63results about How to "Achieve reconstruction" patented technology

The invention provides a depth map super-resolution reconstruction method and system based on a scale perception implicit function, and the method comprises the steps: carrying out the feature extraction of a high-resolution guide map and a low-resolution depth map, and obtaining a high-resolution guide feature and a low-resolution depth feature; the method comprises the following steps: firstly, extracting a high-resolution guide feature and a low-resolution depth feature, then performing mutual fusion on the extracted features by adopting implicit cross attention, performing bidirectional updating and multiple iterations to obtain a final high-resolution guide feature and a final low-resolution depth feature, and decoding the high-resolution guide feature and the low-resolution depth feature through a Transform based on implicit cross attention and implicit self-attention to obtain a high-resolution guide feature and a low-resolution depth feature. And finally, obtaining a super-resolution reconstructed depth map of any scale. According to the method, a unified solution is provided for super-resolution reconstruction of the depth map with any scale, and compared with the most advanced method at present, the model and the system both achieve the best effects of subjective evaluation and objective evaluation on a mainstream data set.

The invention discloses a near-real-time reconstruction method, device and equipment for multispectral remote sensing time sequence images in the whole growth season of crops and a medium, and relates to the field of remote sensing data reconstruction. The method comprises the following steps: acquiring historical time sequence remote sensing image spectral data of a target ground feature type, constructing a spectral reference curve of the target ground feature type in a whole growing season by adopting a ground feature change detection algorithm and a harmonic function fitting algorithm, and further constructing a vegetation index reference curve of the target ground feature type in the whole growing season; according to the vegetation index reference curve and the near-real-time vegetation index data, utilizing an extended Kalman filtering algorithm to reconstruct a vegetation index to obtain a vegetation index reconstruction result; and according to the spectrum reference curve, the near real-time remote sensing image spectrum data and the vegetation index reconstruction result, performing remote sensing image spectrum data reconstruction by using an extended Kalman filtering algorithm to obtain a remote sensing image spectrum data reconstruction result. According to the invention, reconstruction of near-real-time remote sensing image spectral data is realized.

The invention relates to the field of cold end optimization control systems, in particular to a method for reconstructing a wind field of an axial flow fan group of a direct air cooling unit based on a physical information neural network. Comprising the following steps: S1, arranging a plurality of wind speed and pressure sensors near an axial flow fan group of the direct air cooling unit, and collecting sample data when axial flow fans rotate at a high speed by taking wind speeds and pressures in all directions of a wind field of the axial flow fan group of the unit as target variables; s2, performing data preprocessing on the sample data; s3, determining a differential equation of fluid motion satisfied by the axial flow fan group area, and constructing a physical information neural network of the axial flow fan group; s4, training a physical information neural network model of the axial flow fan group by using the preprocessed data obtained in the S2; and S5, reconstructing a wind field of the axial flow fan group of the direct air cooling unit according to the trained physical information neural network model of the axial flow fan group in the step S4. According to the method, the wind field data of the axial flow fan group of the direct air cooling unit is quickly and accurately predicted by using a physical information neural network method.

The invention relates to the technical field of computed tomography, in particular to a CT imaging system and method based on a distributed micro-focus X-ray source, and the system comprises the distributed X-ray source provided with a plurality of micro-focuses, a detector, a scanning carrier, a control module and a data processing module. In the scanning process, the distributed X-ray source and the detector rotate around the rotation center relative to the object to be detected at the same time, the multiple small and micro focuses are sequentially subjected to beam outgoing according to the beam outgoing strategy so as to disperse heat dissipation, the peak power of the small and micro focus radiation source can be effectively improved, and the time needed by single CT scanning is shortened. Besides, by designing a beam outgoing strategy of the distributed X-ray source, sampling distribution of a plurality of small microfocuses can be optimized, sub-pixel-level image reconstruction is realized, and the spatial resolution is further improved. According to the system and the method, ultrafast high-definition CT imaging can be realized, and the problem that a traditional single-focus X-ray CT system is difficult to consider high spatial resolution and high time resolution at the same time is solved.

The invention discloses a cancer category detection method based on multi-omics data clustering remarking, and relates to the technical field of machine learning. The learning process of the category detection model is as follows: n multi-omics data samples are acquired, each sample comprises m different categories of omics data, sample labels are cancer categories corresponding to the samples, and K cancer categories are provided; preprocessing the sample, and generating sample features for the preprocessed sample by using the model; according to sample features, clustering the preprocessed samples into K clusters; performing impurity measurement on each cluster, and re-marking sample labels in the clusters; performing comparative learning based on a re-marking result to enable the model to generate accurate sample features; and according to accurate sample characteristics generated by the model, predicting cancer types. According to the invention, cancer category detection is carried out by using the category detection model, and the accuracy of cancer typing is improved through reconstruction of the sample labels and application of a contrast learning method.

The invention discloses a low-quality fluorescent microscopic image reconstruction method based on noise compensation guided filtering and unmatched projection deconvolution, which efficiently realizes pre-denoising of detail preservation and background noise suppression through guided filtering of noise compensation, and takes the pre-denoising as the input of an unmatched projection deconvolution method. The blurring caused by diffraction is removed, the pre-denoising step can effectively suppress noise amplification in the deconvolution process, and the whole process realizes the improvement of the signal-to-noise ratio of the image and the restoration of the resolution. Therefore, the method disclosed by the invention can be used for simply and effectively improving the quality of the image acquired under the low-light-dose imaging condition, and is beneficial to information acquisition of long-time fluorescent imaging of the living cells.

The invention discloses a three-dimensional space PM2.5 concentration estimation method and device based on an improved UNet, and the method comprises the following steps: employing an unmanned plane to collect and obtain a haze image, employing a haze sensor to collect a PM2.5 concentration value as a data set, and carrying out the data preprocessing; and inputting the training data set into the improved UNet model for iterative training until a model iterative training loss function value is minimum, training to obtain a network model based on the improved UNet, inputting the haze image into the trained network model, and outputting a PM2.5 concentration value. According to the method, information extracted by the improved UNet network is fused, fine-grained haze reconstruction in a three-dimensional space can be realized, and the method has better generalization performance.

The invention discloses a process anomaly detection method based on a time sequence shielding auto-encoder, and belongs to the technical field of anomaly detection in the process industry. The method comprises the following steps of: (1) standardizing data and extracting a data continuous subset as a new sample by using a sliding time window mode; (2) random sampling is adopted to input variables, and mask operation is carried out on data; (3) extracting and reconstructing hidden features of process data by adopting an automatic encoder which takes LSTM (Long Short Term Memory) into which a learnable vector is introduced as a core; (4) model training; (5) positioning abnormal features and positions; and (6) performing a process anomaly detection process by using TSMAE. According to the method, most input variables can be randomly shielded, information redundancy can be reduced, extraction of hidden features and reconstruction of original data can be realized by virtue of time correlation of LSTM modeling process data, equivalent learnable vectors are filled in the hidden features, and the global information capability of model modeling is enhanced by taking overall reconstruction loss as an optimization target.

The invention provides a method and a device for detecting the placement state of a wafer in a wafer box, which are applied to the detection of the wafer box, and the detection method comprises the following steps: obtaining a thickness standard value and an angle standard value of the wafer; scanning the wafer box by using a laser and a camera to obtain a three-dimensional shape of the wafer box; calculating the thickness and angle of each wafer in the wafer box by using a thickness calculation algorithm and an angle calculation algorithm according to the three-dimensional morphology; wherein the thickness calculation algorithm processes three-dimensional shape data through a point cloud downsampling method and a point cloud clustering method; and comparing the thickness and the angle of each wafer in the wafer box with the thickness standard value and the angle standard value to determine whether the wafers in the wafer box have overlapped wafers, inclined wafers and missing wafers. According to the invention, the technical problems that the scanning time is long and the production efficiency is influenced because the position of the wafer needs to be determined by scanning the interior of the wafer box point by point through a distance measuring sensor in the traditional Mapping operation are solved.

The invention discloses a multi-modal three-dimensional semantic occupancy prediction method for a field scene, and the method comprises the steps: generating a three-dimensional semantic occupancy tag based on multi-frame point cloud data with semantic information; the method comprises the following steps: acquiring multi-modal sensing data in a field scene, performing feature extraction and historical frame superposition on the sensing data, transmitting geometric knowledge in extracted point cloud features to an image branch network by using a knowledge distillation function, performing dynamic fusion on the extracted image and point cloud features, and outputting fusion features; and converting the fusion features by using an occupation prediction head constructed by three-dimensional convolution, generating a predicted value of three-dimensional semantic occupation, and realizing automatic reconstruction of a field scene. According to the method, the generation of the three-dimensional semantic occupancy label is realized, the error of scene reconstruction is remarkably reduced, and the vacancy of a three-dimensional semantic occupancy prediction method in a field scene is filled.

The invention discloses a fast-separation microneedle patch and a preparation method and application thereof. The microneedle patch comprises a needle tip prepared from a needle tip liquid and a separation backing prepared from a separation base liquid; the needle tip liquid consists of a drug-containing inclusion compound solution, a photoinitiator and a hypotonic agent; the drug-containing inclusion compound is prepared by first covalently linking a β-cyclodextrin conjugated polymer material to an angiogenesis-promoting drug and then encapsulating a 5α-reductase inhibitor; the separation base liquid consists of a polymer material, a gas initiator and a solvent; the gas initiator consists of an acid source and an alkali source; the fast-separation microneedle of the invention has strong separation and penetration-promoting performance, improves the hair follicle microenvironment from multiple aspects to promote hair regeneration, and has broad application prospects in the treatment of androgenic alopecia.

The invention belongs to the field of holographic phase reconstruction, and discloses a lightweight coaxial holographic reconstruction method based on a double-domain complex-valued neural network, which achieves the same reconstruction effect under the condition that model parameters and calculated amount are greatly reduced, and adopts a physical process based on a double-domain complex-valued neural network architecture in combination with an optical coaxial holographic imaging model. Firstly, a hologram A and an initial phase construct a complex amplitude, the complex amplitude is input into a double-domain complex-valued neural network, an output complex-valued field C is obtained through spatial domain convolution and frequency domain learning, then a coaxial holographic forward propagation process is simulated, and the intensity is taken as a reconstructed hologram D; and then calculating the similarity between the hologram A and the reconstructed hologram D, and realizing phase reconstruction through a gradient descent optimization algorithm of a complex valued neural network. The invention provides a method for directly reconstructing a complex-valued field by combining a lightweight double-domain complex-valued neural network with physical constraints.

The present invention provides a detection method and device for the placement state of wafers in a wafer box, which is applied to the detection of wafer boxes. The detection method includes: obtaining the standard value of the thickness and the standard value of the angle of the wafer; using a laser and a camera to scan the wafer box to obtain the three-dimensional morphology of the wafer box; according to the three-dimensional morphology, using a thickness calculation algorithm and an angle calculation algorithm to calculate the thickness and angle of each wafer in the wafer box; wherein the thickness calculation algorithm processes the data of the three-dimensional morphology through a point cloud downsampling method and a point cloud clustering method; comparing the thickness and angle of each wafer in the wafer box with the standard value of the thickness and the standard value of the angle to determine whether there are duplicate wafers, oblique wafers, and missing wafers in the wafer box. The present invention solves the technical problem that the traditional Mapping operation needs to use a ranging sensor to scan the inside of the wafer box point by point to determine the wafer position, resulting in a long scanning time and affecting production efficiency.

The invention discloses a device and method for measuring corrosion characteristic parameters of a grouting sleeve connecting piece, and belongs to the field of assembly structures, the device mainly comprises a CCD camera and a telescopic probe device, a rigid probe of the telescopic probe device is in contact with the surface of the grouting sleeve connecting piece to be measured, the CCD camera is connected with a computer, and the computer is connected with the CCD camera. The relative positions of the CCD camera and the telescopic probe device are not changed in the moving process, at the beginning, a rigid probe of the telescopic probe device makes contact with the starting point of the measuring area range, when the rigid probe of the telescopic probe device moves to the end point of the measuring area range, measurement is stopped, and in the whole measuring process, the CCD continuously shoots videos and automatically stores the videos in the computer. The method is used for measuring the corrosion characteristic parameters of the grouting sleeve connecting piece based on the device. According to the method, comprehensive and accurate measurement of the size and depth of the rust pit can be realized, and the method has an application value for mechanical property degradation evaluation of a large-scale and wide-range service assembly type structure after corrosion.

The invention discloses a sparse representation correction method for an aerodynamic thermal radiation effect image, which belongs to the field of digital image processing, and comprises the following steps: constructing an energy functional optimization model with the goal of minimizing the sum of a data fidelity term, an image prior regularization term and a bias field prior regularization term; the data fidelity item is used for measuring the difference between the degraded image and the original clear image and the difference between the degraded image and the bias field; the image prior regularization item is used for measuring the gradient of the original clear image; the bias field B adopts dictionary fitting as B = Wa, W and a are a dictionary and a coefficient vector respectively, and a prior regularization item of the bias field is used for measuring an L1 norm of the coefficient vector a; solving the energy functional optimization model under the constraint that the number of non-zero elements in the coefficient vector a does not exceed a preset threshold value to obtain the coefficient vector a; and reconstructing a bias field B according to B = Wa, and subtracting the bias field B from the degraded image to obtain an original clear image. The correction precision and the corrected image quality of the pneumatic thermal radiation effect image can be improved.

The invention discloses a multi-sensor fused all-weather operation target sensing and measuring system and method, and belongs to the field of intelligent sensing and measuring, the system comprises a data acquisition module, an all-weather image enhancement module, a three-dimensional modeling and semantic segmentation module and a motion detection module, and the method comprises the following steps: performing data acquisition on an operation target; performing all-weather image enhancement processing on the acquired image data; performing image semantic segmentation on the enhanced image; performing point cloud semantic modeling by using a result after image semantic segmentation in combination with laser point cloud and IMU (Inertial Measurement Unit) data; and using a result after image semantic segmentation to assist optical flow method calculation to realize motion detection. According to the invention, the limitation of a single sensor can be overcome, the sensing and measuring capabilities of the system in a complex environment are improved, and all-weather and all-time high-precision sensing and measuring tasks are realized. More reliable technical support is provided for application in the fields of automatic driving, unmanned aerial vehicles, intelligent robots and the like, and more intelligent sensing and measurement are achieved.

The device comprises an umbrella-shaped elastic net disc, a pushing rod, a releasing section and a micro catheter, the umbrella-shaped elastic net disc is of a double-layer structure woven by metal wires, points at the maximum diameter of a disc opening of the umbrella-shaped elastic net disc are located on the same plane, and anchoring devices are distributed around the two sides of the skirt edge of the umbrella-shaped elastic net disc; the umbrella-shaped elastic net disc is used for strengthening fixation of the umbrella-shaped elastic net disc and a tumor wall, so that the umbrella-shaped elastic net disc can be better attached to the inner wall of a tumor cavity and is not prone to displacement in the tumor cavity, the umbrella-shaped elastic net disc is provided with a blocking area, the middle position of the blocking area right faces an aneurysm neck opening, and a coating for promoting growth of vascular endothelial cells is arranged in the middle position of the blocking area of the umbrella-shaped elastic net disc. The coating can accelerate endothelialization of the neck of the aneurysm, so that reconstruction of the aneurysm-carrying artery is also realized, and the risk of recurrence and recanalization of the aneurysm is reduced.

The invention discloses a full-computer intelligent high-similarity prosthesis production method and system, and the method comprises the steps: a main computer obtains a 3D model containing the segmentation information of all tissues in a missing part of a patient through a pre-constructed artificial intelligence model by collecting the information of the patient, and obtains a 2D unfolded image for the surface layer of the 3D model based on the information of the patient and the 3D model; controlling 3D printing equipment to print the 3D model to obtain each layered part of the prosthesis main body; controlling film manufacturing equipment to manufacture a surface layer film of the missing part; controlling 2D printing equipment to print a 2D unfolded image on the surface layer film of the missing part to form a skin; assembling equipment is controlled to combine all the parts of the prosthesis, the skin is attached to the surface of the prosthesis body, surface treatment is conducted, and a finished prosthesis product is obtained. According to the invention, the prosthesis production process is computerized and intelligent, the high-precision and high-similarity prosthesis can be provided in a shorter time, and the manufacturing cost is reduced; the artificial intelligence technology is used for prosthesis design and modeling, and the quality and similarity of the prosthesis can be improved.

This invention relates to the field of multimodal sentiment analysis technology, and in particular to a method and system for multimodal sentiment analysis based on sequential translation under uncertain and missing modalities. The method includes: acquiring multimodal sentiment data, including visual, textual, and auditory modalities; constructing a multimodal sentiment analysis model based on sequential translation, firstly by extracting self-attention encoding features of each modality using a Transformer encoder; and then designing a bidirectional sequential translation architecture centered on the textual modality. This invention improves the utilization of textual modal features in the feature fusion process, thereby enhancing the quality of multimodal fusion features and providing strong support for subsequent complex sentiment analysis.

The application provides a social network opinion reconstruction method and device based on graph movement smoothing, wherein the method comprises the following steps: determining a sampling user set according to a reverse greedy sampling algorithm based on a pre-constructed directed social network model; determining graph movement smoothing degrees corresponding to all users in the directed social network model; obtaining opinion observation values corresponding to each user in the sampling user set; and reconstructing social network opinions of the users in the directed social network model based on the graph movement smoothing degrees and the opinion observation values. The method is aimed at a directed social network structure, and the sampling user set is determined through the reverse greedy sampling algorithm. The method can not only effectively improve the accuracy of social network opinion reconstruction, overcome the defects that the existing user opinion reconstruction method is mainly aimed at a non-directed network structure and cannot be directly extended to a directed network structure and the reconstruction mean square error is large, and realize accurate reconstruction of social network opinions, but also effectively improve the running efficiency of the reconstruction process.

The invention discloses a design and construction method for restoration and reconstruction of a forest ecosystem. The design and construction method comprises the following specific steps of 1, selection of vegetation grass, 2, laying of a gravel soil layer, 3, planting of vegetation, 4, fixing of the gravel soil layer and 5, restoration and management of the vegetation. According to the invention, the soil gravel layer can be effectively modified and laid, the vegetation growth and survival effects are improved, the sand stabilization net body structure is optimized and improved, the use function of the sand stabilization net body structure is improved, and the sand stabilization net body structure is convenient to mount and fold.

The application provides a physical constraint-based illumination adaptive spectral reflectance reconstruction system, an illumination adaptive spectral reflectance reconstruction framework (I-SRR) based on no known illumination, auxiliary hardware or multiple captures, which can accurately recover intrinsic spectral reflectance of a material from a single RGB image; a spectral imaging physical model is embedded into a deep unfolding framework, a differentiable optimization structure is constructed based on an alternating direction multiplier method (ADMM), a spectral formation and recovery process is implicitly integrated, a physically guided end-to-end learning is realized through deep unfolding optimization based on physical constraints; the application can realize robust reflectance recovery under unknown or complex illumination conditions, adaptively adjust features through a learned compensation mechanism, maintain consistent reconstruction accuracy in different color temperatures (4000K-11200K) and indoor and outdoor scenes, and realize robust reconstruction under dynamic illumination conditions.

The invention discloses an imaging method for passing through a random scattering medium in visible light based on a diffractive optical neural network, and the method comprises the following steps: 1, carrying out the optical coding of an input image through a coherent or low-coherent monochromatic plane wave, and enabling a coded light field to pass through a random phase diffuser for scattering; and step 2, continuous wavefront modulation of the scattered light field is obtained through a coherent or incoherent diffractive optical neural network, and finally, high-fidelity original image reconstruction is generated on an output plane. The invention finds that the coherent diffraction neural network obtains dynamic phase modulation by introducing a randomly generated diffuser in the training process, enhances the adaptive capacity of the network to the spatial coherence change of the light source, realizes the high-quality reconstruction of the scattering image under the visible light, has the robustness to the low-coherence light source, and is suitable for large-scale popularization and application. The method can be applied to low-power-consumption and real-time imaging of the dynamic scattering medium under natural light.

This invention discloses an underwater target reflectance inversion method based on an underwater transmission model, comprising: acquiring the original digital values ​​of the underwater target collected by a hyperspectral camera; combining the built-in radiometric calibration parameters of the hyperspectral camera; converting the original digital values ​​into radiance based on the sensor pre-calibration response characteristics; physically modeling the water absorption effect, scattering effect, and path radiation effect during underwater light propagation to determine the linear mapping relationship between radiance and target reflectance; constructing a training radiative transmission model using a non-negative linear regression model; using a calibrated target with known reflectance as a sample to train the radiative transmission model between the original digital values ​​and the target reflectance, and solving for the mapping parameters between the two; and based on the original digital values ​​and mapping parameters of the underwater target to be reconstructed, using the trained radiative transmission model to invert and obtain the true reflectance of the target, thus completing the spectral reconstruction.

The application discloses a kind of variable-thickness curved surface weaving ceramic matrix composite three-dimensional reconstruction methods, belong to the field of microstructure identification and reconstruction.The application can quickly and accurately calculate the area of each cross section of the composite material by identifying the outer edge of the curved surface weaving composite material combined with the area of a single pixel;Meanwhile, by deleting the surface fibers arranged complexly, the classification accuracy of internal warp and weft can be improved, and it is more effective for subsequent reduction of human participation;By processing the three views of XCT data of the composite material respectively, the pore position can be accurately extracted from the original XCT image, the fiber bundles in different slices are numbered from the overhead view and the side view through the number of fiber columns and the spatial position relationship, and the spatial paths of warp yarns and weft yarns are respectively fitted, the fiber bundle cross section is represented by oblique elliptic function, the three-dimensional model reconstruction of curved surface weaving CMC can be quickly realized, the algorithm is practical, and the whole process is simple to operate.

The present invention discloses an active visual reconstruction system and method for automobile morphology based on point and line invariants, aiming to solve the problem of active visual detection of automobile morphology based on point and line invariants. The active visual reconstruction system for automobile morphology based on point and line invariants mainly comprises a camera bracket (1), a camera (2), a connecting piece (3), a line laser (4) and a two-dimensional calibration plate (5). The active visual reconstruction method for automobile morphology based on point and line invariants comprises three steps: image acquisition, coordinate solution of vehicle body feature points in the coordinate system of the two-dimensional calibration plate (5) based on point and line invariants, and vehicle body point reconstruction. A system and method for active visual reconstruction of automobile morphology based on point and line invariants with simple structure and reliable performance is provided.

The invention discloses an image processing method and device and a storage medium, and the method comprises the steps: obtaining an input image sequence of a target object, and determining a mask image of the target object based on an image frame of the input image sequence; selecting a key frame from the input image sequence, and determining camera pose information of a visual angle corresponding to the key frame; and based on the key frame, the mask image and the camera pose information, performing three-dimensional model reconstruction on the target object. The technical problem that when an existing three-dimensional reconstruction technology based on a monocular camera reconstructs an object in a real scene, corresponding reconstruction benefit requirements are difficult to meet is solved.

The invention relates to an osteochondral scaffold with biological energy activity and a preparation method of the osteochondral scaffold, and belongs to the technical field of biological materials and tissue engineering. The method comprises the following steps: carrying out first chemical reaction on an intermediate of tricarboxylic acid cycle and trihydric alcohol to obtain a bio-energy material prepolymer; performing a second chemical reaction on the bio-energy material prepolymer and a cross-linking agent, and adding an osteogenesis promoting agent into a system of the second chemical reaction to obtain composite slurry; regulating and controlling the weight of the osteogenesis promoting agents in the composite slurry to obtain three biological energy material inks containing the osteogenesis promoting agents with different weights; carrying out gradient type 3D printing on the three kinds of biological energy material ink containing the osteogenesis promoting agents with different weights so as to respectively simulate a cartilage layer, a transition layer and a subchondral bone layer in the articular cartilage, and carrying out a third chemical reaction after 3D printing, so as to obtain the target osteochondral scaffold. The osteochondral scaffold provided by the invention has biological energy activity, and realizes regeneration of transparent cartilage and reconstruction of an osteochondral complex interface.

The invention discloses a reconstruction method and a reconstruction device of a space protein expression map and a storage medium. Comprising the following steps: receiving a multi-channel high-resolution tissue image of a tissue region obtained based on tissue slices; receiving a polymerization true value of the expression quantity of each target protein on each first-direction tissue band and each second-direction tissue band, wherein the polymerization true value is obtained after the first-direction tissue band and the second-direction tissue band are cut on the first tissue slice and the second tissue slice; a deep learning network is trained based on the aggregation true values of the protein expression quantities on the tissue strips in the first direction and the second direction and the multi-channel high-resolution tissue image, and the trained deep learning network predicts the expression quantity of each target protein on each image unit based on the multi-channel high-resolution tissue image; and reconstructing a two-dimensional protein expression map. According to the method, the two-dimensional space protein map can be reconstructed with high precision only by using as few as two tissue slices and the aggregation value of the protein expression quantity on the tissue strip which is relatively easy to obtain without the protein expression quantity of each unit.

The invention relates to a real-time interactive hybrid ocean simulation method and simulation system based on unified spectrum control, and the method comprises the following steps: obtaining environment wind field parameters, and generating a unified ocean wave spectrum; based on the unified sea wave spectrum, generating a JONSWAP spectrum model by adopting fast Fourier transform, and simulating a global wave field by using the JONSWAP spectrum model; and determining the position of an interactive object, dynamically injecting wave particles at the boundary of a local area corresponding to the position based on the unified sea wave spectrum, generating near-field fluctuation, and superposing the near-field fluctuation to a global wave field to form a continuous mixed sea surface. Compared with the prior art, the method has the advantages of uniform spectrum control, efficient parallel computing, high physical consistency, high expandability and the like, and uniform simulation of the large-scale continuous sea surface and the local interaction area is realized.