218 results about "Temporal resolution" patented technology

The invention discloses a two-dimensional wave spectrum intelligent forecasting method based on a physical information neural network, and belongs to the technical field of ocean information prediction.The two-dimensional wave spectrum intelligent forecasting method comprises the steps that basic data are collected, and the basic data comprise high-temporal-spatial-resolution sea surface wind field data and sea wave height data within the research range; historical sea wave data are obtained, ocean and meteorological data closely related to sea wave changes are integrated and processed, and a sea wave forecasting database is constructed; physical constraints are determined according to the sea wave forecasting database; an ANN neural network model is adopted as a basic model, physical constraints are added, the neural network meets the control equation of the mode, and a PINN physical information neural network is constructed; and carrying out interpretability analysis on the model forecasting process and result. According to the method, the sea wave dynamic process and the deep learning method are fused, interpretability is added for deep learning, and the black box problem of the deep learning method is solved.

The invention relates to the technical field of digital twinning, in particular to a digital twinning fused power grid fault prediction system and method, and the system comprises a state sensing module, a path deduction module, a twinning mapping module and a prediction execution module. According to the method, multi-source heterogeneous sensing data are fused, a space-time alignment algorithm is executed to construct a structural state tensor, Laplacian operation is combined with a stress wave propagation model to analyze non-rigid deformation characteristics, a load transfer path atlas is generated to reveal recessive mechanical association, Young modulus is dynamically corrected through a finite element grid to form a structural degradation coefficient matrix, and the structural degradation coefficient matrix is obtained. The time sequence difference and the long short-term memory network are combined to capture the equipment degradation trend, the Kolmogov test is used to quantify the fault space-time distribution probability, the power grid equipment jumps from single threshold early warning to multi-physics coupling prediction, the fault prediction space-time resolution and dynamic adaptability are improved, the artificial experience dependence is reduced, and the fault prediction efficiency is improved. And the robustness of the complex working condition system is enhanced.

The invention relates to the technical field of deep and far sea small island health monitoring, and discloses a deep and far sea small island health monitoring system and method. A non-intrusive data acquisition system composed of a sensor network, a shore-based camera device and a vibration sensor is adopted, and total-factor real-time monitoring of open sea environment parameters, reef images and the vibration state of a protective building is achieved. Through joint operation of a wave field distribution model and a structural damage prediction model, actually measured data and numerical simulation are combined, and the temporal-spatial resolution of wave parameters of the reef and the evaluation precision of the health state of a protective building are improved; the early warning threshold value is dynamically adjusted through deviation analysis of real-time association of historical disaster data and the current monitoring value, and the timeliness and accuracy of early warning of extreme events are improved; finally, through an automatic triggering mechanism of a multi-stage early warning signal, all-weather and high-reliability safety monitoring guarantee is provided for deep and far sea islands, meanwhile, the manual maintenance cost is reduced, and a remarkable technical synergistic effect is achieved.

The invention discloses a flow field measurement system and method fusing event data and a blurred image, and the method comprises the steps: turning on a sheet-shaped light source, and enabling the sheet-shaped light source to illuminate tracer particles in a gas flow field; starting a gas flow field to enable the gas flow field to carry the tracer particles to flow through the transparent box body; capturing the motion change of the tracer particles in real time through an event camera, and collecting event data; blurred images of the tracer particles are synchronously collected through an industrial camera; based on the event data and the blurred image, reconstructing a clear moving image at any moment by using an image deblurring technology EDI; and carrying out time synchronization and space alignment on the clear moving image and the high-resolution static image, and extracting flow field information in the gas flow field in combination with the movement track and brightness change of the particles. According to the method, the high-speed dynamic capture capability of the event camera and the high spatial resolution of the industrial camera are combined, the bottleneck of the spatial-temporal resolution in the traditional flow field measurement technology can be effectively overcome, and meanwhile, the method has a relatively good effect in a complex flow field and a high-speed dynamic scene.

An XR system is disclosed having a night mode to optimize performance in low light conditions. The system estimates a user's dark adaptation level based on captured image data and selects a rendering configuration aligned with the user's vision. For example, spatial resolution, temporal resolution, color mode, and brightness are adjusted based on whether the adaptation level is scotopic, mesopic, or photopic. With eye tracking, foveated rendering is used for cones and periphery rendering for rods. Without eye tracking, modes with different wavelengths and colorization are employed. The night mode aims to maximize information visible on the display, conserve power by only showing what eyes can perceive, and preserve night vision by avoiding over-stimulating rods.

The invention discloses a thunder approaching prediction method and device based on multi-source meteorological data, and particularly relates to the technical field of thunder disaster prediction.The method comprises the steps that standardization processing of temporal-spatial resolution unification is conducted on meteorological satellite data, radar data and lightning positioning data, and standardized temporal-spatial input data is generated; then spatial-temporal features are extracted through a depth separable 3D convolution module, and a compressed spatial-temporal feature graph is generated; then, a channel-space double attention mechanism (CPCA) is applied to the compressed feature map for feature optimization; and finally, processing the optimized feature map through a coding-decoding structure, and outputting a thunder and lightning probability distribution map. According to the method, the problem of spatial-temporal resolution difference and physical feature mismatching in multi-source data fusion is innovatively solved, the calculation efficiency is remarkably improved through a lightweight network architecture, the feature expression ability is enhanced by using an attention mechanism, and high-precision prediction of sudden thunder and lightning events is realized.

The invention relates to the technical field of remote sensing data processing and water environment monitoring, and discloses a black and odorous water body intelligent identification method based on a multi-source remote sensing image, and the method comprises the steps: firstly obtaining a first high-resolution remote sensing image, and extracting a global water body mask through a TransUNet model; secondly, removing non-target plaques based on geometric features and a basic spectrum rule; then, calculating a black and odorous water body index by using the second multispectral remote sensing image, and screening suspected black and odorous water bodies by combining adaptive threshold segmentation; further, performing fine classification confirmation on the suspected target by using a third high-frequency remote sensing image and an Officient Net model; and finally performing fusion output on the identification result. Through multi-source data collaboration and a coarse screening-fine classification multi-stage strategy, the limitation of insufficient spatial-temporal resolution of a single data source is overcome, and the positioning precision and the anti-interference capability of urban black and odorous water body recognition are improved.

A high temporal-spatial resolution vegetation index fusion method based on a multi-source optical satellite image comprises the following steps: firstly, performing radiometric calibration, atmospheric correction and geometric fine correction on Landsat, Sentinel-2 and MOD09A1 data, and unifying temporal-spatial resolution to 10m / 8 days; pixel-level fusion is carried out by adopting an improved continuous correction method, a correction coefficient K is introduced to compensate Sentinel-2 critical period data defect influence, and fusion precision is improved through dynamic weight adjustment; and finally, a continuous and smooth EVI time sequence is constructed by using cubic spline interpolation and Savitzky-Golay filtering. According to the method, single-source data space-time limitation is broken through, after fusion, the vegetation index spatial resolution reaches 10 m, the time resolution reaches 8 days, the key phenological period extraction error is smaller than or equal to 3 days, the crop classification precision is larger than or equal to 90%, the accuracy and continuity of farmland-scale vegetation monitoring can be remarkably improved, high-precision data support is provided for agricultural application such as crop growth assessment and water resource management, and the method is suitable for popularization and application. The method is suitable for cloudy and rainy areas and various crop types.

The invention relates to the technical field of satellite navigation and positioning, in particular to a zenith troposphere wet delay calculation method based on deep learning, and the basic steps of segmented ZWD multi-source data fusion based on an improved Transform model are as follows: establishing a ZTD model, establishing a TCA-Transform model, and fusing data. The task of establishing the ZTD model is to calculate the atmospheric refractive index according to meteorological data so as to obtain ZTDPP; the TCA-Transform model establishment comprises the following steps of: improving a Transform model by using a domain self-adaptive method; according to the data fusion, the ZWDPP and the ZWDMODIS are fused by using a TCA-Transform model. And finally, a ZWD product with high temporal-spatial resolution and high precision can be obtained. The generalization ability of an original Transform model is improved, so that the troposphere delay correction ability in a specific area is improved, and a zenith troposphere delay (ZWD) product with high resolution and high precision is obtained.

The invention relates to a weather radar beam shielding rate calculation method based on temporal-spatial resolution improvement, belongs to the field of radio, realizes adaptive precise temporal-spatial matching of meteorological unmanned aerial vehicle sounding data and ECMWF forecast data under different weather echo types, realizes calculation of spatial distance in grids in an expansion field, and improves the weather radar beam shielding rate. The method realizes preliminary extraction of spatial-temporal characteristics, constructs a self-attention mechanism model fused with physical perception, successfully generates a high-spatial-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, realizes generation of the high-spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set, and improves the spatial-temporal-resolution atmospheric temperature, atmospheric relative humidity and atmospheric pressure data set. Accurate calculation of the beam ray propagation path of the high-temporal-spatial-resolution radar is achieved, calculation of the shielding orientation and the maximum elevation angle of a shielding object in each orientation ray beam block is achieved, finally, a beam shielding power integral calculation model is introduced, and accurate calculation of the weather radar beam shielding rate is successfully achieved.

The invention provides a power grid equipment large-range progressive real-time flood forecasting method based on a three-water-source Xinanjiang model, which abandons a basin homogenization hypothesis of a traditional lumped model, divides a target basin into subunits with spatial uniqueness and topological connectivity through a Thiessen polygon method, and accurately matches underlying surface spatial differentiation features; according to the method, the limitation of single parameter calibration is broken through, an NSGA-I I multi-objective optimization algorithm is adopted to be combined with historical flood data, a parameter optimization system containing multiple indexes such as a flood peak flow error and a peak present time error is constructed, a Pareto optimal solution set is generated, a parameter knowledge base is constructed in a classified mode, and the problem of poor parameter adaptability is solved; a geographically weighted regression method is used for fusing satellite remote sensing, ground rainfall station and hydrometric station data, a dynamic correction mechanism is established, model input is updated every 30 minutes, and the rainfall runoff calculation temporal-spatial resolution is remarkably improved; after subunit outlet flow riverway convergence calculation is completed based on a Muskingum method, power grid equipment space distribution and vulnerability threshold values are innovatively associated.

The invention relates to a geological disaster early warning algorithm based on hyperspectral and Internet of Things data fusion analysis, and relates to the technical field of geological disaster monitoring and early warning, the geological disaster early warning algorithm comprises the following steps: S1, preprocessing hyperspectral data and Internet of Things data, and respectively extracting spectral-spatial features and dynamic topology time sequence features; s2, fusing the spectrum-space features and the time sequence features to generate cross-modal joint features; s3, performing parameter optimization on the fusion features through a quantum-classical hybrid optimization algorithm, and calculating a disaster risk probability; and S4, based on the optimization result and the risk probability, executing an edge-cloud collaborative early warning decision. According to the method, through non-negative tensor ring decomposition of the hyperspectral data and dynamic topology modeling of the Internet of Things, the limitation of a traditional single data source in temporal-spatial resolution and physical relevance is solved, multi-dimensional joint extraction of spectrum-space-mechanical characteristics can be realized, and the characterization precision of a rock-soil body deformation evolution law is remarkably enhanced.

The invention belongs to the technical field of meteorological safety, and discloses a vertical gradient artificial intelligence recognition algorithm for low-altitude meteorological potential safety hazard airspace which is not encountered in n years in history. Comprising a multi-source data preprocessing module, an extreme vertical structure anomaly threshold extraction module, a small sample extreme recognition deep learning module, a vertical gradient risk level evaluation and interpretation module and a rolling retraining and real-time forecasting module. The low-altitude meteorological potential safety hazard airspace vertical gradient artificial intelligence recognition algorithm has the following advantages that (1) the spatial-temporal resolution is high, and real-time response is realized; (2) systematized vertical gradient sensing; (3) learning a priori threshold value and a small sample oriented to an'encountering once in n years' extreme event; (4) interpretable risk grading and trigger factor description; (5) performing dynamic self-adaption and online iterative updating; and (6) end-to-end closed-loop pushing is compatible with multiple platforms.

Snapshot phase-shifting diffraction modules and associated systems and methods are described that enable high spatial and temporal resolution phase imaging with high immunity to environmental factors such as vibrations and temperature changes. One example optical diffraction phase module includes a polarization grating to produce two circularly polarized light beams with opposite polarizations, a first lens to receive the two circularly polarized beams, and a spatial filter positioned at a focal plane of the first lens. The spatial filter includes two openings, one to spatially filter one of the two circularly polarized light beams, and another opening to allow another circularly polarized light beam to pass. The module also includes a second lens to focus the received light onto an image plane and to enable a phase measurement based a plurality of interferograms. The phase module can be incorporated into a microscope system that operates a reflection or a transmission mode.

The invention provides an atmospheric correction method and device for a solar zenith angle lower coastal water area radiation signal. The method provided by the invention comprises the following steps: generating noon remote sensing reflectivity satellite data based on observation data of a coastal water area by utilizing an ultraviolet-near infrared atmospheric correction combination model; utilizing a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data based on the spatial resolution and the time resolution of the data; calculating a Rayleigh correction radiance based on a Rayleigh scattering lookup table corresponding to the observation data, and matching the Rayleigh correction radiance with the adjusted noon remote sensing reflectivity satellite data based on time information and position information to obtain a training set; and inputting the to-be-corrected coastal water area radiation signal into the model trained based on the training set, and determining the off-water radiance of the radiation signal. According to the method and the device provided by the invention, atmospheric correction of a coastal water area radiation signal at a high solar zenith angle is realized for a turbid coastal water area.

The invention discloses a one-way door control method, system and equipment based on a ground sensing grating, and belongs to the field of one-way doors. According to the embodiment, vibration spectrum data including position information and frequency domain energy distribution information of a vibration sensor and passing direction data including an object moving direction, a position sequence and ground sensing grating layout information in a passing area are obtained; determining time sequence fluctuation information corresponding to the abnormal behavior based on the frequency domain energy distribution information and preset frequency domain energy distribution characteristics associated with the passing behavior; obtaining space-time correlation information according to the position sequence and the layout information, and dividing the position sequence based on the layout information according to a preset space-time resolution to generate space-time grid distribution; generating judgment parameters based on the space-time correlation information, the object moving direction, the position information, the space-time grid distribution and the time sequence fluctuation information; and comparing the judgment parameter with a preset threshold range to generate a control instruction for the one-way door, so that the one-way door can be accurately controlled under the interference of a complex environment.

The present invention provides a Transformer-based method for predicting floods over varying time periods. The method comprises obtaining and preprocessing hydrological and meteorological data for equal time periods; constructing and training a Transformer model; redistributing water volume over varying time periods in the watershed; and, for each time step, inputting the redistributed rainfall data into the trained Transformer model to obtain a predicted runoff sequence. The method can automatically identify rainfall events across time periods and generate new, unequal-time rainfall sequences based on the maximum short-duration rainfall. The Transformer model is used to generate unequal-time flood sequences with higher temporal resolution. The method can more accurately reflect the relationship between maximum rainfall and flood peaks, improving the accuracy and reliability of flood simulations and providing stronger technical support for disaster prevention and mitigation.

The invention provides an airborne high-precision imaging system and method for a single-photon unmanned aerial vehicle, and relates to the field of airborne imaging systems, and the system comprises a jitter collection unit for real-time attitude sensing, a signal collection and emission unit for high-speed transceiving control, a two-dimensional scanning unit for spatial scanning, and a motion compensation unit for real-time data processing. And the main control unit is used for overall planning of global time sequence control. The signal acquisition unit adopts a transmitting-receiving integrated coaxial optical structure, and a dynamic light leakage suppression mechanism is introduced. The jitter acquisition unit is integrated with a high-frequency inertial measurement unit IMU, the sampling frequency of the jitter acquisition unit is higher than the laser pulse frequency through a compensation algorithm, and the three-axis attitude and deflection angle data of the airframe in a dynamic environment are acquired in real time with high time resolution. The motion compensation unit carries out the registration of an IMU coordinate system, a laser radar coordinate system and an earth fixed coordinate system, solves the instantaneous attitude of each laser pulse receiving and transmitting moment, carries out the calibration of the position information of a photon event, and solves the point cloud motion distortion.

The invention relates to a multi-source meteorological data fusion technology, and discloses an air temperature reconstruction method fusing data of a micro-meteorological device, which improves the temporal-spatial resolution and precision of ground temperature under a complex terrain. The method comprises the following steps: densely deploying micrometeorological devices in a complex terrain area to obtain high-frequency observation data, carrying out quality control, and carrying out hierarchical processing in two dimensions of space and time by taking pattern forecast grid point data as an initial background field: in the spatial dimension, dynamically updating a fusion weight for grid points with observation stations by using geographical weighted regression, and carrying out data fusion; a residual machine learning model is combined with multi-topographic feature correction for grid points without observation stations, and a high-precision space fusion background field is generated; in the time dimension, errors after space fusion are decoupled into a trend term and a periodic term, an autoregressive integral moving average model is used for predicting a trend, a Fourier algorithm is used for correcting a periodic phase, and then time dimension machine learning correction is carried out on a grid point of an observation-free station. And finally, complete-process automatic, high-temporal-spatial-resolution and low-error complex terrain area air temperature reconstruction is realized.

The invention belongs to the field of land surface temperature reconstruction, and relates to a high-spatial-temporal-resolution land surface temperature reconstruction method used in a cloud environment, which comprises the following steps: calculating a first reference land surface temperature and a first land surface temperature residual error of a clear sky pixel under a low spatial resolution according to a first land surface temperature annual change model; seamless surface reflectance data under high spatial resolution are obtained; defining a relative solar radiation index based on the digital elevation model data with high spatial resolution; screening clear sky pixels participating in modeling of the XGBoost model; an XGBoost model is adopted, and a plurality of model relations between a low-spatial-resolution land surface temperature annual change model coefficient and a land surface attribute and between a day-by-day land surface temperature residual error and the land surface attribute are constructed; obtaining a high-spatial-resolution surface temperature annual change model coefficient and a day-by-day surface temperature residual error based on the constructed multiple model relationships, and obtaining a high-spatial-resolution daily surface temperature; the accuracy and the stability of a surface temperature reconstruction result are improved.

The invention provides an atmospheric humidity profile inversion method based on a Unet3 + neural network. The atmospheric humidity profile inversion method comprises the following steps: constructing an atmospheric humidity profile inversion model based on Unet3 + MHS microwave data; performing radiometric calibration, projection conversion and quality control on the multi-channel brightness temperature observed by the original satellite; the method comprises the following steps: selecting long-time scale radiation brightness temperature data in a Chinese area range and specific humidity data of ERA-5 corresponding to time and space to make a data set; and performing humidity profile inversion according to the humidity profile inversion model of the brightness temperature data constructed in the step S1. The method has the advantages that the Unet3 + model in deep learning and microwave sensor data which are not affected by time and weather are combined, and the problems that the space distribution of the atmospheric humidity profile is small, and the time resolution is low can be further solved; the method provides atmospheric condition data with higher temporal and spatial resolution for numerical weather forecast research, and has good application value.

The invention discloses an underwater target detection method and system based on multiband sonar data generation, and the method comprises the steps: generating synthetic sonar data through real sonar data, and carrying out the expansion training of an underwater target detection model through a reasonable real sonar data and synthetic sonar data mixing strategy. The generative adversarial network for generating the synthetic sonar data comprises a generator and a discriminator. And the generator is used for receiving the Mel spectrogram corresponding to the real sonar data, realizing multi-scale feature capture of the sonar waveform based on the multi-band processing module and the multi-receptive field residual block design, and outputting the synthesized sonar data consistent with the real sonar data in time resolution. The discriminator comprises two substructures, namely a multi-period discriminator and a multi-scale discriminator, which are respectively used for capturing the periodicity and detail consistency of the sonar signal in different time periods and time scales, and enhancing the multi-level discrimination capability of the authenticity of the generated data.

A method for estimating typhoon structural parameters by fusing spaceborne GNSS reflectometer data and radiometer data, which relates to the technical fields of data fusion and typhoon monitoring. The method comprises: acquiring GNSS-R reflectometer data and radiometer data; filtering typhoon data from the GNSS-R reflectometer data and the radiometer data to obtain filtered GNSS-R reflectometer data and radiometer data; performing data spatio-temporal matching on the filtered GNSS-R reflectometer data and radiometer data to obtain a spatio-temporally matched region; performing data fusion processing on the spatio-temporally matched region to obtain fused data; and performing typhoon structural parameter estimation on the obtained fused data to obtain structural parameters of a typhoon at each development stage. Fused data of spaceborne GNSS-R reflectometer data and radiometer data is used for typhoon structural parameter estimation, such that the temporal resolution and accuracy of parameter estimation can be improved, and the algorithm complexity is low.

A method for non-contact modal analysis uses an optical sensing device such as a video camera. Unlike traditional modal analysis, requiring accelerometers or force sensors to measure the modal characteristics of a structure and animate the motion of a wireframe model of the test structure, the inventive system mathematically modifies the original video recordings by filtering, amplifying, and modifying the playback rate or applying modal shape mask or vectors to produce modified video to show motion at each modal frequency. This provides animated mode shapes with higher spatial and temporal resolution that possible with the traditional approach of using wireframe model animations.

The invention relates to the technical field of meteorological observation, in particular to a solar radiation prediction method and device based on multi-source satellite data fusion, an electronic device and a storage medium. And an end-to-end deep learning prediction model is constructed, so that a dark start prediction blind area caused by visible light deficiency before and after sunrise is effectively overcome, continuous prediction in all days, especially in a dawn period, is realized, and the prediction coverage capability is expanded. And secondly, regional adaptability parameter calibration is carried out on the physical inversion model, so that the localization precision of a historical reference radiation field is improved, and a more reliable training basis is provided for the prediction model. Finally, the output solar radiation prediction product with high temporal-spatial resolution and physical consistency can directly support key businesses such as photovoltaic power station output prediction and power grid climbing management, and the practicability and reliability of the prediction result are enhanced.

This application relates to the field of medical image processing technology, and provides a method, apparatus, device, and storage medium for reconstructing ECT images. This allows for more thorough capture of subtle changes in the concentration distribution of radioactive tracers at high temporal resolution, helping to reduce image noise levels and improve image quality. This application obtains a first ECT image sequence; the first ECT image sequence is obtained by scanning with an ECT scanning device; the first ECT image sequence is input into a pre-constructed intermediate frame prediction network to obtain an estimated second ECT image sequence; using the estimated second ECT image sequence as a priori image, a target second ECT image sequence is reconstructed; the estimated second ECT image sequence and the target second ECT image sequence belong to the second ECT image sequence; the temporal resolution of the second ECT image sequence is higher than that of the first ECT image sequence.

The invention relates to a high-precision land carbon flux reconstruction method based on four-dimensional space-time deep learning. The method comprises the steps of obtaining a multi-source heterogeneous data set and performing preprocessing; constructing a four-dimensional space-time deep learning model architecture which is sequentially connected with a multi-scale space-time feature extraction module, a space-time feature fusion module, a prediction layer and an uncertainty quantization layer from input to output, and taking the preprocessed multi-source heterogeneous data set as input and a carbon flux reconstruction result as output; constructing a loss function, adopting a hierarchical learning rate strategy and introducing a cosine annealing scheduler, and training a four-dimensional space-time deep learning model architecture based on the preprocessed multi-source heterogeneous data set to obtain a four-dimensional space-time deep learning model; and obtaining a carbon flux data reconstruction set, and inputting the four-dimensional space-time deep learning model to generate a carbon flux reconstruction result. By adopting the method, multi-source heterogeneous data can be effectively integrated, the complex spatial-temporal dynamic characteristics of the carbon flux are captured, and accurate reconstruction of the land carbon flux with high spatial-temporal resolution is realized.

The invention relates to a method for demarcating an underground water reserve area. The method comprises the following steps: acquiring and tagging remote sensing, geological survey, monitoring well and Internet of Things data; the heterogeneous data is subjected to normalization preprocessing, and space-time and physical quantity unification is achieved; semantic annotation, feature completion and causal relationship modeling are performed based on a knowledge graph, and data integrity and internal relation are improved; the reliability of multi-source data is improved by adopting contradiction detection and confidence evaluation; self-adaptive weighted multi-source data fusion is realized through a dynamic weight mechanism; and constructing an adaptive graph neural network model to carry out dynamic time sequence prediction and spatial partition analysis of groundwater reserves. According to the method, the precision and temporal-spatial resolution of groundwater reserve estimation can be remarkably improved, the robustness to abnormity and environmental fluctuation is improved, and scientific decision and intelligent management can be realized.

The invention relates to the technical field of biomedical imaging, and discloses a photoacoustic-ultrasonic volume imaging method and system based on an annular ultrasonic array, and the method comprises the steps: reconstructing a two-dimensional photoacoustic image and a two-dimensional ultrasonic image; after photoacoustic imaging and ultrasonic synthetic aperture imaging of one imaging plane are completed, the annular ultrasonic array is moved in the direction perpendicular to the imaging plane, photoacoustic images and ultrasonic images of all the imaging planes are repeatedly executed on the next imaging plane, rebuilt and stacked, and a three-dimensional photoacoustic volume image and a three-dimensional ultrasonic volume image are formed; performing spatial pulse response deconvolution processing on the three-dimensional photoacoustic volume image; and fusing the processed three-dimensional photoacoustic volume image and the three-dimensional ultrasonic volume image to generate a three-dimensional bimodal volume image. According to the invention, high-time-resolution opto-acoustic-ultrasonic bimodal three-dimensional imaging is realized by using an annular ultrasonic array and motion scanning, and the high spatial resolution of the image is further ensured through a series of technologies.

The invention discloses a regional ionosphere temporal-spatial resolution refinement method, which comprises the following steps: acquiring real-time GIM data, a solar activity index and a geomagnetic activity index, inputting the data into a trained forecasting model, and outputting a VTEC mean value of a grid center point; calculating an STEC value based on the output VTEC mean value; the training of the forecasting model comprises the following steps: acquiring original GIM data and CORS observation value data, and respectively preprocessing the original GIM data and the CORS observation value data to obtain GIM-VTEC calculated based on the GIM data and puncture point VTEC calculated based on the CORS observation value data; the GIM-VTEC obtained through preprocessing and the puncture point VTEC are matched according to the time and the grid range; acquiring a solar activity index and a geomagnetic activity index, matching the solar activity index and the geomagnetic activity index with the matched VTEC data according to time, and constructing a training sample set; constructing a BP neural network, taking time, longitude, latitude, GIM-VTEC, a solar activity index and a geomagnetic activity index as input, and taking a puncture point VTEC obtained by calculating CORS observation value data as output; and training is carried out on the constructed training sample set, and a trained forecasting model is output.