71 results about "Meso scale" patented technology

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 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.

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 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 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 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.

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 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.

This invention provides a design method for additive manufacturing lattice-filled support structures based on multi-scale concurrent topology optimization, belonging to the field of additive manufacturing and structural optimization design technology. Targeting the layer-by-layer forming and overhanging characteristics of additive manufacturing, this method uses the unit density of the support structure's topological layout as the design variable at the macro scale and the geometric parameters of the lattice unit cells as the design variable at the meso scale. A multi-scale concurrent optimization model is constructed with the objective of minimizing part deformation under comprehensive loading conditions, and volume fraction constraints, overhang retention constraints, and support removability constraints are introduced. Through the synergistic optimization of macro and meso design variables, the method achieves efficient solution to the multi-scale coupled optimization problem, obtaining a lattice-filled support structure that can effectively control part deformation and meet the requirements of additive manufacturing processes. This method is suitable for the design of support structures for large-scale complex parts, reducing manufacturing costs and enhancing engineering application value.

Techniques for operating a wind farm include setting an area of interest, a forecast interval, and a maximum lag time for using mesoscale forecasts. Mesoscale forecasts are collected for a training time interval TT at model grid locations. TT is at least ten times the maximum lag time. Fine-scale wind measurements are collected in the area during TT. Selected parameters of the mesoscale forecasts, and coefficients of an evolving ML forecast model are determined based on the mesoscale forecasts and the fine-scale wind measurements during the TT ending at the current time. Then, the coefficients and the mesoscale forecast for the selected parameters during the lag time produce a forecast wind at the wind turbines during the forecast interval. Operation of the wind farm is based on the forecast wind.

The application discloses a kind of electricity theft detection methods based on multi-scale graph calculation model, first constructs micro, meso, macro three-level graph structure: micro scale is with user and electricity meter as core, is modeled by affiliation and load association edge;Meso scale is with block equipment as core, is modeled by physical connection and power transmission edge;Macro scale is with regional power grid node as core, is modeled by connection and scheduling edge;For three-level graph respectively, time series graph convolution network, graph attention network, graph convolution network is extracted multi-scale feature, realizes user-level spatiotemporal correlation, block-level equipment weight distribution, regional-level topology aggregation hierarchical capture;Through two-stage attention mechanism, cross-grain alignment and weighted fusion are generated global fusion features;Based on multilayer perception machine binary classification model, electricity theft detection model is trained, and global fusion features are used as input, finally output electricity theft probability and trigger alarm based on threshold value.The method can realize efficient and accurate detection of various electricity theft behaviors.

The invention discloses a meso-scale convection system wind speed and rainfall extreme value tracking method based on station matching. The method comprises the following steps: reading grid point data marked with an MCS number and station data containing station information; generating a closed polygon representing the range of the MCS for the MCS of each number at each time; judging whether the site is located in the closed polygon or not; based on the matching result, according to MCS serial numbers and time grouping, the maximum value of wind speed and / or rainfall at each time in the life history of each MCS is obtained, and corresponding station serial numbers, positions and moments are recorded; and finally, obtaining the maximum wind speed and / or rainfall data in the whole life history of each MCS, and recording the number, the position and the moment of a station where the maximum value appears. According to the method, polygon matching and MCS full life history extreme value tracking are integrated, and efficient, accurate and continuous automatic tracking of the wind speed and rainfall extreme values in the MCS is achieved.

The invention discloses a mesoscale eddy detection method and system based on an improved RT-DETR network structure. According to the method, firstly, a multi-source high-resolution SAR satellite image is preprocessed through a fast non-local mean denoising algorithm, so that the data quality is effectively improved; secondly, aiming at the problem that the boundary of a scale vortex is fuzzy in an SAR image, a feature extraction network, a feature fusion network and a loss function of an end-to-end target detection model RT-DETR are improved, and based on the improved RT-DETR network structure, the capability of capturing vortex features of different scales is enhanced through a multi-scale feature fusion module; the innovatively designed Inner-MPDIOU loss function remarkably improves the bounding box regression precision, the problem of detection of the fuzzy vortex boundary in the SAR image is solved, and high-precision and high-efficiency mesoscale vortex detection can be achieved through the method.

The invention provides a severe convective weather multi-scale intelligent fusion seamless forecasting method and system, and relates to the technical field of weather forecasting, and the method comprises the steps: obtaining multi-source meteorological data which comprises observation data, mesoscale numerical mode data and weather scale numerical mode data; performing cascade scale decomposition on the multi-source meteorological data to obtain a small-scale feature component, a medium-scale feature component and a weather scale feature component; cross-scale features are extracted; the method comprises the following steps: acquiring cross-scale features, coding forecast timeliness and geographic area information into embedded vectors, performing adaptive dynamic fusion on the embedded vectors and the cross-scale features, and outputting a severe convection weather forecast result according to a fusion result, so that the recognition accuracy of a small-scale convection system is improved, cooperative processing and space-time perception of the multi-scale features are realized, and the recognition accuracy of the severe convection weather forecast is improved. The prediction precision of the full life cycle of severe convective weather inception, development and attenuation is improved, full-time seamless prediction is achieved through the dynamic fusion strategy, and reliable support is provided for severe convective weather early warning.

This invention relates to the field of environmental monitoring, and more particularly to a soil environmental monitoring method and system based on multi-level sampling. The soil environmental monitoring method provided by this invention includes the following steps: acquiring a GIS model of a target area; based on the GIS model of the target area, deploying a first-level data acquisition module within the target area at a macro scale, and using the first-level data acquired by the first-level data acquisition module to identify at least one abnormal sub-area within the target area; based on the GIS model of the abnormal sub-area, deploying a second-level data acquisition module within the abnormal sub-area at a meso scale, and using the second-level data acquired by the second-level data acquisition module to identify at least one abnormal point within the abnormal sub-area; based on the GIS model of the abnormal point, deploying a third-level data acquisition module at each abnormal point at a micro scale, and using the third-level data acquired by each third-level data acquisition module to identify the soil condition at each abnormal point.

The present application relates to a kind of buried hill reservoir's multiscale fracture zone prediction method, device, medium and equipment, utilize Gaussian curvature attribute to predict macro scale buried hill weathering fracture dominant enrichment area, utilize f-k transform technique to depict buried hill inside high angle fracture, predict large scale structural fracture enrichment area, utilize ant body attribute to predict buried hill mesoscale fracture zone enrichment area, based on wavelet transform technique, extract the low frequency and high frequency components of three scale fracture zone prediction attributes of curvature attribute, f-k transform attribute, ant body attribute, design low frequency end and high frequency end fusion rule, realize the fusion of three scale fracture zone prediction attributes.The method of the present application can comprehensively represent the development characteristics of buried hill multiscale fracture network, and then guide reserves production and well pattern design.

The application provides a street block scale atmospheric pollution simulation and tracing method and system, the method comprising: obtaining a street block scale dynamic field based on mesoscale meteorological data by using a comprehensive simulation strategy of machine learning model and parameterized model fusion; analyzing street block scale emission data into pollution source characteristic information; based on simulation grid data, mesoscale pollution distribution and departmental contribution data, precipitation data, street block scale dynamic field and pollution source characteristic information, performing collaborative processing to obtain street block scale pollution distribution data; based on the street block scale pollution distribution data and the interaction mechanism of pollutants and environmental elements to generate a correction signal to dynamically adjust the collaborative processing process; outputting street block scale pollution distribution and tracing result data. The application can reduce the consumption of computing resources and simulation time while ensuring high-precision simulation of wide-area coverage, and has the ability to simulate the interaction of pollutants and environmental factors, which can improve the authenticity of street block scale pollution simulation.

A system and method are disclosed for implementing a multiscale persistent cognitive fabric that achieves scalable, long-term cognition through geometric coupling of structure and time. The system includes interconnected cognitive manifolds-fast, mesoscale, and slow-linked by fibers that transmit curvature, bias, and coherence information across scales. The architecture regulates curvature and compression pressure according to activity level, enabling logarithmic scaling of computational cost and transitions between noise, flow, coherence, generative, and doctrinal regimes. A curvature-exchange field couples cognitive and temporal manifolds to maintain equilibrium between cognitive and temporal processes, sustaining cognition across restarts and temporal discontinuities. Curvature regulators maintain stability and form shielded core domains that localize curvature into persistent attractors representing long-term memory and schema structures. Through continuous internal activity and feedback, the system achieves autonomous metabolic maintenance, scalable reasoning, and enduring cognitive persistence.

A scaling method for simulating any manufacturing process employing a moving heat source is disclosed. The method is aimed at melting or sintering materials where the heat source is driven according to a defined path. The method requires a mesoscale model that evaluates physical quantities representative of the process-induced thermal history and residual stress and strain fields for each set of process parameters for a given material. The mesoscale results obtained by modeling one or more scan lines are transferred to the elements of a macro-scale finite element mesh based on the defined path. Scaling is performed point-by-point and subsequent averaging operations are performed on the values of the physical quantities calculated within each element of the macro-scale finite element mesh. Finally, a macro-scale simulation is performed for evaluating the residual stresses and deformations produced throughout the manufacturing process.

The present application belongs to the technical field of physical ocean and remote sensing application, in particular to a sub-mesoscale signal extraction method based on Ku / Ka dual-frequency SAR height measurement, comprising the following steps: obtaining Ku / Ka dual-frequency sea surface height observation original data set and IMU attitude and trajectory data, registering average sea surface and tidal height to the space-time sampling position of Ku / Ka synchronous observation and deducting from the original height, obtaining two-channel initial sea surface height anomaly sequence, and processing the two-channel initial sea surface height anomaly data to obtain a fused height anomaly sequence; performing attitude coupling mode separation on the fused height anomaly sequence to obtain a height anomaly sequence after removing platform error; simultaneously performing smoothing processing to obtain a smoothed sequence; interpolating the smoothed sequence to a two-dimensional regular grid, performing small-scale secondary smoothing denoising on the grid data, and deducting the mesoscale background field at the same time after interpolating the same grid to obtain a data system of sea surface height anomaly mainly in sub-mesoscale.

The invention discloses an objective identification method for a sea entering process of a mesoscale convection system. The objective identification method comprises the following steps: acquiring metadata of a full life cycle of the mesoscale convection system; forming a water surface coverage rate time sequence of the mesoscale convection system based on the high-resolution sea-land mask data; the method comprises the following steps: classifying a mesoscale convection system into two source land types of'land origin 'or'ocean origin' according to an initial section feature of a water surface coverage rate time sequence of the mesoscale convection system; and for the two source land types, respectively setting judgment rules based on a threshold value and a time sequence continuity condition, and identifying key time nodes of'entering the sea 'and'logging-in' processes opposite to the'entering the sea 'in the water surface coverage rate time sequence. According to the method, the traditional subjective judgment depending on artificial experience is replaced on the basis of quantitative indexes of high-resolution physical data and clear logic rules, so that the identification result of the MCS sea-land transition process is objective, accurate and completely repeatable.