312 results about "Spectral domain" patented technology

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The invention relates to the technical field of electromagnetic pulses, and discloses an electromagnetic pulse data transmission method. The method comprises the following steps: collecting natural electromagnetic pulse signal data of a detection target area through a six-dimensional complementary electrode array structure, and executing Kalman filtering peak extraction and characteristic frequency locking to obtain frequency-optimized signal data; executing first-order and second-order gradient difference matrix calculation and space vector synthesis to obtain signal source position coordinate data and signal intensity data; performing spectral domain reconstruction and multilayer dynamic threshold analysis to obtain a spatial clustering abnormal data packet; and selecting an optimal transmission channel to transmit the spatial clustering abnormal data packet to a cloud server. According to the invention, the influence of an external interference source is effectively eliminated, accurate extraction and characteristic frequency locking of effective frequency band signals are realized, power frequency and harmonic interference thereof are effectively suppressed, the signal-to-noise ratio of the effective frequency band of the signals is improved, and the reliability and timeliness of data transmission in a severe field environment are ensured.

Disclosed in the present invention are a hyperspectral calculation imaging method and system based on multi-domain fusion of spatial, spectral and frequency domains, and a medium. The method of the present invention comprises: using a two-dimensional offline discrete cosine transform (DCT) to convert an RGB image Y into a frequency domain to obtain a frequency domain feature map Yfreq; extracting a frequency information image (I) from the frequency domain feature map Yfreq; using a two-dimensional offline inverse discrete cosine transform (IDCT) to transform the frequency information image (I) into a spatial domain to obtain a frequency information image Xfreq of the spatial domain; and fusing the frequency information image Xfreq of the spatial domain into the spatial-spectral domain features of the RGB image Y to generate a hyperspectral image (II). The present invention aims to solve the problems of poor detail information and low reconstruction accuracy of hyperspectral images in existing hyperspectral calculation imaging, and realizes high-fidelity reconstruction of a target spectrum.

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

An audio encoder has a first information sink oriented encoding branch such as a spectral domain encoding branch, a second information source or SNR oriented encoding branch such as an LPC-domain encoding branch, and a switch for switching between the first and second encoding branches, the second encoding branch having a converter into a specific domain different from the spectral domain such as an LPC analysis stage generating an excitation signal, and the second encoding branch having a specific domain coding branch such as LPC domain processing branch, and a specific spectral domain coding branch such as LPC spectral domain processing branch, and an additional switch for switching between the specific domain coding branch and the specific spectral domain coding branch. An audio decoder has a first domain decoder, a second domain decoder, and a third domain decoder as well as two cascaded switches for switching between the decoders.

The invention provides an ocean multi-source data fusion method based on an adaptive Fourier nerve process, and relates to the technical field of data fusion, and the method specifically comprises the following steps: carrying out the preprocessing of ocean variable field data; and adaptively generating a set background field for any time point. And organizing the background field, the truth value and the observation value into a standardized data set for adaptive Fourier neural process model training. An AFNP model is constructed, the AFNP model comprises a background field encoder, an observation field encoder, a neural Fourier operator, an adaptive alignment fusion module and a decoder, and finally the decoder outputs a prediction result of a real field. Supervised end-to-end training is carried out on the AFNP model, a nonlinear relation between a background field and an observation field is learned, and a real ocean state field is reconstructed. According to the technical scheme, the problems that in the prior art, dependence on linear hypothesis and Gaussian errors is caused, a deep learning model cannot be physically explained, and the boundary adaptability of a spectral domain method is poor are solved.

The invention provides an electric energy quality device power smooth flexible capacity expansion method for a new energy access scene. The method comprises the following steps: acquiring three-phase voltage, current and power signals, obtaining standardized three-dimensional tensor data through multi-dimensional tensor spectrum projection decomposition, calculating a characteristic value and a characteristic tensor base, and generating a dynamic characteristic vector and a weight coefficient set; a multi-layer probability graph matrix is constructed, a feature mapping matrix and a hierarchical contribution coefficient are obtained through iterative calculation, a fluctuation feature tensor is constructed, and a multi-time scale prediction model is established; constructing a control manifold space and a measurement tensor, calculating a topological connection matrix, designing a multi-dimensional control law and performing stability optimization; and constructing an embedded feature matrix and an inter-module distance matrix, calculating a capacity expansion weight and a power distribution scheme, and performing spectral domain equalization control and grid-connected coordination control. According to the invention, the power smoothing effect is obviously improved, and the system capacity expansion flexibility is enhanced.

An audio encoder has a first information sink oriented encoding branch such as a spectral domain encoding branch, a second information source or SNR oriented encoding branch such as an LPC-domain encoding branch, and a switch for switching between the first and second encoding branches, the second encoding branch having a converter into a specific domain different from the spectral domain such as an LPC analysis stage generating an excitation signal, and the second encoding branch having a specific domain coding branch such as LPC domain processing branch, and a specific spectral domain coding branch such as LPC spectral domain processing branch, and an additional switch for switching between the specific domain coding branch and the specific spectral domain coding branch. An audio decoder has a first domain decoder, a second domain decoder, and a third domain decoder as well as two cascaded switches for switching between the decoders.

An audio encoder has a first information sink oriented encoding branch such as a spectral domain encoding branch, a second information source or SNR oriented encoding branch such as an LPC-domain encoding branch, and a switch for switching between the first and second encoding branches, the second encoding branch having a converter into a specific domain different from the spectral domain such as an LPC analysis stage generating an excitation signal, and the second encoding branch having a specific domain coding branch such as LPC domain processing branch, and a specific spectral domain coding branch such as LPC spectral domain processing branch, and an additional switch for switching between the specific domain coding branch and the specific spectral domain coding branch. An audio decoder has a first domain decoder, a second domain decoder, and a third domain decoder as well as two cascaded switches for switching between the decoders.

The invention discloses a remote sensing cloud layer repairing method based on time-spectral domain fusion and time sequence self-attention enhancement, and relates to the technical field of remote sensing cloud layer image processing. The method comprises the steps of receiving a remote sensing missing image as a source image; the method comprises the following steps: constructing a generator, inputting a remote sensing missing image into the generator for preliminary processing, the generator comprising a feature extractor, a ConformerPlus module and a cloud detection module, and a new multi-head self-attention mechanism being embedded in the ConformerPlus module. According to the method, time-frequency spectrum domain information extraction and fusion can be carried out on data, so that the feature expression ability is enhanced, the edge information capturing ability of the model is improved, the model is guided to realize progressive cloud layer removal from outside to inside, the detail expression can be improved, noise interference can be suppressed, and the method is suitable for large-scale popularization and application. And the detail definition and the texture fidelity of the image are effectively improved.

The invention discloses a liquid crystal display screen gray scale control method based on adaptive brightness compensation, which relates to the technical field of liquid crystal display screen control, and comprises the following steps: collecting environmental spectrum distribution information and a pixel gray scale voltage response curve of a liquid crystal display screen in real time, and carrying out fusion processing to form a time sequence joint feature vector; through multi-mode synchronous sampling and time sequence joint feature vector generation, ambient light and screen content changes are accurately captured, target adjustment values of backlight power and gray scale voltage are dynamically predicted, and response lag and overshoot are avoided. Through spectral domain balancing and a self-adaptive damping scheduling mechanism, cooperative control of backlight and gray scale is optimized, high-frequency oscillation is suppressed, and linear response of a low-frequency band is ensured. Meanwhile, Gamma mapping and global color gamut calibration are dynamically refreshed, accurate adjustment of area-level brightness and gray scale is achieved, and continuity of the display effect and visual experience of a user are improved.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention discloses a remote sensing image multi-scale segmentation method based on frequency spectrum information processing and Mama spatial modeling, and the method comprises the steps: carrying out the preprocessing of a remote sensing image, and obtaining an input image; inputting the input image into the trained remote sensing image segmentation model; the remote sensing image segmentation model comprises an initial convolutional layer, a spectral domain information processing unit branch, a Mama layer branch and a segmentation head; performing feature extraction on the input image through the initial convolutional layer to obtain initial image features; combining a frequency spectrum domain information processing unit branch and a Mama layer branch, and performing feature extraction on the initial image features to obtain fusion features; and performing up-sampling decoding and pixel-level classification on the fusion features through the segmentation head, and outputting a multi-scale segmentation result corresponding to the remote sensing image. According to the method, the spectral domain and the spatial domain are combined for feature extraction, and the common problems of fuzzy details, unclear boundaries, insufficient multi-scale target expression and the like in remote sensing image segmentation are solved.

The invention discloses an optical cable micro-disturbance identification method based on physical simulation and self-supervised time sequence decoupling, and relates to the technical field of optical cable identification, and the method comprises the steps: constructing a physical digital twin simulator, and generating a high-fidelity training set; constructing a deep learning model, wherein the deep learning model adopts a lightweight time sequence decoupling network; training the model by adopting a staged training strategy, and sequentially carrying out self-supervised noise distribution pre-training, simulation supervised training and spectral domain physical consistency fine tuning operation; inputting DAS time sequence data collected in real time into the trained model, and outputting the data as an optical cable identity ID and a physical position; the lightweight time sequence decoupling network comprises a physical guide preprocessing module, a lightweight U-Net separation module, a sparse gating module and an intelligent parallel decoding module. Through the technical means of simulation-driven data generation, staged training strategies and the like, the defects of the prior art in the aspects of reducing the data cost, improving the detection capability in a low SNR environment, realizing multi-source blind source separation and the like are overcome.

The invention discloses a multi-channel dynamic hypergraph sentiment analysis method and a multi-channel dynamic hypergraph sentiment analysis network fusing time sequence consistency, belongs to the field of artificial intelligence and multi-modal sentiment calculation, and aims to solve the problems existing in the existing sentiment analysis technology. The method comprises the following steps: S1, a multi-channel feature extraction step: extracting multi-channel features of a text mode and an audio mode through a heterogeneous pre-training model; s2, a local time sequence context fusion step based on a video number: fusing short-term emotional fluctuation based on a local context mechanism of the video number, and capturing long-range dependence across time dimensions through Transform; s3, a single-modal-multi-modal hypergraph collaborative prediction step: dynamically constructing a single-modal hypergraph and a multi-modal hypergraph in a training batch, and modeling a high-order relationship by adopting spectral domain-spatial domain hybrid convolution; and S4, a multi-level multi-branch supervision step: outputting a final emotion prediction result through joint optimization of an early MLP branch and a late hypergraph branch.

A neural operator-based architecture for performing synthetic frame generation is introduced. The architecture leverages the principles of physics to learn the features in the frames, independent of input resolution, through token mixing and global convolution in the Fourier spectral domain by using Fast Fourier Transform (FFT). The architecture overcomes one of the common limitations exhibited by models that use convolutional layers, a variance to scale, and makes the model resolution independent. This approach is particularly relevant in cases where hardware and resource limitations prevent the capture of high frame rate videos.

The invention relates to the technical field of optical processing, and discloses an imaging depth adjustable laser optical processing system, which comprises a laser emission unit, an optical conduction unit, a spectral domain optical coherence tomography imaging unit, a laser galvanometer scanning unit, an indication light imaging unit, an image processing display unit and a control unit, according to the spectral domain optical coherence tomography imaging system, time scanning of high output wavelength is realized by utilizing a wavelength scanning mode that the scanning telescopic rotating module drives the spectral domain optical coherence tomography imaging system, and position information or image information of a sample from the surface to the interior is provided in real time in combination with an image processing technology; the three-dimensional galvanometer scanning unit scans the position of a sample in real time and records the position and orientation information of a scanning point at the same time, real-time image information and different depths, widths and axial positions of the sample are scanned at the same time, the focusing depth and wavelength of an imaging laser beam and the focusing position and energy of the scanning laser beam are fed back and adjusted, and the laser beam is positioned to the sample for machining.

The invention relates to the technical field of origin identification, in particular to a hyperspectral imaging-based Chinese wolfberry origin identification method, which comprises the following steps of: gradually exciting the surface of a sample by light sources with different wavelengths and different energies to generate a dynamic hyperspectral data cube containing time sequence characteristics; based on the dynamic hyperspectral data cube, key spectral features of the wolfberry samples are extracted in a spectral domain, a spatial domain and a time domain by adopting a feature extraction method based on a space-time multi-dimensional convolutional network; a regional feature correlation mapping method based on geographical correlation is adopted, a regional distribution mode feature library is established through correlation analysis of a geographical environment of a production place and key spectral features of a sample, a classification model is constructed, and the production place is identified by inputting real-time key spectral features of Chinese wolfberry fruits. According to the method, fusion of multi-dimensional information such as spectrum, space and time is combined, the expressive force of sample features is greatly enriched, and the limitation that a traditional method only depends on a single wave band or a single feature is avoided.

The invention discloses a multi-domain feature level fusion expression recognition method based on a graph neural network, and relates to the technical field of computer vision, and the method comprises the steps: firstly, carrying out the data enhancement of an original image, and carrying out the region division of a face according to three courts and five eyes based on key point detection; secondly, constructing a graph structure of the face, endowing each edge with a corresponding weight according to an area where the feature points are located, obtaining weighted distance information, and storing the weighted distance information in an adjacent matrix; then spectral domain features and spatial domain features are obtained through a graph neural network, and local features and context features are obtained through a multistage convolutional neural network and ViT; and then, multi-domain feature fusion is carried out through a feature fusion network based on structural feature guidance, and then an expression recognition result is obtained. Therefore, by adopting the multi-domain feature level fusion expression recognition method based on the graph neural network, the model can understand the change of the expression more deeply, the recognition capability is enhanced, and the robustness and the stability of the model are improved.

An audio encoder has a first information sink oriented encoding branch such as a spectral domain encoding branch, a second information source or SNR oriented encoding branch such as an LPC-domain encoding branch, and a switch for switching between the first and second encoding branches, the second encoding branch having a converter into a specific domain different from the spectral domain such as an LPC analysis stage generating an excitation signal, and the second encoding branch having a specific domain coding branch such as LPC domain processing branch, and a specific spectral domain coding branch such as LPC spectral domain processing branch, and an additional switch for switching between the specific domain coding branch and the specific spectral domain coding branch. An audio decoder has a first domain decoder, a second domain decoder, and a third domain decoder as well as two cascaded switches for switching between the decoders.

A system for controlling an operation of an induction motor is provided. The induction motor includes circuitry and a memory having instructions stored thereon that, when executed by the circuitry, causes the system to collect time-domain measurements of a stator current of the induction motor operating under varying conditions. The system transforms the time-domain measurements into a spectral domain using a sequence of STFTs based on sliding time windows over the time-domain measurements. The system performs spectral analysis in the spectral domain of the stator current to determine harmonics of different types present in the stator current of the induction motor and stabilize the determined harmonics to a shape of corresponding harmonics of the induction motor when operating under steady-state conditions. The system performs one or a combination of control, fault detection, and / or monitoring of the induction motor based on the stabilized harmonics of the induction motor.

The invention provides a wind, light and water storage double-layer robust optimization scheduling strategy construction method and system, and relates to the technical field of energy management, and the method comprises the steps: firstly obtaining multi-source data of a wind power plant, a photovoltaic power station and a cascade hydropower station; performing feature extraction and integrated prediction based on the multi-source data to generate a wind-solar power prediction result; combining hydrodynamic coupling modeling analysis to obtain a hydropower station power generation capacity and adjustable dynamic water volume sequence; the method comprises the following steps: constructing a multi-layer energy coupling graph and performing spectral domain structure optimization to form a power coupling network representing system space-time correlation and ecological sensitivity; and finally, adopting double-layer distributed robust optimization, synchronously optimizing the economic target and the ecological risk constraint, and generating a collaborative scheduling strategy. According to the method, the time sequence matching problem between the wind and light volatility and the hydroelectric time lag is effectively solved, the renewable energy consumption capability and the water resource utilization efficiency are improved, and the ecological operation risk is reduced.

The invention provides an intelligent beauty feature optimization processing method and system for high-definition face changing, and relates to the technical field of image processing, and the method comprises the steps: extracting the features of a source face and a target face through a nonlinear feature decoupler, modulating a feature mapping group through a diffusion probability field, and fusing expression and identity features; extracting contour and detail features by using spectral domain conversion and sub-band decomposition, and performing structure mapping and texture enhancement; and extracting core facial beautification features through high-order tensor decomposition, performing adaptive facial beautification through a quantum entangled state coding network, and outputting a high-definition face-changing facial beautification image by using a fractional order partial differential equation.

The invention provides a DCIM-based method for rapidly calculating a green function of a uniaxial anisotropic layered medium and an electromagnetic simulation system. The method comprises the following steps: modeling a uniaxial anisotropic layered medium to be simulated; determining a uniaxial anisotropic dielectric material of each layer, and assigning transverse parameters and longitudinal parameters; determining longitudinal coordinates of field point and source point distribution required to be solved, fitting a uniaxial anisotropic medium Green function spectral domain expression under the field source distribution by using a DCIM method, and solving an analytical expression in a spatial domain by using Somerfeld identical equation properties; solving the size of the electromagnetic field on the plane according to the fitted analytical expression; compared with a direct numerical integration method or a finite element method numerical solution using the Green function, the method has the advantages that the airspace analytical expression corresponding to the field source distributed on the plane is directly fitted, so that the solution of the airspace Green function is greatly accelerated, and the calculation efficiency and the calculation precision are both good in an actual example.

The invention belongs to the field of optical coherence layer imaging, and particularly discloses a spectrum calibration method for a linear field spectral domain OCT (optical coherence tomography) system, which comprises the following steps: acquiring interference signal instantaneous phases corresponding to two optical path differences by adjusting a lifting platform at a sample mirror in the linear field spectral domain OCT system, and subtracting the interference signal instantaneous phases to obtain an instantaneous phase difference; based on the calibration light source, discrete spectral lines are generated on a detector through a sample arm in the shielding line view field spectral domain OCT system, and spectral intensity distribution corresponding to the calibration light source is obtained; calculating the sub-pixel position of the peak value of each spectral line and the instantaneous phase difference corresponding to the sub-pixel position based on the instantaneous phase difference and the spectral intensity distribution corresponding to the calibration light source; and based on the wave number and the instantaneous phase difference corresponding to each spectral line, determining a linear relationship between the instantaneous phase difference and the wave number through linear fitting, and calibrating the corresponding wave number based on the linear relationship. According to the method and the device, the linear field OCT spectrum calibration result with higher precision can be realized.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The invention discloses a robot-assisted real-time three-dimensional Doppler OCT (optical coherence tomography) angiography method and system, relates to the technical field of medical imaging, and can be used for carrying out real-time and large-view-field three-dimensional Doppler optical coherence imaging evaluation during a microvascular anastomosis operation and improving the anastomosis operation evaluation accuracy and efficiency. According to the system, a spectral domain optical coherence tomography system SD-OCT is used for performing spectral domain optical coherence tomography on a sample to obtain a sample cross-sectional image, and the sample cross-sectional image is sent to a display to be displayed. The OCT imaging probe and the binocular depth camera are integrally installed at the tail end of the robot arm, and the robot arm operates the OCT probe and the binocular depth camera to move to different positions and angles to image a sample. The OCT imaging probe collects optical signals reflected by a sample, and the signal processing host and the displayer receive spectral signals transmitted by the OCT imaging probe and display the three-dimensional structural characteristics of the microvessel and the blood flow velocity condition in the blood vessel.

The invention discloses a gravitational field inversion method considering unsteady noise of a satellite gravity observation value. The gravitational field inversion method comprises the following steps: A, acquiring original observation data of a gravity satellite; b, setting observation value noise as white noise, and constructing a random model considering non-stationary noise; c, performing parameter estimation to obtain a post-test residual error of an observation value; d, estimating the noise auto-covariance and precision factor matrix of the observation value based on the post-test residual error of the observation value; e, repeating the steps C and D for iteration to realize refinement of the gravitational field parameter and observation value random model; and F, evaluating the time-varying gravity field model in the spectral domain. According to the invention, the defects in the prior art can be improved, and the precision and stability of gravitational field inversion are improved.

The invention discloses a vector flow velocity measuring device and method based on a bidirectional scanning spectral domain optical coherence tomography technology. Comprising a super light-emitting diode light source, an optical fiber coupler, a polarization controller, an interferometer comprising a sample arm and a reference arm, and a collimating lens, the collimating lens, the grating, the focusing lens group and the linear array camera are sequentially connected through a light path; the linear array camera, the computer and the two-dimensional galvanometer are sequentially connected through a line; according to the method, the fixed bias vector velocity is introduced by utilizing the bidirectional scanning technology, the dynamic light scattering optical coherence tomography method and the Doppler optical coherence tomography method are combined, decoupling of each parameter of the vector flow velocity is efficiently and accurately realized, and a quantitative index is provided for vector flow velocity analysis. The vector flow velocity measuring device based on the bidirectional scanning spectral domain optical coherence tomography technology is simple in system setting and low in cost, the time resolution is improved by about 2.5 times, and the measuring time is shorter.

The present invention discloses a multimodal image fusion method for joint learning of multi-scene features, including: constructing a cross-modal knowledge reinforcement network, an optimization network based on spectral attention and spatial attention, an edge-guided learning network, and a multimodal image fusion model: it completely separates single-mode tasks and multi-modal tasks, and proposes a multi-scene feature joint learning structure to unify infrared and visible light image fusion and medical image fusion tasks; in order to break down the fusion barriers between different modalities, the fusion process is decomposed into three tasks, and a multi-task learning strategy is applied to guide the model to learn different feature representations more accurately; wherein the cross-modal knowledge reinforcement network enhances the communication between features, thereby achieving effective feature aggregation. In addition, the spatial-spectral domain optimization network and the edge-guided learning network enable the multimodal image fusion model to simultaneously maintain the foreground target and background details of the source image.