving a TCBL grey area scale perception similarity function based on LES simulation optimization, a medium and a program product, and aims to improve the prediction precision of a numerical weather prediction model on a tropical cyclone path and strength. According to the method, a high-resolution LES simulation framework is constructed, a high-resolution reference data set of TCBLs with different intensities is generated, scale turbulence kinetic energy and secondary grid turbulence kinetic energy are calculated and analyzed, and an improved grey area scale perception similarity function is constructed based on multi-scale filtering and function fitting. The similarity function can more accurately represent the next grid turbulence characteristic of the gray region resolution, and the effectiveness of the similarity function in TC path and intensity prediction is verified by integrating the similarity function with a numerical weather prediction model. According to the method, the simulation precision of the model under the conditions of complex wind shearing and strong rotation is remarkably improved, the universality of the model is enhanced, important support is provided for weather forecast and tropical cyclone research, and the method has wide scientific value and application prospects.

The invention belongs to the technical field of meteorological analysis, and provides a southwest vortex identification and path tracking method and device, equipment and a medium. The method comprises the following steps: identifying a cyclonic circulation region; determining three characteristic parameter grid points in the cyclonic circulation region based on the three characteristic parameters; wherein the three characteristic parameters are the lowest value of the center of the southwest vortex relative to the potential height of the periphery, the highest value of the relative vorticity and the lowest value of the wind speed; determining a potential southwest vortex according to the three characteristic parameter grid points; and determining a southwest vortex in the potential southwest vortexes by using a four-layer wind field algorithm. The method provided by the invention can solve the problem that the traditional method cannot accurately identify the circumfluence situation.

The invention relates to the field of business forecasting, and discloses an intelligent weather forecasting system for numerical mode simulation, which is used for timely adjusting decisions according to business execution states and comprehensively improving weather forecasting accuracy and business decision scientificity, adaptability and real-time performance. The intelligent weather forecasting system based on numerical mode simulation comprises the steps that a cross-scale continuous correction vortex field is generated through spatial fractional order Laplace operation in combination with a bit vortex conservation law, meteorological phase change events such as cyclones and frontal surfaces are recognized through continuous coherence analysis topology, and invalid data are filtered; a Riemannian metric field of meteorological-commercial curvature conservation mapping is constructed based on conformal differential transformation, a decision manifold is constructed in combination with symplectic structure tensor and Gaussian curvature constraint, decision parameters such as a minimum entropy generation path output logistics curvature coefficient and an inventory elastic factor are solved, and real-time commercial data feedback is introduced to realize dynamic closed-loop calibration of the decision manifold. And the decision-making precision and response timeliness of commercial systems such as supply chains and energy sources in extreme weather are remarkably improved.

The invention discloses a tropical cyclone maximum continuous wind speed prediction method based on deep learning. The tropical cyclone maximum continuous wind speed prediction method comprises the following steps: 1) acquiring the maximum continuous wind speed data of a tropical cyclone; 2) dividing the data into a training set and a test set; 3) inputting the training set into a one-dimensional convolutional neural network for training; 4) inputting the feature data output by the one-dimensional convolutional neural network into a long short-term memory neural network for training; 5) determining optimal parameters; 6) obtaining a prediction result of the training set; 7) comparing training effects of the model and a traditional model, and performing error comparison on a prediction result and a test set; and 8) inputting the test set into the model to obtain a future prediction result. The method takes the historical data of the maximum continuous wind speed of the tropical cyclone as an input value, can achieve real-time prediction, has higher prediction precision, can adapt to meteorological data under the influence of the El Nino phenomenon, and has important significance for prediction research of the maximum continuous wind speed of the tropical cyclone under the influence of the El Nino phenomenon.

The invention relates to the technical field of pulverized coal separation, in particular to a cyclone separator, a pulverized coal separation system and a cyclone separator structure optimization method.The cyclone separator comprises an air inlet unit which comprises an air inlet assembly and a separation assembly connected with the air inlet assembly; and the discharge unit comprises an exhaust assembly arranged on the inner side of the separation assembly and a dust discharge assembly arranged at the bottom of the separation assembly. The cyclone separator has the beneficial effects that by arranging the spiral channel, the centrifugal force after dust-containing gas enters can be increased, the dust particle separation efficiency is improved, the longitudinal circulation phenomenon at the top of the separator can be effectively avoided, the influence of a top ash ring is relieved, and mutual interference of airflow in adjacent inner and outer rotational flows is avoided; and the number of particles of large-particle-size dust entering the exhaust pipe through longitudinal circulation is reduced, the flow field can be stabilized, the separation performance is improved, and the separation efficiency of small-particle-size dust is improved.

Disclosed are a tropical cyclone symmetry structure analysis method and apparatus, a device, and a storage medium. TC locations and TC intensities are acquired based on TC best track datasets; polar coordinate systems are created with the TC locations as origins; hourly infrared brightness temperature data are acquired based on satellite data, and are interpolated into the corresponding polar grids to obtain infrared brightness temperature polar grid data; then convection symmetry indexes of inner-core regions of TCs are calculated based on the infrared brightness temperature polar grid data; and finally, the relationship between the convective symmetry indexes and the TC intensities is analyzed.

To properly guide a flow of granular material to a probe in a measuring system for measuring characteristics of the granular material separated from gas by a cyclone separator.SOLUTION: A separation system includes: a cyclone that separates granular material from mixed fluid containing a gas and the granular material; a container that has a truncated conical portion whose diameter gradually decreases downward and is connected to a granular material outlet of the cyclone so that the granular material separated by the cyclone falls while swirling; a conical cone disposed in the container; and a measurement probe that has a measurement unit disposed downstream of a flow of the granular material passing between a lower end of the cone and an inner wall of the container and measures a particle diameter of the granular material passing through the measurement unit.SELECTED DRAWING: Figure 1

The invention discloses a micro-scale turbine blade boundary layer heat-flow double-field synchronous test method and system. The test method comprises the steps that temperature field initial data and velocity field initial data are acquired; based on the initial data of the temperature field and the initial data of the velocity field, the distribution state of the microparticles in the boundary layer is diagnosed, and according to the diagnosis result, the optimal distribution of the microparticles in the boundary layer is obtained by feeding back and controlling the operation parameters of the reverse cyclone separation system; based on the optimal distribution, an improved PIV algorithm is adopted to calculate a boundary layer velocity field, and the improved PIV algorithm is obtained by introducing multistage grid adaptive encryption, physical constraint conditions and a transition area recognition mechanism into the PIV algorithm; a boundary layer temperature field reconstruction model is established, depth phase coupling is carried out on a temperature field reconstruction result and a boundary layer velocity field, synchronous analysis of heat-flow coupling behaviors in a turbine blade boundary layer is achieved, and finally a boundary layer coupling cloud picture reflecting heat-flow interaction characteristics is obtained.

The utility model discloses an atomization rotational flow crystallization device for CO2 resource recovery, which relates to the technical field of recovery of CO2 in combustion flue gas and comprises a spray gas inlet device, a spherical cyclone and a condensation crystallization device. According to the utility model, the atomization rotational flow crystallization device is adopted to realize CO2 resource recovery of flue gas, the flue gas is firstly in full contact with atomization absorption liquid sprayed by the multi-point spraying array assembly to form a flue gas absorption liquid mixture, the flue gas absorption liquid mixture is in a rotational flow state after entering the spherical hydrocyclone, and gas-liquid phase separation is carried out, so that the CO2 resource recovery is realized. The liquid phase is condensed under the condensation action of the condensation crystallization device to obtain a solid-phase product sodium bicarbonate and a liquid-phase absorption liquid, and the liquid-phase absorption liquid is subjected to temperature control adjustment and atomization again to provide an absorption liquid for the multi-point spray array assembly; and the method can also be used for large-scale CO2 resource recovery scenes such as thermal power plants and the like, the process and equipment are stable and reliable, the operation is flexible, and the CO2 absorption conversion rate is high.

A cone or cylindrical tube stack with narrow in-between gap circumferential surface swirling cyclone separator according to the present invention, creating swirling flow by flowing along the convex outer surface of the inner-stacked cone or cylindrical tube. The inner wall of the outer-stacked cone or cylindrical tube, which is a concave surface, serves as the wall for collecting fluid with larger or higher density particles that is opposite to the surface for swirling, which is disposed in accordance with the separation movement of larger or higher density particles that centrifuged from swirling by their inertial force. The distance that the particles travel towards the wall for collecting fluid with larger or higher density particles is short due to the narrow swirling and separating gaps, combined with the flow on the convex curved surface of the cone or cylindrical tube walls which pressure drag is low, this results in higher swirling speeds than those on the concave inner surface of conventional cyclone, significantly increasing the inertial force of particles centrifuged away from the swirling. Consequently, the separation efficiency of the cone or tube stack circumferential surface swirling cyclone separator according to the present invention is much higher than that of conventional cyclone separators. Especially in cases where the cyclone fluid separator according to the present invention is equipped with multi-stage separation, where the final stage utilizes multiple small cyclones, and in configurations where multiple cones are stacked and equipped with device for swirling and counter-flow separation, which convex fluid drainage ridges are installed on the wall for collecting fluid with larger or higher density particles, the ridges curving towards the side that the swirling flow flows to, sloping from the downstream edges of the cones or cylindrical tubes towards the upstream edges of the cones or cylindrical tubes, which counter to the swirling flow along the convex curved surface of the outer circumferential surface of cone or cylindrical tube. When working with a reverse swirling system, enabling to recycle multiple separation within the system before the fluid exits the separator, enabling high separation efficiency for separating various types of fluids mixed into emulsion or separating micron-level dust such as PM 2.5 from the air.

The invention relates to a tropical cyclone strength prediction method and device based on a large meteorological model, and the method comprises the following stages: 1, mapping tropical cyclone strength information to a discrete vector implicit space through an encoder under the conditions of a satellite image and reanalysis of atmospheric field data, a mapping result is compared with all vectors of a learnable discrete codebook, the closest vector is taken and restored to original tropical cyclone strength information through a decoder, and iterative training is carried out on the encoder, the discrete codebook and the decoder; and a second stage: inputting historical meteorological data into the trained encoder and discrete codebook, converting the historical meteorological data into discrete hidden variables, carrying out autoregression iteration to obtain forecast hidden variables, and restoring tropical cyclone strength information through the decoder by taking wind-wind forecast field data as physical constraints to obtain a tropical cyclone strength prediction result. Compared with the prior art, the method has the advantages that long-term forecasting can be achieved, good performance is preserved, and the calculation cost is reduced.

The invention belongs to the technical field of weather prediction, discloses a tropical cyclone ensemble forecasting method, system and equipment based on a depth generation diffusion model and a medium, and aims to improve the accuracy, reliability and uncertainty quantification capability of forecasting by taking the output of a big fortune weather model as a condition and generating a diversified forecasting set by using the diffusion model. The method comprises the following steps: acquiring global ERA5 reanalysis data at a current moment and a previous moment as initial field data of deterministic forecasting, and inputting the initial field data into a big fortune weather model to generate a deterministic forecasting result at a (t + 1) moment; taking the deterministic forecast result as the conditional input of a conditional probability diffusion model, and generating N ensemble forecast members through the conditional probability diffusion model in combination with random noise data and auxiliary condition information with the same dimension as the deterministic forecast result; and carrying out statistical analysis and calculation on probability distribution of tropical cyclone paths and strength based on the N ensemble forecasting members, and generating an ensemble forecasting product.

The invention discloses a typhoon intensity prediction method and device fused with a space-time deep network, and a medium, and the method comprises the following steps: 1), collecting typhoon historical observation data, and obtaining typhoon data; 2) data preprocessing; 3) extracting data features; 4) feature fusion; 5) establishing a typhoon intensity prediction model; 6) model training; and 7) performing prediction by using the trained model, outputting a final prediction value, and restoring a prediction result into an actual typhoon intensity value through reverse normalization processing. The influence of the remote sensing satellite image and the characteristics of the tropical cyclone on the tropical cyclone strength estimation accuracy is considered, the typhoon strength prediction integrated with the spatio-temporal information is carried out, the spatio-temporal representation of the typhoon focus area is enhanced through the integration of the spatial characteristics and the time sequence characteristics of the typhoon, and the accuracy of the strength prediction result is improved.

The invention discloses a continuous multi-stage narrow-granularity-section micron particle and coarse particulate matter grading screening device and a continuous multi-stage narrow-granularity-section micron particle and coarse particulate matter grading screening method. The device is formed by connecting a rotational flow grading module, a purification liquid supplementing module and a filtering concentration module in series, the rotational flow grading module and the purification liquid supplementing module are respectively formed by connecting n hydrocyclones with the same structure and different specifications in parallel, the hydrocyclones of the two modules are in one-to-one correspondence, every two hydrocyclones are connected in series to form a hydrocyclone group, then the n hydrocyclone groups are connected in parallel, and n is equal to the number of target particle size sections minus 1. And through a series-parallel connection structure of the multi-stage cyclone, precise stepless regulation and control and circulating purification of size grading are realized. After being stirred, a solid-liquid mixture is sequentially graded and purified by a cyclone group, and then is concentrated by a cloth bag filter of the filtering and concentrating module. The device is automatically controlled through a PLC, can realize high-precision and high-efficiency grading of micron and coarse particles in a narrow particle size section, and has the advantages of high separation precision, energy conservation, high applicability and the like.

The invention discloses an inland area TRP feature recognition and evaluation method based on data driving and numerical simulation, a medium and a program product, and relates to the technical field of meteorological disaster monitoring and early warning and numerical simulation. And objectively identifying a TRP event which occurs outside the typhoon circulation and is connected with the strong water vapor conveying belt by combining the typhoon peripheral circulation radius, the distance constraint and the whole-layer water vapor conveying flux. Key physical fields such as whole-layer water vapor flux, water vapor income and expenditure, dry invasion and monsoon water vapor surge are diagnosed by utilizing a regional numerical mode and reanalysis data, and a characteristic index set reflecting typhoon, monsoon and dry and cold air coupling influence is formed; on the basis, a data-driven learning algorithm is introduced, a TRP occurrence and rainstorm intensity grading recognition model is obtained through training, a risk assessment product is generated through calibration and risk grading, and therefore the inland typhoon long-distance rainstorm recognition and assessment capacity is improved.

The invention relates to an underground cyclone separator multi-stage parallel device suitable for injection and production in the same well in an offshore oilfield, which comprises a plurality of stages of cyclone separators, the plurality of stages of cyclone separators are sequentially connected from top to bottom, each stage of cyclone separator is provided with a combined sleeve structure, and the plurality of stages of combined sleeve structures are sequentially connected to form an assembled shell. An oil-water mixed liquid inlet channel, an oil transportation channel and a water drainage channel are arranged in the wall of the assembled shell, the multi-stage cyclone separator is connected in parallel with the oil-water mixed liquid inlet channel through an oil-water mixed liquid inlet of the multi-stage cyclone separator, and the parallel hydrocyclones simultaneously feed liquid, simultaneously perform oil-water separation work and simultaneously lift an oil phase upwards; the upper end of a sleeve at the upper end of the first-stage cyclone is connected with a liquid inlet pipe through a bridge type channel; the liquid inlet pipe is communicated with an oil-water mixed liquid inlet channel; the cyclone separator comprises an upper end sleeve, a lower end sleeve, a spiral flow channel, an oil-water separation cone and a bridge type channel. According to the invention, the recovery efficiency of an offshore oil field can be improved, and meanwhile, large-displacement offshore oil-water mixed liquid separation can be continuously treated.

The invention discloses a typhoon center positioning method, device and equipment based on an SAR (Synthetic Aperture Radar) satellite, relates to the technical field of ocean microwave remote sensing, and is used for solving the problems that a typhoon positioning method in the prior art cannot accurately extract the position of a typhoon center and the positioning accuracy of the typhoon center is low. Comprising the following steps: acquiring basic data of an SAR satellite according to typhoon path information; performing sea surface wind speed and wind direction inversion based on the basic data to obtain inversion data; according to the inversion data, a gradient wind equation, a wind profile equation and the maximum wind speed are adopted to determine a high-wind-speed area of the typhoon; according to a cyclonic vortex structure of the typhoon, based on the vertical vortex field of the high-wind-speed area, the horizontal divergence field of the high-wind-speed area and the composite field of the high-wind-speed area, determining the position range of the center point of the typhoon; and positioning a typhoon center meeting preset precision according to the typhoon center point position range. According to the technical scheme, the positioning precision of the typhoon center can be improved.

The invention relates to the technical field of data analysis, in particular to a mangrove forest health assessment system based on tropical cyclones. The structure risk coefficient, the geological risk coefficient and the ecological risk coefficient are combined to obtain the health risk index of the mangrove forest, so that accurate health assessment is realized, meteorological data and cyclonic state data are monitored to predict a cyclonic prediction path, and the prediction accuracy is improved. And then the cyclonic prediction path and the health risk index of the mangrove forest are combined to form risk response management and control on damaged and undamaged mangrove forests, so that accurate judgment and supervision on key areas are formed, and the situation of excessive damage caused by untimely management and control on the mangrove forest is reduced.

The invention belongs to the technical field of oil field associated gas, and particularly relates to an oil well associated gas cleaning treatment device and a working method thereof. The invention provides an oil well associated gas cleaning treatment device which comprises a swirler, a condenser pipe spirally arranged on the outer wall of the swirler in a sleeving mode, and a gas outlet of the condenser pipe is communicated with a gas inlet of the swirler; the interior of the storage device is hollow, the storage device is arranged at the lower end of the cyclone, and the storage device is suitable for collecting a solid-liquid mixture falling into the cyclone; the buffering assembly is rotationally arranged in the storage device and is arranged below the discharging opening of the cyclone; when a solid-liquid mixture separated in the cyclone falls into the storage device, the buffer assembly rotates to slow down the impact of the solid-liquid mixture on the bottom wall of the storage device. And through the arrangement of the buffer assembly, the solid-liquid mixture can be prevented from impacting the inner bottom wall of the collector when falling, and meanwhile, the extension plate rotates to disperse the solid-liquid mixture and prevent the solid-liquid mixture from being accumulated.

The invention relates to the technical field of hydrocyclone design and manufacturing, and provides a hydrocyclone structure optimization method based on machine learning, and the optimization method comprises the steps: carrying out CFD simulation on the motion state of particles in a hydrocyclone; constructing a standardized data set; the correlation between the parameters and the particle rotation speed is analyzed through the Pearson correlation, and weak correlation parameters are removed; designing an integrated prediction model architecture based on a plurality of models, taking the reserved parameters as input, training and testing the parameters, and screening out the model with the optimal performance; embedding the screened model with the best performance into a PYQT5 framework to develop prediction software; angular velocities of the cyclone required in different application scenes are used as input of cyclone particle rotation speed prediction software, and an optimal structure parameter and operation parameter combination of the cyclone is analyzed under the condition that particle dehydration requirements are met through extrapolation operation of the prediction software. And an efficient and visual application tool is provided for performance optimization of the cyclone.

The invention relates to a tropical cyclone composite disaster multi-source data intelligent fusion early warning system, and the system comprises a data collection module which is used for collecting a multi-source data set; the preprocessing module is used for preprocessing the multi-source data set to obtain a standardized multi-source data set; the feature mining module is used for performing spatial feature mining and time feature mining on the standardized multi-source data set to obtain a spatial feature vector matrix and a time feature vector; the fusion module is used for fusing the spatial feature vector matrix and the time feature vector by adopting an attention mechanism to obtain a multi-dimensional fusion feature; the composite disaster prediction module is used for performing trajectory calculation and intensity evolution analysis on the multi-dimensional fusion features, and acquiring occurrence probabilities of rainstorm, flood and storm surge according to trajectory calculation data and intensity evolution data; and the early warning module is used for obtaining a comprehensive early warning result according to the occurrence probability of the rainstorm, the flood and the storm surge. According to the invention, accurate early warning of tropical cyclones and composite disasters caused by the tropical cyclones can be realized.

The invention relates to the field of meteorological research, in particular to a forecasting test method and system for asymmetric rainfall of a tropical cyclone. The method comprises the following steps: step 1, obtaining observed and forecasted tropical cyclone data, and calculating an average observed and forecasted tropical cyclone position; step 2, extracting tropical cyclone rainfall in the observation and forecast rainfall field; 3, calculating relative asymmetric rainfall of observation rainfall and forecast rainfall; step 4, calculating rainfall asymmetry indexes of observation and forecast rainfall; 5, calculating the radius and azimuth angle of the observation and forecast rainfall gravity center; 6, converting the relative asymmetric precipitation field to a target direction coordinate system; and 7, calculating a forecast error. According to the method, the asymmetric rainfall forecast of the tropical cyclones can be qualitatively and quantitatively checked, the difference of the asymmetric rainfall forecast capabilities of the tropical cyclones in different modes, different forecast time periods and the like can be reflected, and a support is provided for improving the rainfall forecast of the tropical cyclones.

The invention provides a pipeline type inrush water magnetic adsorption visual grouting plugging simulation method, which comprises the following steps of: 1, dynamically assembling a modular magnetic rod, sleeving a time sequence functional sleeve, pre-adsorbing a magnetic slurry layer, mounting a long pressure relief pipe with a shunting hole and a rotational flow grouting pipe, and integrally inserting into a pipeline for positioning; 2, high-speed water flow is dispersed through a pressure relief pipe, grouting parameters are dynamically regulated and controlled based on flow state perception, and dynamic balance is maintained through synchronous pressure relief of swirl jet grout; 3, after solidification, scanning through a pressure relief pipe ultrasonic probe, and constructing a three-dimensional density cloud picture to quantitatively evaluate integrity; and 4, the inert film is dissolved by the solvent to activate the lubricating layer, the degaussing coil counteracts the adsorption force, and the hydraulic mechanism axially pulls out the magnetic bar with constant force. The method has the advantages that magnetic flux self-adaption and pressure relief grouting cooperate to solve the flowing water plugging retention problem; high-quality recovery of the magnetic bars is optimized through sequential control; flow state perception and ultrasonic verification double closed loops guarantee the controllability of the plugging quality; the modular fast-assembly architecture supports cross-environment deployment from land karst to a deep sea scene.

The invention discloses a tropical cyclone induced area heavy rainfall event identification and unsteady state characteristic analysis method. The method comprises the following steps: data acquisition; identifying a regional heavy rainfall event; associating a tropical cyclone with a regional heavy rainfall event; identifying the characteristics of regional heavy rainfall caused by the tropical cyclone; analyzing the regional heavy rainfall characteristic change rate caused by the tropical cyclone; unsteady-state frequency analysis of regional heavy rainfall characteristics caused by tropical cyclones is carried out; the action mechanism and the influence of shortening of the recurrence period of a heavy rainfall event in an extreme region caused by tropical cyclone; the method has the beneficial effects that the space-time evolution and movement characteristics of the regional extreme rainfall caused by the tropical cyclone can be fully identified, the change rule of the recurrence period of the severe rainfall event caused by the tropical cyclone in the extreme region is clarified, and the accuracy of forecasting the severe rainfall caused by the tropical cyclone can be further improved; and a scientific basis is provided for resisting disasters caused by tropical cyclones and making decisions.

The invention provides a multi-task tropical cyclone strength prediction method under guidance of physical information, a TC-MulPred tropical cyclone strength prediction model is constructed, and the TC-MulPred tropical cyclone strength prediction model sequentially comprises a feature aggregation module, a backbone prediction module, a feature enhancement module and a feedforward output module. The feature aggregation module performs feature aggregation on an input satellite image through encoding space information and tropical cyclone strength information to obtain global feature representation, and the backbone prediction module predicts the global feature representation based on an encoder and decoder framework to obtain a tropical cyclone strength initial prediction sequence. The feature enhancement module performs data enhancement on the initial prediction sequence of the tropical cyclone strength to obtain an enhanced prediction sequence, and the feed-forward output module maps the enhanced prediction sequence into a tropical cyclone strength index prediction value; in the training process, a physical guidance constraint loss function based on the tropical cyclone air pressure relation is introduced, and the tropical cyclone strength prediction precision is improved.

A method of forecasting a maximum wind intensity associated with tropical cyclones, the method includes identifying a three-dimensional (3-D) field of ocean temperature and velocity, identifying a two-dimensional (2-D) field of sea surface temperature, tropopause temperature, surface level winds, incoming total solar radiation, and outgoing longwave radiation, determining a set of heat fluxes associated with ocean heat, and generating a 2-D map of the maximum potential intensity (MPI) based on (i) the set of heat fluxes and (ii) the first and second sets of data. The method may include training a machine learning model based on the first and second sets of data or the 2-D map of the MPI, and performing, based on the trained machine learning model and the 2-D map of the MPI, a mitigating activity corresponding to anticipated effects associated with the determined upper bound for tropical cyclone wind speed at a geographical location.