108 results about "Meso scale" patented technology

The invention provides a sub-mesoscale flow field extraction method and device based on boundary perception and Poisson constraint. The method provided by the invention comprises the following steps: acquiring stationary ocean water color satellite image data; inputting the continuous satellite image into the flow field extraction model to obtain a continuous feature map; a boundary attention module performs feature extraction and enhancement on a boundary structure of the continuous feature map, and a detail enhancement module performs feature extraction and enhancement on detail features in the continuous feature map to obtain an enhanced continuous feature map; a volume construction module generates a multi-scale pyramid based on the enhanced continuous feature map, determines a search radius based on each scale of the multi-scale pyramid, and constructs a related volume; the circulating flow field refining module is used for carrying out circulating flow field refining on the related volume by utilizing a convolution gating circulating unit, and carrying out iterative updating to obtain a primary flow field; and the physical constraint correction module performs physical constraint correction on the initial flow field to obtain a final sea surface flow field.

The invention discloses a complex terrain wind field multi-scale numerical simulation real-time assimilation method fusing external field measurement data, and the method comprises the steps: obtaining conventional meteorological and topographic data, determining a reference point, and carrying out the simulation of a complex terrain wind field numerical value based on a mesoscale WRF mode; acquiring external field observation data and high-precision terrain data, and improving the mesoscale numerical simulation precision of the complex terrain wind field based on a four-dimensional variational assimilation method by combining the high-precision terrain and the high-resolution grid until a first preset precision requirement is met; establishing a medium-micro scale coupling simulation framework based on WRF-CFD mode coupling, and performing high-fidelity CFD numerical simulation on the target wind field; and applying a four-dimensional variational assimilation technology to the CFD simulation process to improve the CFD numerical simulation precision to meet a second preset precision requirement. According to the invention, the precision of complex terrain wind field multi-scale numerical simulation can be improved.

The invention belongs to the technical field of physical ocean and remote sensing application, and particularly relates to a sub-mesoscale signal extraction method based on Ku / Ka dual-frequency SAR (Synthetic Aperture Radar) height measurement, which comprises the following steps of: acquiring a Ku / Ka dual-frequency sea surface height observation original data set and IMU (Inertial Measurement Unit) attitude and trajectory data; registering the average sea surface and tide height to a space-time sampling position of Ku / Ka synchronous observation and deducting from the original height to obtain an initial sea surface height anomaly sequence of the two channels, and processing the initial sea surface height anomaly data of the two channels to obtain a fused height anomaly sequence; performing attitude coupling mode extraction on the fused height anomaly sequence to obtain a height anomaly sequence after platform error elimination; meanwhile, smoothing processing is carried out to obtain a smoothed sequence; and interpolating the smoothed sequence into a two-dimensional regular grid, performing small-scale secondary smoothing denoising on grid data, interpolating a mesoscale background field at the same time into the same grid, and deducting the mesoscale background field to obtain a sea surface height anomaly data system with sub-mesoscale as a main part.

The invention discloses a floating fan numerical simulation method based on a cross-scale typhoon modeling algorithm, and aims to solve the problems that a typhoon unsteady-state wind field cannot be accurately simulated and the influence of the typhoon unsteady-state wind field on the coupling dynamic response of a floating fan cannot be accurately simulated in the prior art. The method comprises the following steps: firstly, dividing the whole typhoon process into a plurality of typical stages according to meteorological characteristics; for each stage, calculating a time-varying average wind field by adopting a parameterized mesoscale typhoon model, and embedding a three-dimensional turbulence component generated based on a microscale turbulence model to generate a high-fidelity stage wind field file; integrating the wind fields of all stages into a complete and continuous unsteady typhoon wind field input file by using a splicing algorithm; and finally, inputting the wind field file into a pneumatic-hydrodynamic-servo-elastic coupling simulation platform OpenFAST, and carrying out full-coupling dynamic response analysis on the floating fan. According to the method, the prediction precision of the load and motion response of the floating fan system under the extreme typhoon working condition is improved, and a reliable method is provided for typhoon resistance design and safety evaluation of the floating fan.

The invention discloses a grid-adaptive sub-mesoscale hybrid parameterization method and system, and the method comprises the steps: calculating an effective resolution and a frontal scale according to the spatial resolution of an ocean model, carrying out the calibration of a horizontal buoyancy gradient, and calculating an air-sea interface buoyancy flux and a horizontal Akkerman buoyancy flux according to the calibrated horizontal buoyancy gradient; and calculating to obtain expressions of the convective mixing effect and the sub-mesoscale mixing effect, and a vortex viscosity coefficient and a vortex diffusion coefficient of the sub-mesoscale mixing effect. According to the method, the problem that an existing parameterization scheme cannot be applied to a coarse-resolution ocean model at present is solved, and the effect of introducing sub-mesoscale mixing into ocean models with different resolutions is achieved.

The invention relates to the field of meteorological data processing, and discloses a method for generating downscaling data by using mesoscale data and spatial topographic data, which comprises the following steps of: generating a global scale transformation coefficient alpha according to the data resolution a of the mesoscale data in a specified area and the resolution b of the downscaling data specified by a user, and if alpha is greater than or equal to a preset threshold value n, generating a global scale transformation coefficient; if yes, interpolating the mesoscale data by using the high-resolution meteorological observation data and the spatial dynamic weight of the grid to obtain intermediate data with the resolution of m, and m is greater than a and less than b; calculating a terrain complexity index corresponding to each grid of the intermediate data according to a predetermined terrain evaluation feature group, and generating an adaptive intermediate scale transformation coefficient beta corresponding to each grid according to the terrain complexity index, the observation station density and the intermediate scale data quality; according to the beta and the intermediate data, downscaling data corresponding to each grid are generated, and the installation position of the fan is determined and the fan is installed. Therefore, the precision of downscaling data is improved, and the refined requirement of fan site selection is met.

The embodiment of the invention provides a method and device for generating a convective scale atmosphere reanalysis product in a target area, and is applied to the technical field of meteorology. Specifically, according to the embodiment of the invention, an hour-by-hour convective scale atmosphere reanalysis system adaptive to a target area can be constructed based on a high-resolution mesoscale numerical forecasting mode system adaptive to the target area, and then the hour-by-hour convective scale atmosphere reanalysis system is used for reanalyzing collected global hour-by-hour atmosphere products. Processing an hour-by-hour observation data set of a target area, a weather radar networking three-dimensional reflectivity data set, a total satellite cloud amount and an equivalent black body brightness temperature data set; and obtaining a three-dimensional atmosphere reanalysis product, a three-dimensional hydrogel reanalysis product and a three-dimensional reflectivity reanalysis product of the hourly convection scale of the target area and a rainfall forecast product of the target area in the future one hour. In this way, the precision and reliability of reanalysis products in the target area can be improved, and then meteorological refinement research and business requirements are met.

The invention discloses a rainfall downscaling method and system based on a deep learning network model fusing rainfall priori knowledge, and the method comprises the steps: firstly collecting the topographic data and low-resolution day-by-day rainfall data of a target region, and taking the data as input data; a short-term high-resolution precipitation field generated in a mesoscale weather forecast WRF mode is used as training truth value data; according to the method, the function of accurately downscaling the rainfall data in combination with the convolutional neural network and the long and short term memory network is realized, the spatial-temporal correlation of rainfall is fully considered in the downscaling process, and meanwhile, a likelihood function combined with coupled censored data, Box-Cox conversion and time variation variance Gaussian distribution is adopted as a rainfall loss function; the method not only can represent zero expansibility, skewness and heterovariance characteristics of rainfall, but also can improve the rainfall downscaling precision and quantify the uncertainty of rainfall downscaling, and is suitable for wide popularization and use.

A smoke and fire detection method based on multi-scale dynamic receptive field convolution module enhancement comprises the following steps: 1, performing initial feature projection on an input image, and mapping input features to a low-dimensional feature space through parameterized convolution operation; 2, a multi-scale parallel feature extraction architecture is adopted, and detail information and large-range context information are captured at the same time; focusing high-frequency details of flame edges and textures by small-scale convolution; mesoscale morphological characteristics in the smoke diffusion process are captured through mesoscale convolution; global context information is perceived through large-scale convolution; 3, fusing the features extracted by the multi-scale convolution along the channel dimension through feature splicing; and 4, carrying out feature fusion and channel dimension reduction by adopting 1 * 1 convolution. The multi-scale parallel feature extraction architecture can improve the flame and smoke detection precision under the condition that the cost is not changed or reduced, and is suitable for electric vehicle charging stations, intelligent monitoring systems and other scenes needing high-precision and low-delay fire detection.

The invention discloses an ocean mesoscale vortex three-dimensional temperature field inversion method, device and equipment and a medium, and relates to the field of ocean observation and data inversion, and the method comprises the steps: obtaining multi-source ocean observation data; based on the multi-source ocean observation data, performing mesoscale vortex identification, and determining a mesoscale vortex three-dimensional structure; constructing a mesoscale eddy three-dimensional temperature field inversion model by adopting a regression analysis method according to the mesoscale eddy three-dimensional structure based on the multi-source ocean observation data; solving a regression coefficient in the mesoscale eddy three-dimensional temperature field inversion model by adopting a hierarchical least square method; and according to the regression coefficient, the mesoscale eddy three-dimensional temperature field inversion model is adopted to invert a mesoscale eddy three-dimensional temperature field, and dynamic constraints are embedded in the inversion process so as to obtain the temperature of each standard layer of the mesoscale eddy. According to the invention, the precision and reliability of the temperature inversion result of each standard layer of the mesoscale vortex are improved.

The present invention provides a mesoscale convective parameter optimization method and system based on a genetic algorithm, which relates to the field of meteorological forecasting technology. The method comprises: modeling the precipitation evolution state through a neural ordinary differential equation, identifying the dynamical system parameters in combination with variational inference, extracting features using a spatiotemporal heterogeneous graph neural network and a diffusion probability model, correcting parameter thresholds using a physics-guided neural network, and realizing parameter optimization by constructing an optimization objective function through a deep neural network. The method can improve the accuracy of precipitation forecast, reduce forecast errors, and has strong adaptability and generalization capabilities.

A resonant beam accelerometer employing a pair of double ended tuning forks and a proof mass having meso-scale mass as it is formed in the handle layer of a SOI wafer and a method of fabricating same is disclosed. The resonant frequencies of the pair of double ended tuning forks change in opposite directions when an externally applied acceleration is in a direction parallel to the pair of double ended tuning forks. Due to the proof mass having meso-scale mass, the resonant beam accelerometer is very sensitive. By employing a lid and a stator cap, the active portions of the resonant beam accelerometer may be sealed in a vacuum to further increase sensitivity.

The invention discloses a quantitative evaluation model and system for underwater sonar detection efficiency by mesoscale vortexes, and belongs to the technical field of marine acoustic detection. The method comprises the following steps: acquiring marine environment data; identifying vortex types (cold vortex / warm vortex / mixed vortex), positions, radiuses R and amplitudes A based on a velocity gradient method of optimized parameters and a deep learning model; utilizing an ROMS numerical mode to simulate a vortex three-dimensional thermohaline structure; generating a sound velocity disturbance coefficient in combination with a Wilson sound velocity model and the vortex amplitude A; calculating propagation loss PL (z) through a Bellhop sound field model; an acoustic detection efficiency index ADEI is constructed; and outputting the detection probability matrix. According to the method, the technical bottleneck that a traditional model cannot dynamically couple the vortex acoustic effect and the detection probability is solved, full-process automatic evaluation is realized, and a sonar deployment strategy is remarkably optimized.

The invention relates to the technical field of ocean monitoring, in particular to an ocean mesoscale vortex monitoring system and method based on radar images, and the system comprises a data collection module, a preprocessing module, a feature extraction and recognition module, a vortex verification module and a data analysis and storage module. The method comprises the steps of data acquisition and preprocessing, feature extraction and recognition, vortex verification and data analysis and storage. The weak feature recognition capability is improved through an improved edge detection algorithm and a multi-scale analysis method, false vortex false detection is reduced by utilizing priori knowledge and a machine learning algorithm, a data processing flow is optimized, and the computing resource demand of large-range monitoring is reduced. According to the method, the mesoscale ocean vortex can be accurately and efficiently monitored, powerful support is provided for ocean research and ocean resource management, and compared with a traditional method, the weak feature recognition accuracy, the false vortex false detection rate, the monitoring efficiency and the like are remarkably improved.

The invention discloses an ocean mesoscale vortex intelligent identification method and system based on flow field geometric features and a satellite altimeter, and the method comprises the steps: obtaining the synchronous SSHA data of at least three satellites, and carrying out the calibration according to the actual measurement data of a buoy; on the basis of the calibrated SSHA data, utilizing a ground-to-balance equation to calculate weft-direction and warp-direction ground-to-balance flow velocity components; generating a flow field geometric feature VG vortex center criterion; dynamically adjusting VG vortex core criterion parameters according to the ocean dynamic environment; converting the SSHA and geostationary flow field data into a grayscale image, marking vortex polarity and a bounding box, and adding a salt and pepper noise expansion sample through random rotation; resNet-50 is adopted as a backbone network, and a bidirectional feature pyramid network BiFPN is added to optimize multi-scale feature fusion; and inputting the VG preliminary identification result into the trained ResNet-BiFPN model, and outputting the vortex position, radius and polarity. The technical defects of a traditional method in weak vortex recognition, background noise interference and calculation timeliness are overcome.

The invention relates to the technical field of wind resource assessment, and provides a mesoscale mode optimization method and device for wind resource assessment in a complex terrain area, a medium and a product, and the method comprises the steps: obtaining high-precision terrain data and high-precision land utilization data, and putting the data into a WRF for calling; selecting a corresponding parameterization scheme in the WRF according to the resolution of the simulation area, generating an initial condition by the WRF, solving an atmospheric physical change process, and obtaining a simulation result of the WRF; and evaluating a simulation effect based on a simulation result of the WRF, and selecting an optimal parameterization scheme. According to the invention, the simulation capability of a near-ground wind field in a complex terrain can be obviously improved.

The application discloses a large-scale wind farm mesoscale numerical simulation method considering wind turbine interference. The method first obtains the basic data of the target wind farm wind turbine; numerical modeling is carried out on each wind turbine by using the basic data; then in the numerical modeling, the wind turbine sub-grid interference model is added, and the atmospheric inflow conditions of each wind turbine in the same grid are corrected, so that the accuracy of the large-scale wind farm mesoscale numerical simulation is improved. Finally, the modeling results are coupled to the numerical weather prediction model to carry out numerical simulation research of the large-scale wind farm. Compared with the traditional wind farm mesoscale numerical modeling method, the modeling method of the application has better accuracy and robustness, and is more suitable for research on the interference in the large-scale wind farm and the wake characteristics of the whole farm.

This invention discloses a sub-mesoscale process detection method based on prior structural feature embedding. The method first performs outlier removal, contrast enhancement, and normalization on acquired chlorophyll concentration remote sensing data to construct an image dataset suitable for network input. Then, the images are input into the sub-mesoscale process detection network EPSFNet, which uses a multi-stage feature extraction module composed of EPSF blocks to extract multi-scale features, and combines this with a feature pyramid network for feature fusion. The fused features are then processed by a localization-refinement decoupled detection head, which outputs a center prediction heatmap and a boundary distance feature map, respectively, and decodes the results to obtain the detection outcome. Finally, the network is optimized using a joint loss function including Gaussian focus loss, GIoU loss, and hard sample width-height balance loss. This invention significantly improves the detection accuracy and localization stability of sub-mesoscale targets in complex backgrounds by introducing a parallel multi-scale feature fusion module PMF and a bidirectional strip attention module BSA, utilizing the rotational structure prior of sub-mesoscale processes to enhance feature representation and filter background noise.

The invention belongs to the technical field of new energy vehicles, and particularly relates to a hybrid vehicle approximate optimal energy management strategy integrating prediction and control, and the strategy specifically comprises the following steps: (1) constructing a global prediction domain, predicting a global speed curve through employing a bidirectional long-short-term memory network based on traffic data, and obtaining a global speed curve; obtaining a global approximate optimal reference state-of-charge sequence through a dynamic programming algorithm; (2) constructing a mesoscopic coordination domain, performing mesoscale speed prediction by adopting a method of fusing an encoder and a bidirectional long-short-term memory network, and solving a multi-scale information evaluation factor online and performing dynamic correction in combination with a grey wolf optimization algorithm; and (3) constructing a real-time control domain, introducing the evaluation factor to construct a multi-target optimal real-time control framework, and realizing instantaneous optimal mode decision and power distribution. According to the method, the economy, the smoothness and the NVH performance are comprehensively considered, global reference and real-time control are effectively decoupled, and the adaptability of the vehicle to a complex dynamic driving scene and the fuel economy of the whole vehicle are remarkably improved.

This paper discloses a single-variable ultra-short-term wind power forecasting method based on two-level trend decomposition. The method performs multiple trend decompositions on historical wind power time series data to generate macro-trend components, meso-scale trend components, and residual components. The decomposed macro-trend components and meso-scale trend components are each initialized with causal convolution kernels using exponential distributions to extract multi-scale trend features, and adaptive weights with scale normalization are used to maintain temporal causality. A cyclic reconstructed attention mechanism is used to enhance residual modeling, and dynamic features of the residual components are obtained through sequence concatenation and double residual connections. Each component is linearly processed and the results are fused to generate an ultra-short-term wind power forecast value. This method can achieve accurate ultra-short-term wind power forecasting.

The invention provides an ocean mesoscale vortex generation method and device based on three-dimensional convolution, and the method comprises the steps: carrying out the multi-layer vortex recognition of a target ocean region corresponding to first ocean reanalysis data based on the first ocean reanalysis data, obtaining a plurality of first single-layer vortex images, and obtaining a plurality of second single-layer vortex images based on the time-space information of the target ocean region; performing spatial feature matching on the first single-layer vortex image to obtain a target single-layer vortex image corresponding to the target ocean area, and combining the target single-layer vortex image based on the plurality of layered boundaries of the target ocean area and the spatial information of the target single-layer vortex image to obtain a target ocean mesoscale vortex image of the target ocean area, the ocean mesoscale vortex image can be more stereoscopic, the motion phenomenon of ocean current can be reflected more objectively, and the accuracy and reliability of the ocean mesoscale vortex image are improved.

The present application relates to the field of marine information processing technology, and specifically to a method and device for analyzing the spatiotemporal characteristics of mesoscale eddies based on a neural network. The method comprises the following steps: obtaining the type, eddy center position, eddy intensity, and temperature and sound velocity anomaly characteristics of each layer of mesoscale eddies based on three-dimensional ocean current velocity data in a target area; and predicting the eddy center position, eddy intensity, and temperature and sound velocity anomaly characteristics of mesoscale eddies at future times based on the type, eddy center position, eddy intensity, and temperature and sound velocity anomaly characteristics of each layer of mesoscale eddies at historical times. This method solves the problems in the prior art of difficulty in capturing small-scale eddies or rapidly changing eddy characteristics, difficulty in coping with large-scale data processing requirements, and poor robustness.

The application discloses a method for constructing a sound convergence zone of a mesoscale eddy in the ocean based on a neural network, which comprises the following steps: obtaining an underwater sound speed field of the mesoscale eddy under a plurality of eddy parameters and calculating sound propagation loss under a plurality of sound source parameters; performing information coding on samples formed by the plurality of eddy parameters, the sound source parameters and the corresponding sound propagation loss to obtain training samples; building a neural network model, loading the training samples to train the neural network model to obtain an eddy-acoustic neural network model; inputting information coded from eddy parameters and sound source parameters of a target mesoscale eddy into the eddy-acoustic neural network model to predict sound propagation loss and obtain a predicted value of the sound propagation loss; and processing the predicted value of the sound propagation loss by a convergence zone calculation method to obtain sound convergence zone parameters of the target mesoscale eddy. The application aims to obtain the influence of the mesoscale eddy on the sound convergence zone and accurately obtain the variability characteristics of the convergence zone corresponding to the mesoscale eddy.

Electromagnetic manufacturing process for a mesoscopic scale plateau, characterized in that it comprises a forming step and a shaping step, wherein the forming step comprises at least: Step (1): a first workpiece (4) to be formed is arranged opposite a (casting) mold (6) with a distance between the first workpiece (4) and the mold (6), two ends of the first workpiece (4) are restricted by lateral pressing, and a delay block (8-1; 8-2) is arranged on two sides of the mold (6), wherein a height of the delay block (8-1; 8-2) is less than a height of the (casting) mold (6); Step (2): the first workpiece (4) is controlled so that, under the driving force of a uniform electromagnetic force, it tends and accelerates towards the shape (6) in order to be deformed; and Step (3): A central region of the first workpiece (4) initially collides with the mold (6) under the driving force of the uniform electromagnetic force, whereby a region of the two ends of the first workpiece (4) is moved further towards the mold (6), the velocity of the central region of the first workpiece (4) is decelerated to zero, and the forming process is completed when the region of the two ends collides with the deceleration block (8-1; 8-2) and decelerates, and the velocity of all regions of the first workpiece (4) is zero; and the shaping step includes in particular: Step (4): the first workpiece (4) is guided to tend further towards the form (6) under a drive of an electromagnetic force, and proceed to step (5); and Step (5): it is determined whether the first workpiece (4) completely fills the form (6), if so the forming is complete, and if not, the process is returned to step (4).

The application discloses a mesoscale vortex three-dimensional temperature field inversion method, device, equipment and medium, and relates to the field of ocean observation and data inversion. The method comprises the following steps: acquiring multi-source ocean observation data; identifying a mesoscale vortex based on the multi-source ocean observation data and determining a three-dimensional structure of the mesoscale vortex; constructing a mesoscale vortex three-dimensional temperature field inversion model by using a regression analysis method according to the three-dimensional structure of the mesoscale vortex based on the multi-source ocean observation data; solving regression coefficients in the mesoscale vortex three-dimensional temperature field inversion model by using a hierarchical least square method; and inversing a three-dimensional temperature field of the mesoscale vortex by using the mesoscale vortex three-dimensional temperature field inversion model according to the regression coefficients, while embedding a dynamic constraint in the inversion process to obtain the temperature of each standard layer of the mesoscale vortex. The application improves the precision and reliability of the inversion result of the temperature of each standard layer of the mesoscale vortex.

This invention belongs to the interdisciplinary field of numerical meteorology and computational fluid dynamics, and discloses a mesoscale-microscale atmospheric coupling simulation method and related apparatus. The mesoscale-microscale atmospheric coupling simulation method includes: acquiring initial field, lateral boundary conditions, and terrain data; based on the initial field, lateral boundary conditions, and terrain data, performing time integration of the microscale meteorological model within a preset time period to obtain the microscale simulation results for all main time steps; and performing spatiotemporal matching and fusion of the microscale simulation results of all main time steps with the background field data of the mesoscale meteorological model to obtain the mesoscale-microscale atmospheric coupling simulation results. The technical solution disclosed in this invention solves the problem of computational collapse in existing atmospheric coupling simulations due to steep terrain or strong wind shear, and can improve the robustness and success rate of simulation operation while ensuring the physical realism of the simulation.

The present invention provides a downscaling numerical simulation method for wind-temperature coupling of bridges considering real terrain, belonging to the technical field of bridge structure design. It includes: determining the calculation area of the mesoscale model according to the geographical location of the bridge and adopting multi-layer grid nesting to improve the calculation accuracy; obtaining terrain static data and initial meteorological field data and configuring physical schemes; establishing the geometric model of the small-scale model through 3D modeling software; performing fitting block division and extraction point planning according to the morphology of the inlet surface of the small-scale model calculation domain; extracting the inlet wind speed and temperature data through the mesoscale model by using the Cressman interpolation method and fitting by using the least square method to obtain the wind speed and temperature functions of the inlet surface of the small-scale model; loading the geometric model and the wind speed and temperature functions of the inlet surface according to the characteristics of the bridge structure material, establishing the small-scale model by using the CFD method, and solving the environmental wind, temperature field and structural response. The present invention improves the simulation authenticity and reduces the redundancy of the simulation work.

The present disclosure provides a method, device, and electronic device for processing the vertical structure of a mesoscale vortex, which can be applied to the field of underwater observation technology. The method includes: obtaining cross-sectional data of multiple sections of a single underwater glider in a mesoscale vortex; obtaining the vertical density of each of the multiple sections based on the cross-sectional data, wherein the vertical density represents the density of the section at different depths in the mesoscale vortex; determining N cross-sectional segment intervals from the multiple sections based on the vertical density of each of the multiple sections, where N is a positive integer; constructing a multivariate ordinal modal sequence for each of the N cross-sectional segment intervals based on the vertical density difference between adjacent sections in each of the N cross-sectional segment intervals, wherein the multivariate ordinal modal sequence represents the structural characteristics of multiple cross sections of the mesoscale vortex at different depths; and obtaining a visualization result of the vertical structure of the mesoscale vortex based on the multivariate ordinal modal sequence for each of the N cross-sectional segment intervals.

The present application belongs to the technical field of atmospheric dynamic structure identification, and discloses a method for automatically identifying the horizontal scale of atmospheric vortex and matching the vortex with a mesoscale convective system, which comprises the following steps: based on the two-dimensional coordinates of the vortex center of each extended height layer of a three-dimensional vortex at time t, a vortex range identification algorithm is used to identify the vortex range of each extended height layer of the three-dimensional vortex at time t; a vortex range smoothing processing algorithm is used to smooth the vortex range at each time t in the vortex life history time sequence; and it is determined whether the range of the mesoscale convective system coincides with the stable vortex range of any extended height layer of the three-dimensional vortex at the same time t. The present application automatically and accurately identifies the range of a large-scale and long-time atmospheric vortex, solves the problem that the traditional vortex identification cannot determine the horizontal range and the real influence scale, realizes the automatic matching and correlation analysis between the vortex and the mesoscale convective system, and constructs an automatic identification process which can be used in real-time or reanalysis business platforms.