134 results about "Meso scale" patented technology

The invention discloses a wind field wind resource fine simulation method, system and device based on WRF-CFD medium and micro-scale coupling and a storage medium, and belongs to the technical field of wind energy resource evaluation.The method comprises the steps that mesoscale meteorological analysis data is collected and calculated through a mesoscale WRF numerical simulation technology; performing fitting processing to obtain a fitting function between the wind speed component and the height; extracting wind speed contour information at a micro-scale modeling boundary based on the mesoscale meteorological analysis data and the fitting function, and determining a boundary condition of a micro-scale CFD model; performing directional calculation and correction on the wind speed of each sector by using a virtual anemometer tower; performing grid division on the wind power plant area to generate a refined grid corresponding to the wind power plant area; and driving numerical simulation of the micro-scale CFD model, and carrying out wind power plant regional range stroke resource evaluation. According to the method, the complex characteristics of the wind field can be comprehensively reflected, the wind resource evaluation precision and the calculation efficiency are remarkably improved, and the resource utilization is optimized.

Resonant sensors for environmental health risk detection are disclosed. A mechanical member may include at least one meso-scale or micro-scale resonator disposed on a surface of the mechanical member. Additionally, the at least one meso-scale or micro-scale resonator may include a plurality of first carbon particles configured to uniquely resonate in response to an electromagnetic ping based at least in part on a concentration level of the first carbon particles within the at least one meso-scale or micro-scale resonator. Further, the at least one meso-scale or micro-scale resonator may be configured to resonate at a first frequency in response to the electromagnetic ping when the mechanical member is in a first state, and may be configured to resonate at a second frequency in response to the electromagnetic ping when the mechanical member is in a second state.

The invention relates to the technical field of ocean information processing, in particular to a mesoscale vortex space-time characteristic analysis method and device based on a neural network, and the method comprises the following steps: obtaining the type, vortex center position, vortex intensity and temperature and sound velocity abnormal characteristics of each layer of mesoscale vortex according to the ocean current three-dimensional speed data of a target region; and the trained neural network predicts the vortex center position, vortex intensity and temperature and sound velocity abnormal characteristics of the mesoscale vortex at the future moment according to the type, vortex center position, vortex intensity and temperature and sound velocity abnormal characteristics of the mesoscale vortex at each layer at the historical moment. The problems that in the prior art, small-scale vortexes or rapidly-changing vortex characteristics are difficult to capture, large-scale data processing requirements are difficult to meet, and robustness is poor are solved.

The invention provides an ocean mesoscale vortex identification method and device, and the method comprises the steps: obtaining the input data of an ocean environment numerical forecasting product, and the input data at least comprise the sea surface height abnormality, the horizontal flow velocity field, the temperature and the salinity; preprocessing the input data to obtain processed marine environment data; recognizing vortexes in the processed marine environment data according to a plurality of preset vortex recognition algorithms to obtain a plurality of vortexes; wherein the preset multiple vortex recognition algorithms at least comprise an Okubo-Wei ss parameter method, a vector geometry method, a rotation invariance method and an SSH isoline method; performing clustering analysis according to the vortex centers of the plurality of vortexes to obtain a target vortex corresponding to the input data; performing feature extraction on the target vortex to obtain feature parameters corresponding to the target vortex; and the feature parameters are displayed according to a preset display mode, so that the recognition accuracy and comprehensiveness are improved.

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 wind power prediction method and system based on mesoscale-microscale dynamics, and the method comprises the following steps: constructing a wind power plant microscale aerodynamic model, setting an inflow boundary condition, generating data according to the inflow boundary condition and the wind power plant microscale aerodynamic model, and constructing a database; constructing a mesoscale-microscale aerodynamic coupling model and a prediction network of the wind power plant; taking the mesoscale-microscale aerodynamic coupling model of the wind power plant as a system environment, training a prediction network by using a database, and inputting mesoscale meteorological prediction data and corresponding inflow boundary conditions into the trained prediction network to obtain a wind power prediction result; compared with an existing method, the method is more beneficial to meeting the requirement of wind power prediction, the reliability of wind power prediction is improved, and the speed and precision of wind power prediction are improved.

The invention provides a mesoscale vortex temperature salt structure reconstruction method based on ocean reanalysis data, and belongs to the field of mesoscale vortex three-dimensional reconstruction. The method comprises the following steps: firstly, identifying mesoscale vortexes by adopting a hybrid identification algorithm of the mesoscale vortexes, then extracting a data set based on an identification result and ocean reanalysis, converting a three-dimensional reconstruction problem of a mesoscale vortex temperature salt structure into a prediction problem of a layer depth sequence, and realizing construction of a mesoscale vortex reconstruction model by virtue of deep learning; meanwhile, mesoscale vortex vertical and horizontal structures are introduced, the mesoscale vortex vertical and horizontal structures are taken as a constraining physical mechanism to be incorporated into a model input module, and an attention mechanism is correspondingly added to assist weight output of the model, so that three-dimensional reconstruction of the mesoscale vortex temperature salt structure is realized. According to the method, the complete mesoscale vortex can be better understood from the perspective of vertical water transportation, one-sided summarization of characteristic properties at the vortex center is made up, and the model shows good adaptability and generalization ability on different data sets.

The present invention belongs to the field of image processing, and discloses a method and system for quickly and efficiently identifying mesoscale convective systems in mid - low latitude regions, including obtaining cloud - top brightness temperature data; constructing a dataset for identifying mesoscale convective systems based on the cloud - top brightness temperature data; constructing a model for identifying mesoscale convective systems; training the model using the constructed training set; after reaching the specified number of training rounds, testing the model with the test set to obtain the final result of identifying mesoscale convective systems. The present invention designs a piece - by - piece learning strategy, enabling the model to simultaneously learn the characteristics of mesoscale convective systems in different regions; constructs a multi - scale feature extractor with semi - residuals to prevent the loss of small - scale features during the feature extraction process; designs a semantic consistency regularization to constrain redundant feature extraction and the feature asymmetry during the feature recovery process. The present invention solves the problem of slow identification speed using traditional threshold methods in mid - low latitude regions.

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 provides an underwater point cloud segmentation method and apparatus, and a program product. The method comprises the steps of obtaining point cloud data and a time sequence; clustering and dividing the point cloud data to obtain a dense region and a sparse region; calculating a first local feature of each point in the clustering clusters of the dense region and the sparse region; screening out a sparse feature point set from the sparse region; obtaining a multi-scale feature set according to the sparse feature point set and the point cloud data; obtaining a 2D feature set according to the time sequence, and obtaining a fusion feature set through the multi-scale feature set and the 2D feature set; generating a second local feature according to the fused feature set; obtaining mesoscale features through the second local features; obtaining global features according to the mesoscale features; obtaining feature representation according to the second local feature, the mesoscale feature and the global feature; and obtaining a category label through feature representation, and mapping the category label to an actual object category. According to the technical scheme, the segmentation efficiency and precision are improved, and the adaptability of the model to the complex underwater target is enhanced.

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 wind-solar power prediction method and device based on spatial-temporal correlation, and relates to the technical field of field power prediction. According to the main technical scheme, the method comprises the following steps: selecting a to-be-tested node object from all node objects in a new energy field; a future micro-scale numerical weather forecast corresponding to the node object to be measured is obtained, the future micro-scale numerical weather forecast is obtained after a future mesoscale numerical weather forecast corresponding to the node object to be measured is subjected to localization downscaling processing, and the future mesoscale numerical weather forecast is output in a local numerical forecasting mode of the new energy field area; and inputting the future micro-scale numerical weather forecast into the power prediction model corresponding to the to-be-measured node object to obtain the future prediction power corresponding to the to-be-measured node object. The method mainly aims at reducing the influence of actual working conditions with complex landforms and changeable weather conditions on new energy field power prediction in a new energy field and improving the power prediction precision.

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 an atmospheric visibility and photovoltaic power forecasting method and device and a storage medium, and belongs to the field of photoelectric new energy, new-generation information technology and atmospheric environment, and the method comprises the steps: constructing an aerosol cluster model with fractal geometric characteristics based on a diffusion limitation condensation algorithm; calculating fractal dimensions of the aerosol cluster model by using a box counting method; according to the fractal dimension, calculating the optical equivalent radius of the aerosol cluster model of the fractal geometric features; coupling the optical equivalent radius into a mesoscale air quality mode, and calculating to obtain an atmospheric extinction coefficient considering the aerosol process; the atmospheric visibility and photovoltaic power forecast result is calculated by considering the atmospheric extinction coefficient of the aerosol process; according to the method, the physical simulation degree and the forecast simulation precision of the atmospheric aerosol optical characteristics by the atmospheric mode are improved, so that the simulation result of the mesoscale air quality mode on the atmospheric visibility and the photovoltaic power is remarkably improved.

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.